1
0 Page
0 Documentation for MINPACK subroutine HYBRD1
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of HYBRD1 is to find a zero of a system of N non-
linear functions in N variables by a modification of the Powell
hybrid method. This is done by using the more general nonlinea
equation solver HYBRD. The user must provide a subroutine whic
calculates the functions. The Jacobian is then calculated by a
forward-difference approximation.
0
2. Subroutine and type statements.
0 SUBROUTINE HYBRD1(FCN,N,X,FVEC,TOL,INFO,WA,LWA)
INTEGER N,INFO,LWA
REAL TOL
REAL X(N),FVEC(N),WA(LWA)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to HYBRD1 and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from HYBRD1.
0 FCN is the name of the user-supplied subroutine which calculate
the functions. FCN must be declared in an EXTERNAL statement
in the user calling program, and should be written as follows
0 SUBROUTINE FCN(N,X,FVEC,IFLAG)
INTEGER N,IFLAG
REAL X(N),FVEC(N)
----------
CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
----------
RETURN
END
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of HYBRD1. In this case se
IFLAG to a negative integer.
1
0 Page
0 N is a positive integer input variable set to the number of
functions and variables.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length N which contains the function
evaluated at the output X.
0 TOL is a nonnegative input variable. Termination occurs when
the algorithm estimates that the relative error between X and
the solution is at most TOL. Section 4 contains more details
about TOL.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Algorithm estimates that the relative error between
X and the solution is at most TOL.
0 INFO = 2 Number of calls to FCN has reached or exceeded
200*(N+1).
0 INFO = 3 TOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 4 Iteration is not making good progress.
0 Sections 4 and 5 contain more details about INFO.
0 WA is a work array of length LWA.
0 LWA is a positive integer input variable not less than
(N*(3*N+13))/2.
0
4. Successful completion.
0 The accuracy of HYBRD1 is controlled by the convergence parame-
ter TOL. This parameter is used in a test which makes a compar
ison between the approximation X and a solution XSOL. HYBRD1
terminates when the test is satisfied. If TOL is less than the
machine precision (as defined by the MINPACK function
SPMPAR(1)), then HYBRD1 only attempts to satisfy the test
defined by the machine precision. Further progress is not usu-
ally possible. Unless high precision solutions are required,
the recommended value for TOL is the square root of the machine
precision.
0 The test assumes that the functions are reasonably well behaved
1
0 Page
0 If this condition is not satisfied, then HYBRD1 may incorrectly
indicate convergence. The validity of the answer can be
checked, for example, by rerunning HYBRD1 with a tighter toler-
ance.
0 Convergence test. If ENORM(Z) denotes the Euclidean norm of a
vector Z, then this test attempts to guarantee that
0 ENORM(X-XSOL) .LE. TOL*ENORM(XSOL).
0 If this condition is satisfied with TOL = 10**(-K), then the
larger components of X have K significant decimal digits and
INFO is set to 1. There is a danger that the smaller compo-
nents of X may have large relative errors, but the fast rate
of convergence of HYBRD1 usually avoids this possibility.
0
5. Unsuccessful completion.
0 Unsuccessful termination of HYBRD1 can be due to improper input
parameters, arithmetic interrupts, an excessive number of func-
tion evaluations, errors in the functions, or lack of good prog
ress.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
TOL .LT. 0.E0, or LWA .LT. (N*(3*N+13))/2.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by HYBRD1. In this
case, it may be possible to remedy the situation by not evalu
ating the functions here, but instead setting the components
of FVEC to numbers that exceed those in the initial FVEC,
thereby indirectly reducing the step length. The step length
can be more directly controlled by using instead HYBRD, which
includes in its calling sequence the step-length- governing
parameter FACTOR.
0 Excessive number of function evaluations. If the number of
calls to FCN reaches 200*(N+1), then this indicates that the
routine is converging very slowly as measured by the progress
of FVEC, and INFO is set to 2. This situation should be unu-
sual because, as indicated below, lack of good progress is
usually diagnosed earlier by HYBRD1, causing termination with
INFO = 4.
0 Errors in the functions. The choice of step length in the for-
ward-difference approximation to the Jacobian assumes that th
relative errors in the functions are of the order of the
machine precision. If this is not the case, HYBRD1 may fail
(usually with INFO = 4). The user should then use HYBRD
instead, or one of the programs which require the analytic
Jacobian (HYBRJ1 and HYBRJ).
1
0 Page
0 Lack of good progress. HYBRD1 searches for a zero of the syste
by minimizing the sum of the squares of the functions. In so
doing, it can become trapped in a region where the minimum
does not correspond to a zero of the system and, in this situ
ation, the iteration eventually fails to make good progress.
In particular, this will happen if the system does not have a
zero. If the system has a zero, rerunning HYBRD1 from a dif-
ferent starting point may be helpful.
0
6. Characteristics of the algorithm.
0 HYBRD1 is a modification of the Powell hybrid method. Two of
its main characteristics involve the choice of the correction a
a convex combination of the Newton and scaled gradient direc-
tions, and the updating of the Jacobian by the rank-1 method of
Broyden. The choice of the correction guarantees (under reason
able conditions) global convergence for starting points far fro
the solution and a fast rate of convergence. The Jacobian is
approximated by forward differences at the starting point, but
forward differences are not used again until the rank-1 method
fails to produce satisfactory progress.
0 Timing. The time required by HYBRD1 to solve a given problem
depends on N, the behavior of the functions, the accuracy
requested, and the starting point. The number of arithmetic
operations needed by HYBRD1 is about 11.5*(N**2) to process
each call to FCN. Unless FCN can be evaluated quickly, the
timing of HYBRD1 will be strongly influenced by the time spen
in FCN.
0 Storage. HYBRD1 requires (3*N**2 + 17*N)/2 single precision
storage locations, in addition to the storage required by the
program. There are no internally declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... DOGLEG,SPMPAR,ENORM,FDJAC1,HYBRD,
QFORM,QRFAC,R1MPYQ,R1UPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MIN0,MOD
0
8. References.
0 M. J. D. Powell, A Hybrid Method for Nonlinear Equations.
Numerical Methods for Nonlinear Algebraic Equations,
P. Rabinowitz, editor. Gordon and Breach, 1970.
0
9. Example.
1
0 Page
0 The problem is to determine the values of x(1), x(2), ..., x(9)
which solve the system of tridiagonal equations
0 (3-2*x(1))*x(1) -2*x(2) = -1
-x(i-1) + (3-2*x(i))*x(i) -2*x(i+1) = -1, i=2-8
-x(8) + (3-2*x(9))*x(9) = -1
0 C **********
C
C DRIVER FOR HYBRD1 EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,N,INFO,LWA,NWRITE
REAL TOL,FNORM
REAL X(9),FVEC(9),WA(180)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
N = 9
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH SOLUTION.
C
DO 10 J = 1, 9
X(J) = -1.E0
10 CONTINUE
C
LWA = 180
C
C SET TOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
TOL = SQRT(SPMPAR(1))
C
CALL HYBRD1(FCN,N,X,FVEC,TOL,INFO,WA,LWA)
FNORM = ENORM(N,FVEC)
WRITE (NWRITE,1000) FNORM,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // (5X,3E15.7))
C
C LAST CARD OF DRIVER FOR HYBRD1 EXAMPLE.
C
END
SUBROUTINE FCN(N,X,FVEC,IFLAG)
INTEGER N,IFLAG
REAL X(N),FVEC(N)
C
1
0 Page
0 C SUBROUTINE FCN FOR HYBRD1 EXAMPLE.
C
INTEGER K
REAL ONE,TEMP,TEMP1,TEMP2,THREE,TWO,ZERO
DATA ZERO,ONE,TWO,THREE /0.E0,1.E0,2.E0,3.E0/
C
DO 10 K = 1, N
TEMP = (THREE - TWO*X(K))*X(K)
TEMP1 = ZERO
IF (K .NE. 1) TEMP1 = X(K-1)
TEMP2 = ZERO
IF (K .NE. N) TEMP2 = X(K+1)
FVEC(K) = TEMP - TEMP1 - TWO*TEMP2 + ONE
10 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.1192636E-07
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 -0.5706545E+00 -0.6816283E+00 -0.7017325E+00
-0.7042129E+00 -0.7013690E+00 -0.6918656E+00
-0.6657920E+00 -0.5960342E+00 -0.4164121E+00
1
0
1
0 Page
0 Documentation for MINPACK subroutine HYBRD
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of HYBRD is to find a zero of a system of N non-
linear functions in N variables by a modification of the Powell
hybrid method. The user must provide a subroutine which calcu-
lates the functions. The Jacobian is then calculated by a for-
ward-difference approximation.
0
2. Subroutine and type statements.
0 SUBROUTINE HYBRD(FCN,N,X,FVEC,XTOL,MAXFEV,ML,MU,EPSFCN,DIAG,
* MODE,FACTOR,NPRINT,INFO,NFEV,FJAC,LDFJAC,
* R,LR,QTF,WA1,WA2,WA3,WA4)
INTEGER N,MAXFEV,ML,MU,MODE,NPRINT,INFO,NFEV,LDFJAC,LR
REAL XTOL,EPSFCN,FACTOR
REAL X(N),FVEC(N),DIAG(N),FJAC(LDFJAC,N),R(LR),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(N)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to HYBRD and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from HYBRD.
0 FCN is the name of the user-supplied subroutine which calculate
the functions. FCN must be declared in an EXTERNAL statement
in the user calling program, and should be written as follows
0 SUBROUTINE FCN(N,X,FVEC,IFLAG)
INTEGER N,IFLAG
REAL X(N),FVEC(N)
----------
CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
----------
RETURN
END
0 The value of IFLAG should not be changed by FCN unless the
1
0 Page
0 user wants to terminate execution of HYBRD. In this case set
IFLAG to a negative integer.
0 N is a positive integer input variable set to the number of
functions and variables.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length N which contains the function
evaluated at the output X.
0 XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at mos
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
0 MAXFEV is a positive integer input variable. Termination occur
when the number of calls to FCN is at least MAXFEV by the end
of an iteration.
0 ML is a nonnegative integer input variable which specifies the
number of subdiagonals within the band of the Jacobian matrix
If the Jacobian is not banded, set ML to at least N - 1.
0 MU is a nonnegative integer input variable which specifies the
number of superdiagonals within the band of the Jacobian
matrix. If the Jacobian is not banded, set MU to at least
N - 1.
0 EPSFCN is an input variable used in determining a suitable step
for the forward-difference approximation. This approximation
assumes that the relative errors in the functions are of the
order of EPSFCN. If EPSFCN is less than the machine preci-
sion, it is assumed that the relative errors in the functions
are of the order of the machine precision.
0 DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
0 MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is speci
fied by the input DIAG. Other values of MODE are equivalent
to MODE = 1.
0 FACTOR is a positive input variable used in determining the ini
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTO
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
1
0 Page
0 NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X and FVEC available for printing. If NPRINT
is not positive, no special calls of FCN with IFLAG = 0 are
made.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Relative error between two consecutive iterates is
at most XTOL.
0 INFO = 2 Number of calls to FCN has reached or exceeded
MAXFEV.
0 INFO = 3 XTOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 4 Iteration is not making good progress, as measured
by the improvement from the last five Jacobian eval
uations.
0 INFO = 5 Iteration is not making good progress, as measured
by the improvement from the last ten iterations.
0 Sections 4 and 5 contain more details about INFO.
0 NFEV is an integer output variable set to the number of calls t
FCN.
0 FJAC is an output N by N array which contains the orthogonal
matrix Q produced by the QR factorization of the final approx
imate Jacobian.
0 LDFJAC is a positive integer input variable not less than N
which specifies the leading dimension of the array FJAC.
0 R is an output array of length LR which contains the upper
triangular matrix produced by the QR factorization of the
final approximate Jacobian, stored rowwise.
0 LR is a positive integer input variable not less than
(N*(N+1))/2.
0 QTF is an output array of length N which contains the vector
(Q transpose)*FVEC.
0 WA1, WA2, WA3, and WA4 are work arrays of length N.
1
0 Page
0
4. Successful completion.
0 The accuracy of HYBRD is controlled by the convergence paramete
XTOL. This parameter is used in a test which makes a compariso
between the approximation X and a solution XSOL. HYBRD termi-
nates when the test is satisfied. If the convergence parameter
is less than the machine precision (as defined by the MINPACK
function SPMPAR(1)), then HYBRD only attempts to satisfy the
test defined by the machine precision. Further progress is not
usually possible.
0 The test assumes that the functions are reasonably well behaved
If this condition is not satisfied, then HYBRD may incorrectly
indicate convergence. The validity of the answer can be
checked, for example, by rerunning HYBRD with a tighter toler-
ance.
0 Convergence test. If ENORM(Z) denotes the Euclidean norm of a
vector Z and D is the diagonal matrix whose entries are
defined by the array DIAG, then this test attempts to guaran-
tee that
0 ENORM(D*(X-XSOL)) .LE. XTOL*ENORM(D*XSOL).
0 If this condition is satisfied with XTOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 1. There is a danger that the smaller compo-
nents of D*X may have large relative errors, but the fast rat
of convergence of HYBRD usually avoids this possibility.
Unless high precision solutions are required, the recommended
value for XTOL is the square root of the machine precision.
0
5. Unsuccessful completion.
0 Unsuccessful termination of HYBRD can be due to improper input
parameters, arithmetic interrupts, an excessive number of func-
tion evaluations, or lack of good progress.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
XTOL .LT. 0.E0, or MAXFEV .LE. 0, or ML .LT. 0, or MU .LT. 0,
or FACTOR .LE. 0.E0, or LDFJAC .LT. N, or LR .LT. (N*(N+1))/2
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by HYBRD. In this
case, it may be possible to remedy the situation by rerunning
HYBRD with a smaller value of FACTOR.
0 Excessive number of function evaluations. A reasonable value
for MAXFEV is 200*(N+1). If the number of calls to FCN
reaches MAXFEV, then this indicates that the routine is con-
verging very slowly as measured by the progress of FVEC, and
1
0 Page
0 INFO is set to 2. This situation should be unusual because,
as indicated below, lack of good progress is usually diagnose
earlier by HYBRD, causing termination with INFO = 4 or
INFO = 5.
0 Lack of good progress. HYBRD searches for a zero of the system
by minimizing the sum of the squares of the functions. In so
doing, it can become trapped in a region where the minimum
does not correspond to a zero of the system and, in this situ
ation, the iteration eventually fails to make good progress.
In particular, this will happen if the system does not have a
zero. If the system has a zero, rerunning HYBRD from a dif-
ferent starting point may be helpful.
0
6. Characteristics of the algorithm.
0 HYBRD is a modification of the Powell hybrid method. Two of it
main characteristics involve the choice of the correction as a
convex combination of the Newton and scaled gradient directions
and the updating of the Jacobian by the rank-1 method of Broy-
den. The choice of the correction guarantees (under reasonable
conditions) global convergence for starting points far from the
solution and a fast rate of convergence. The Jacobian is
approximated by forward differences at the starting point, but
forward differences are not used again until the rank-1 method
fails to produce satisfactory progress.
0 Timing. The time required by HYBRD to solve a given problem
depends on N, the behavior of the functions, the accuracy
requested, and the starting point. The number of arithmetic
operations needed by HYBRD is about 11.5*(N**2) to process
each call to FCN. Unless FCN can be evaluated quickly, the
timing of HYBRD will be strongly influenced by the time spent
in FCN.
0 Storage. HYBRD requires (3*N**2 + 17*N)/2 single precision
storage locations, in addition to the storage required by the
program. There are no internally declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... DOGLEG,SPMPAR,ENORM,FDJAC1,
QFORM,QRFAC,R1MPYQ,R1UPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MIN0,MOD
0
8. References.
0 M. J. D. Powell, A Hybrid Method for Nonlinear Equations.
1
0 Page
0 Numerical Methods for Nonlinear Algebraic Equations,
P. Rabinowitz, editor. Gordon and Breach, 1970.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), ..., x(9)
which solve the system of tridiagonal equations
0 (3-2*x(1))*x(1) -2*x(2) = -1
-x(i-1) + (3-2*x(i))*x(i) -2*x(i+1) = -1, i=2-8
-x(8) + (3-2*x(9))*x(9) = -1
0 C **********
C
C DRIVER FOR HYBRD EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,N,MAXFEV,ML,MU,MODE,NPRINT,INFO,NFEV,LDFJAC,LR,NWRITE
REAL XTOL,EPSFCN,FACTOR,FNORM
REAL X(9),FVEC(9),DIAG(9),FJAC(9,9),R(45),QTF(9),
* WA1(9),WA2(9),WA3(9),WA4(9)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
N = 9
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH SOLUTION.
C
DO 10 J = 1, 9
X(J) = -1.E0
10 CONTINUE
C
LDFJAC = 9
LR = 45
C
C SET XTOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
XTOL = SQRT(SPMPAR(1))
C
MAXFEV = 2000
ML = 1
MU = 1
EPSFCN = 0.E0
MODE = 2
DO 20 J = 1, 9
DIAG(J) = 1.E0
1
0 Page
0 20 CONTINUE
FACTOR = 1.E2
NPRINT = 0
C
CALL HYBRD(FCN,N,X,FVEC,XTOL,MAXFEV,ML,MU,EPSFCN,DIAG,
* MODE,FACTOR,NPRINT,INFO,NFEV,FJAC,LDFJAC,
* R,LR,QTF,WA1,WA2,WA3,WA4)
FNORM = ENORM(N,FVEC)
WRITE (NWRITE,1000) FNORM,NFEV,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,31H NUMBER OF FUNCTION EVALUATIONS,I10 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // (5X,3E15.7))
C
C LAST CARD OF DRIVER FOR HYBRD EXAMPLE.
C
END
SUBROUTINE FCN(N,X,FVEC,IFLAG)
INTEGER N,IFLAG
REAL X(N),FVEC(N)
C
C SUBROUTINE FCN FOR HYBRD EXAMPLE.
C
INTEGER K
REAL ONE,TEMP,TEMP1,TEMP2,THREE,TWO,ZERO
DATA ZERO,ONE,TWO,THREE /0.E0,1.E0,2.E0,3.E0/
C
IF (IFLAG .NE. 0) GO TO 5
C
C INSERT PRINT STATEMENTS HERE WHEN NPRINT IS POSITIVE.
C
RETURN
5 CONTINUE
DO 10 K = 1, N
TEMP = (THREE - TWO*X(K))*X(K)
TEMP1 = ZERO
IF (K .NE. 1) TEMP1 = X(K-1)
TEMP2 = ZERO
IF (K .NE. N) TEMP2 = X(K+1)
FVEC(K) = TEMP - TEMP1 - TWO*TEMP2 + ONE
10 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.1192636E-07
0 NUMBER OF FUNCTION EVALUATIONS 14
1
0 Page
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 -0.5706545E+00 -0.6816283E+00 -0.7017325E+00
-0.7042129E+00 -0.7013690E+00 -0.6918656E+00
-0.6657920E+00 -0.5960342E+00 -0.4164121E+00
1
0
1
0 Page
0 Documentation for MINPACK subroutine HYBRJ1
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of HYBRJ1 is to find a zero of a system of N non-
linear functions in N variables by a modification of the Powell
hybrid method. This is done by using the more general nonlinea
equation solver HYBRJ. The user must provide a subroutine whic
calculates the functions and the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE HYBRJ1(FCN,N,X,FVEC,FJAC,LDFJAC,TOL,INFO,WA,LWA)
INTEGER N,LDFJAC,INFO,LWA
REAL TOL
REAL X(N),FVEC(N),FJAC(LDFJAC,N),WA(LWA)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to HYBRJ1 and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from HYBRJ1.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the Jacobian. FCN must be declared in an
EXTERNAL statement in the user calling program, and should be
written as follows.
0 SUBROUTINE FCN(N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER N,LDFJAC,IFLAG
REAL X(N),FVEC(N),FJAC(LDFJAC,N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC. DO NOT ALTER FJAC.
IF IFLAG = 2 CALCULATE THE JACOBIAN AT X AND
RETURN THIS MATRIX IN FJAC. DO NOT ALTER FVEC.
----------
RETURN
END
0 The value of IFLAG should not be changed by FCN unless the
1
0 Page
0 user wants to terminate execution of HYBRJ1. In this case se
IFLAG to a negative integer.
0 N is a positive integer input variable set to the number of
functions and variables.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length N which contains the function
evaluated at the output X.
0 FJAC is an output N by N array which contains the orthogonal
matrix Q produced by the QR factorization of the final approx
imate Jacobian. Section 6 contains more details about the
approximation to the Jacobian.
0 LDFJAC is a positive integer input variable not less than N
which specifies the leading dimension of the array FJAC.
0 TOL is a nonnegative input variable. Termination occurs when
the algorithm estimates that the relative error between X and
the solution is at most TOL. Section 4 contains more details
about TOL.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Algorithm estimates that the relative error between
X and the solution is at most TOL.
0 INFO = 2 Number of calls to FCN with IFLAG = 1 has reached
100*(N+1).
0 INFO = 3 TOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 4 Iteration is not making good progress.
0 Sections 4 and 5 contain more details about INFO.
0 WA is a work array of length LWA.
0 LWA is a positive integer input variable not less than
(N*(N+13))/2.
0
4. Successful completion.
0 The accuracy of HYBRJ1 is controlled by the convergence
1
0 Page
0 parameter TOL. This parameter is used in a test which makes a
comparison between the approximation X and a solution XSOL.
HYBRJ1 terminates when the test is satisfied. If TOL is less
than the machine precision (as defined by the MINPACK function
SPMPAR(1)), then HYBRJ1 only attempts to satisfy the test
defined by the machine precision. Further progress is not usu-
ally possible. Unless high precision solutions are required,
the recommended value for TOL is the square root of the machine
precision.
0 The test assumes that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then HYBRJ1 ma
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning HYBRJ1 with a tighter toler-
ance.
0 Convergence test. If ENORM(Z) denotes the Euclidean norm of a
vector Z, then this test attempts to guarantee that
0 ENORM(X-XSOL) .LE. TOL*ENORM(XSOL).
0 If this condition is satisfied with TOL = 10**(-K), then the
larger components of X have K significant decimal digits and
INFO is set to 1. There is a danger that the smaller compo-
nents of X may have large relative errors, but the fast rate
of convergence of HYBRJ1 usually avoids this possibility.
0
5. Unsuccessful completion.
0 Unsuccessful termination of HYBRJ1 can be due to improper input
parameters, arithmetic interrupts, an excessive number of func-
tion evaluations, or lack of good progress.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
LDFJAC .LT. N, or TOL .LT. 0.E0, or LWA .LT. (N*(N+13))/2.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by HYBRJ1. In this
case, it may be possible to remedy the situation by not evalu
ating the functions here, but instead setting the components
of FVEC to numbers that exceed those in the initial FVEC,
thereby indirectly reducing the step length. The step length
can be more directly controlled by using instead HYBRJ, which
includes in its calling sequence the step-length- governing
parameter FACTOR.
0 Excessive number of function evaluations. If the number of
calls to FCN with IFLAG = 1 reaches 100*(N+1), then this indi
cates that the routine is converging very slowly as measured
1
0 Page
0 by the progress of FVEC, and INFO is set to 2. This situatio
should be unusual because, as indicated below, lack of good
progress is usually diagnosed earlier by HYBRJ1, causing ter-
mination with INFO = 4.
0 Lack of good progress. HYBRJ1 searches for a zero of the syste
by minimizing the sum of the squares of the functions. In so
doing, it can become trapped in a region where the minimum
does not correspond to a zero of the system and, in this situ
ation, the iteration eventually fails to make good progress.
In particular, this will happen if the system does not have a
zero. If the system has a zero, rerunning HYBRJ1 from a dif-
ferent starting point may be helpful.
0
6. Characteristics of the algorithm.
0 HYBRJ1 is a modification of the Powell hybrid method. Two of
its main characteristics involve the choice of the correction a
a convex combination of the Newton and scaled gradient direc-
tions, and the updating of the Jacobian by the rank-1 method of
Broyden. The choice of the correction guarantees (under reason
able conditions) global convergence for starting points far fro
the solution and a fast rate of convergence. The Jacobian is
calculated at the starting point, but it is not recalculated
until the rank-1 method fails to produce satisfactory progress.
0 Timing. The time required by HYBRJ1 to solve a given problem
depends on N, the behavior of the functions, the accuracy
requested, and the starting point. The number of arithmetic
operations needed by HYBRJ1 is about 11.5*(N**2) to process
each evaluation of the functions (call to FCN with IFLAG = 1)
and 1.3*(N**3) to process each evaluation of the Jacobian
(call to FCN with IFLAG = 2). Unless FCN can be evaluated
quickly, the timing of HYBRJ1 will be strongly influenced by
the time spent in FCN.
0 Storage. HYBRJ1 requires (3*N**2 + 17*N)/2 single precision
storage locations, in addition to the storage required by the
program. There are no internally declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... DOGLEG,SPMPAR,ENORM,HYBRJ,
QFORM,QRFAC,R1MPYQ,R1UPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MIN0,MOD
0
8. References.
1
0 Page
0 M. J. D. Powell, A Hybrid Method for Nonlinear Equations.
Numerical Methods for Nonlinear Algebraic Equations,
P. Rabinowitz, editor. Gordon and Breach, 1970.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), ..., x(9)
which solve the system of tridiagonal equations
0 (3-2*x(1))*x(1) -2*x(2) = -1
-x(i-1) + (3-2*x(i))*x(i) -2*x(i+1) = -1, i=2-8
-x(8) + (3-2*x(9))*x(9) = -1
0 C **********
C
C DRIVER FOR HYBRJ1 EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,N,LDFJAC,INFO,LWA,NWRITE
REAL TOL,FNORM
REAL X(9),FVEC(9),FJAC(9,9),WA(99)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
N = 9
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH SOLUTION.
C
DO 10 J = 1, 9
X(J) = -1.E0
10 CONTINUE
C
LDFJAC = 9
LWA = 99
C
C SET TOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
TOL = SQRT(SPMPAR(1))
C
CALL HYBRJ1(FCN,N,X,FVEC,FJAC,LDFJAC,TOL,INFO,WA,LWA)
FNORM = ENORM(N,FVEC)
WRITE (NWRITE,1000) FNORM,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // (5X,3E15.7))
1
0 Page
0 C
C LAST CARD OF DRIVER FOR HYBRJ1 EXAMPLE.
C
END
SUBROUTINE FCN(N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER N,LDFJAC,IFLAG
REAL X(N),FVEC(N),FJAC(LDFJAC,N)
C
C SUBROUTINE FCN FOR HYBRJ1 EXAMPLE.
C
INTEGER J,K
REAL ONE,TEMP,TEMP1,TEMP2,THREE,TWO,ZERO
DATA ZERO,ONE,TWO,THREE,FOUR /0.E0,1.E0,2.E0,3.E0,4.E0/
C
IF (IFLAG .EQ. 2) GO TO 20
DO 10 K = 1, N
TEMP = (THREE - TWO*X(K))*X(K)
TEMP1 = ZERO
IF (K .NE. 1) TEMP1 = X(K-1)
TEMP2 = ZERO
IF (K .NE. N) TEMP2 = X(K+1)
FVEC(K) = TEMP - TEMP1 - TWO*TEMP2 + ONE
10 CONTINUE
GO TO 50
20 CONTINUE
DO 40 K = 1, N
DO 30 J = 1, N
FJAC(K,J) = ZERO
30 CONTINUE
FJAC(K,K) = THREE - FOUR*X(K)
IF (K .NE. 1) FJAC(K,K-1) = -ONE
IF (K .NE. N) FJAC(K,K+1) = -TWO
40 CONTINUE
50 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.1192636E-07
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 -0.5706545E+00 -0.6816283E+00 -0.7017325E+00
-0.7042129E+00 -0.7013690E+00 -0.6918656E+00
-0.6657920E+00 -0.5960342E+00 -0.4164121E+00
1
0
1
0 Page
0 Documentation for MINPACK subroutine HYBRJ
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of HYBRJ is to find a zero of a system of N non-
linear functions in N variables by a modification of the Powell
hybrid method. The user must provide a subroutine which calcu-
lates the functions and the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE HYBRJ(FCN,N,X,FVEC,FJAC,LDFJAC,XTOL,MAXFEV,DIAG,
* MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,R,LR,QTF,
* WA1,WA2,WA3,WA4)
INTEGER N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV,LR
REAL XTOL,FACTOR
REAL X(N),FVEC(N),FJAC(LDFJAC,N),DIAG(N),R(LR),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(N)
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to HYBRJ and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from HYBRJ.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the Jacobian. FCN must be declared in an
EXTERNAL statement in the user calling program, and should be
written as follows.
0 SUBROUTINE FCN(N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER N,LDFJAC,IFLAG
REAL X(N),FVEC(N),FJAC(LDFJAC,N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC. DO NOT ALTER FJAC.
IF IFLAG = 2 CALCULATE THE JACOBIAN AT X AND
RETURN THIS MATRIX IN FJAC. DO NOT ALTER FVEC.
----------
RETURN
END
1
0 Page
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of HYBRJ. In this case set
IFLAG to a negative integer.
0 N is a positive integer input variable set to the number of
functions and variables.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length N which contains the function
evaluated at the output X.
0 FJAC is an output N by N array which contains the orthogonal
matrix Q produced by the QR factorization of the final approx
imate Jacobian. Section 6 contains more details about the
approximation to the Jacobian.
0 LDFJAC is a positive integer input variable not less than N
which specifies the leading dimension of the array FJAC.
0 XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at mos
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
0 MAXFEV is a positive integer input variable. Termination occur
when the number of calls to FCN with IFLAG = 1 has reached
MAXFEV.
0 DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
0 MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is speci
fied by the input DIAG. Other values of MODE are equivalent
to MODE = 1.
0 FACTOR is a positive input variable used in determining the ini
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTO
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
0 NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X and FVEC available for printing. FVEC and
FJAC should not be altered. If NPRINT is not positive, no
1
0 Page
0 special calls of FCN with IFLAG = 0 are made.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Relative error between two consecutive iterates is
at most XTOL.
0 INFO = 2 Number of calls to FCN with IFLAG = 1 has reached
MAXFEV.
0 INFO = 3 XTOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 4 Iteration is not making good progress, as measured
by the improvement from the last five Jacobian eval
uations.
0 INFO = 5 Iteration is not making good progress, as measured
by the improvement from the last ten iterations.
0 Sections 4 and 5 contain more details about INFO.
0 NFEV is an integer output variable set to the number of calls t
FCN with IFLAG = 1.
0 NJEV is an integer output variable set to the number of calls t
FCN with IFLAG = 2.
0 R is an output array of length LR which contains the upper
triangular matrix produced by the QR factorization of the
final approximate Jacobian, stored rowwise.
0 LR is a positive integer input variable not less than
(N*(N+1))/2.
0 QTF is an output array of length N which contains the vector
(Q transpose)*FVEC.
0 WA1, WA2, WA3, and WA4 are work arrays of length N.
0
4. Successful completion.
0 The accuracy of HYBRJ is controlled by the convergence paramete
XTOL. This parameter is used in a test which makes a compariso
between the approximation X and a solution XSOL. HYBRJ termi-
nates when the test is satisfied. If the convergence parameter
is less than the machine precision (as defined by the MINPACK
function SPMPAR(1)), then HYBRJ only attempts to satisfy the
test defined by the machine precision. Further progress is not
1
0 Page
0 usually possible.
0 The test assumes that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then HYBRJ may
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning HYBRJ with a tighter toler-
ance.
0 Convergence test. If ENORM(Z) denotes the Euclidean norm of a
vector Z and D is the diagonal matrix whose entries are
defined by the array DIAG, then this test attempts to guaran-
tee that
0 ENORM(D*(X-XSOL)) .LE. XTOL*ENORM(D*XSOL).
0 If this condition is satisfied with XTOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 1. There is a danger that the smaller compo-
nents of D*X may have large relative errors, but the fast rat
of convergence of HYBRJ usually avoids this possibility.
Unless high precision solutions are required, the recommended
value for XTOL is the square root of the machine precision.
0
5. Unsuccessful completion.
0 Unsuccessful termination of HYBRJ can be due to improper input
parameters, arithmetic interrupts, an excessive number of func-
tion evaluations, or lack of good progress.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
LDFJAC .LT. N, or XTOL .LT. 0.E0, or MAXFEV .LE. 0, or
FACTOR .LE. 0.E0, or LR .LT. (N*(N+1))/2.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by HYBRJ. In this
case, it may be possible to remedy the situation by rerunning
HYBRJ with a smaller value of FACTOR.
0 Excessive number of function evaluations. A reasonable value
for MAXFEV is 100*(N+1). If the number of calls to FCN with
IFLAG = 1 reaches MAXFEV, then this indicates that the routin
is converging very slowly as measured by the progress of FVEC
and INFO is set to 2. This situation should be unusual
because, as indicated below, lack of good progress is usually
diagnosed earlier by HYBRJ, causing termination with INFO = 4
or INFO = 5.
0 Lack of good progress. HYBRJ searches for a zero of the system
by minimizing the sum of the squares of the functions. In so
1
0 Page
0 doing, it can become trapped in a region where the minimum
does not correspond to a zero of the system and, in this situ
ation, the iteration eventually fails to make good progress.
In particular, this will happen if the system does not have a
zero. If the system has a zero, rerunning HYBRJ from a dif-
ferent starting point may be helpful.
0
6. Characteristics of the algorithm.
0 HYBRJ is a modification of the Powell hybrid method. Two of it
main characteristics involve the choice of the correction as a
convex combination of the Newton and scaled gradient directions
and the updating of the Jacobian by the rank-1 method of Broy-
den. The choice of the correction guarantees (under reasonable
conditions) global convergence for starting points far from the
solution and a fast rate of convergence. The Jacobian is calcu
lated at the starting point, but it is not recalculated until
the rank-1 method fails to produce satisfactory progress.
0 Timing. The time required by HYBRJ to solve a given problem
depends on N, the behavior of the functions, the accuracy
requested, and the starting point. The number of arithmetic
operations needed by HYBRJ is about 11.5*(N**2) to process
each evaluation of the functions (call to FCN with IFLAG = 1)
and 1.3*(N**3) to process each evaluation of the Jacobian
(call to FCN with IFLAG = 2). Unless FCN can be evaluated
quickly, the timing of HYBRJ will be strongly influenced by
the time spent in FCN.
0 Storage. HYBRJ requires (3*N**2 + 17*N)/2 single precision
storage locations, in addition to the storage required by the
program. There are no internally declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... DOGLEG,SPMPAR,ENORM,
QFORM,QRFAC,R1MPYQ,R1UPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MIN0,MOD
0
8. References.
0 M. J. D. Powell, A Hybrid Method for Nonlinear Equations.
Numerical Methods for Nonlinear Algebraic Equations,
P. Rabinowitz, editor. Gordon and Breach, 1970.
0
9. Example.
1
0 Page
0 The problem is to determine the values of x(1), x(2), ..., x(9)
which solve the system of tridiagonal equations
0 (3-2*x(1))*x(1) -2*x(2) = -1
-x(i-1) + (3-2*x(i))*x(i) -2*x(i+1) = -1, i=2-8
-x(8) + (3-2*x(9))*x(9) = -1
0 C **********
C
C DRIVER FOR HYBRJ EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV,LR,NWRITE
REAL XTOL,FACTOR,FNORM
REAL X(9),FVEC(9),FJAC(9,9),DIAG(9),R(45),QTF(9),
* WA1(9),WA2(9),WA3(9),WA4(9)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
N = 9
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH SOLUTION.
C
DO 10 J = 1, 9
X(J) = -1.E0
10 CONTINUE
C
LDFJAC = 9
LR = 45
C
C SET XTOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
XTOL = SQRT(SPMPAR(1))
C
MAXFEV = 1000
MODE = 2
DO 20 J = 1, 9
DIAG(J) = 1.E0
20 CONTINUE
FACTOR = 1.E2
NPRINT = 0
C
CALL HYBRJ(FCN,N,X,FVEC,FJAC,LDFJAC,XTOL,MAXFEV,DIAG,
* MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,R,LR,QTF,
* WA1,WA2,WA3,WA4)
FNORM = ENORM(N,FVEC)
WRITE (NWRITE,1000) FNORM,NFEV,NJEV,INFO,(X(J),J=1,N)
1
0 Page
0 STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,31H NUMBER OF FUNCTION EVALUATIONS,I10 //
* 5X,31H NUMBER OF JACOBIAN EVALUATIONS,I10 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // (5X,3E15.7))
C
C LAST CARD OF DRIVER FOR HYBRJ EXAMPLE.
C
END
SUBROUTINE FCN(N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER N,LDFJAC,IFLAG
REAL X(N),FVEC(N),FJAC(LDFJAC,N)
C
C SUBROUTINE FCN FOR HYBRJ EXAMPLE.
C
INTEGER J,K
REAL ONE,TEMP,TEMP1,TEMP2,THREE,TWO,ZERO
DATA ZERO,ONE,TWO,THREE,FOUR /0.E0,1.E0,2.E0,3.E0,4.E0/
C
IF (IFLAG .NE. 0) GO TO 5
C
C INSERT PRINT STATEMENTS HERE WHEN NPRINT IS POSITIVE.
C
RETURN
5 CONTINUE
IF (IFLAG .EQ. 2) GO TO 20
DO 10 K = 1, N
TEMP = (THREE - TWO*X(K))*X(K)
TEMP1 = ZERO
IF (K .NE. 1) TEMP1 = X(K-1)
TEMP2 = ZERO
IF (K .NE. N) TEMP2 = X(K+1)
FVEC(K) = TEMP - TEMP1 - TWO*TEMP2 + ONE
10 CONTINUE
GO TO 50
20 CONTINUE
DO 40 K = 1, N
DO 30 J = 1, N
FJAC(K,J) = ZERO
30 CONTINUE
FJAC(K,K) = THREE - FOUR*X(K)
IF (K .NE. 1) FJAC(K,K-1) = -ONE
IF (K .NE. N) FJAC(K,K+1) = -TWO
40 CONTINUE
50 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
1
0 Page
0 FINAL L2 NORM OF THE RESIDUALS 0.1192636E-07
0 NUMBER OF FUNCTION EVALUATIONS 11
0 NUMBER OF JACOBIAN EVALUATIONS 1
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 -0.5706545E+00 -0.6816283E+00 -0.7017325E+00
-0.7042129E+00 -0.7013690E+00 -0.6918656E+00
-0.6657920E+00 -0.5960342E+00 -0.4164121E+00
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMDER1
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMDER1 is to minimize the sum of the squares of
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm. This is done by using the more
general least-squares solver LMDER. The user must provide a
subroutine which calculates the functions and the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE LMDER1(FCN,M,N,X,FVEC,FJAC,LDFJAC,TOL,
* INFO,IPVT,WA,LWA)
INTEGER M,N,LDFJAC,INFO,LWA
INTEGER IPVT(N)
REAL TOL
REAL X(N),FVEC(M),FJAC(LDFJAC,N),WA(LWA)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMDER1 and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMDER1.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the Jacobian. FCN must be declared in an
EXTERNAL statement in the user calling program, and should be
written as follows.
0 SUBROUTINE FCN(M,N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER M,N,LDFJAC,IFLAG
REAL X(N),FVEC(M),FJAC(LDFJAC,N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC. DO NOT ALTER FJAC.
IF IFLAG = 2 CALCULATE THE JACOBIAN AT X AND
RETURN THIS MATRIX IN FJAC. DO NOT ALTER FVEC.
----------
RETURN
END
1
0 Page
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMDER1. In this case se
IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 FJAC is an output M by N array. The upper N by N submatrix of
FJAC contains an upper triangular matrix R with diagonal ele-
ments of nonincreasing magnitude such that
0 T T T
P *(JAC *JAC)*P = R *R,
0 where P is a permutation matrix and JAC is the final calcu-
lated Jacobian. Column j of P is column IPVT(j) (see below)
of the identity matrix. The lower trapezoidal part of FJAC
contains information generated during the computation of R.
0 LDFJAC is a positive integer input variable not less than M
which specifies the leading dimension of the array FJAC.
0 TOL is a nonnegative input variable. Termination occurs when
the algorithm estimates either that the relative error in the
sum of squares is at most TOL or that the relative error
between X and the solution is at most TOL. Section 4 contain
more details about TOL.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Algorithm estimates that the relative error in the
sum of squares is at most TOL.
0 INFO = 2 Algorithm estimates that the relative error between
X and the solution is at most TOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 FVEC is orthogonal to the columns of the Jacobian t
machine precision.
1
0 Page
0 INFO = 5 Number of calls to FCN with IFLAG = 1 has reached
100*(N+1).
0 INFO = 6 TOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 TOL is too small. No further improvement in the
approximate solution X is possible.
0 Sections 4 and 5 contain more details about INFO.
0 IPVT is an integer output array of length N. IPVT defines a
permutation matrix P such that JAC*P = Q*R, where JAC is the
final calculated Jacobian, Q is orthogonal (not stored), and
is upper triangular with diagonal elements of nonincreasing
magnitude. Column j of P is column IPVT(j) of the identity
matrix.
0 WA is a work array of length LWA.
0 LWA is a positive integer input variable not less than 5*N+M.
0
4. Successful completion.
0 The accuracy of LMDER1 is controlled by the convergence parame-
ter TOL. This parameter is used in tests which make three type
of comparisons between the approximation X and a solution XSOL.
LMDER1 terminates when any of the tests is satisfied. If TOL i
less than the machine precision (as defined by the MINPACK func
tion SPMPAR(1)), then LMDER1 only attempts to satisfy the test
defined by the machine precision. Further progress is not usu-
ally possible. Unless high precision solutions are required,
the recommended value for TOL is the square root of the machine
precision.
0 The tests assume that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then LMDER1 ma
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning LMDER1 with a tighter toler-
ance.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+TOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with TOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
INFO is set to 1 (or to 3 if the second test is also
1
0 Page
0 satisfied).
0 Second convergence test. If D is a diagonal matrix (implicitly
generated by LMDER1) whose entries contain scale factors for
the variables, then this test attempts to guarantee that
0 ENORM(D*(X-XSOL)) .LE. TOL*ENORM(D*XSOL).
0 If this condition is satisfied with TOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but the choice of D is such
that the accuracy of the components of X is usually related t
their sensitivity.
0 Third convergence test. This test is satisfied when FVEC is
orthogonal to the columns of the Jacobian to machine preci-
sion. There is no clear relationship between this test and
the accuracy of LMDER1, and furthermore, the test is equally
well satisfied at other critical points, namely maximizers an
saddle points. Therefore, termination caused by this test
(INFO = 4) should be examined carefully.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMDER1 can be due to improper input
parameters, arithmetic interrupts, or an excessive number of
function evaluations.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or LDFJAC .LT. M, or TOL .LT. 0.E0, or
LWA .LT. 5*N+M.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMDER1. In this
case, it may be possible to remedy the situation by not evalu
ating the functions here, but instead setting the components
of FVEC to numbers that exceed those in the initial FVEC,
thereby indirectly reducing the step length. The step length
can be more directly controlled by using instead LMDER, which
includes in its calling sequence the step-length- governing
parameter FACTOR.
0 Excessive number of function evaluations. If the number of
calls to FCN with IFLAG = 1 reaches 100*(N+1), then this indi
cates that the routine is converging very slowly as measured
by the progress of FVEC, and INFO is set to 5. In this case,
it may be helpful to restart LMDER1, thereby forcing it to
disregard old (and possibly harmful) information.
0
1
0 Page
0 6. Characteristics of the algorithm.
0 LMDER1 is a modification of the Levenberg-Marquardt algorithm.
Two of its main characteristics involve the proper use of
implicitly scaled variables and an optimal choice for the cor-
rection. The use of implicitly scaled variables achieves scale
invariance of LMDER1 and limits the size of the correction in
any direction where the functions are changing rapidly. The
optimal choice of the correction guarantees (under reasonable
conditions) global convergence from starting points far from th
solution and a fast rate of convergence for problems with small
residuals.
0 Timing. The time required by LMDER1 to solve a given problem
depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMDER1 is about N**3 to process
each evaluation of the functions (call to FCN with IFLAG = 1)
and M*(N**2) to process each evaluation of the Jacobian (call
to FCN with IFLAG = 2). Unless FCN can be evaluated quickly,
the timing of LMDER1 will be strongly influenced by the time
spent in FCN.
0 Storage. LMDER1 requires M*N + 2*M + 6*N single precision sto-
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,LMDER,LMPAR,QRFAC,QRSOLV
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
1
0 Page
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMDER1 EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,M,N,LDFJAC,INFO,LWA,NWRITE
INTEGER IPVT(3)
REAL TOL,FNORM
REAL X(3),FVEC(15),FJAC(15,3),WA(30)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LDFJAC = 15
LWA = 30
C
C SET TOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
TOL = SQRT(SPMPAR(1))
C
CALL LMDER1(FCN,M,N,X,FVEC,FJAC,LDFJAC,TOL,
* INFO,IPVT,WA,LWA)
FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
C LAST CARD OF DRIVER FOR LMDER1 EXAMPLE.
C
1
0 Page
0 END
SUBROUTINE FCN(M,N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER M,N,LDFJAC,IFLAG
REAL X(N),FVEC(M),FJAC(LDFJAC,N)
C
C SUBROUTINE FCN FOR LMDER1 EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3,TMP4
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .EQ. 2) GO TO 20
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
GO TO 40
20 CONTINUE
DO 30 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
TMP4 = (X(2)*TMP2 + X(3)*TMP3)**2
FJAC(I,1) = -1.E0
FJAC(I,2) = TMP1*TMP2/TMP4
FJAC(I,3) = TMP1*TMP3/TMP4
30 CONTINUE
40 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 0.8241058E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMDER
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMDER is to minimize the sum of the squares of M
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm. The user must provide a subrou-
tine which calculates the functions and the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE LMDER(FCN,M,N,X,FVEC,FJAC,LDFJAC,FTOL,XTOL,GTOL,
* MAXFEV,DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,
* IPVT,QTF,WA1,WA2,WA3,WA4)
INTEGER M,N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV
INTEGER IPVT(N)
REAL FTOL,XTOL,GTOL,FACTOR
REAL X(N),FVEC(M),FJAC(LDFJAC,N),DIAG(N),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(M)
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMDER and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMDER.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the Jacobian. FCN must be declared in an
EXTERNAL statement in the user calling program, and should be
written as follows.
0 SUBROUTINE FCN(M,N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER M,N,LDFJAC,IFLAG
REAL X(N),FVEC(M),FJAC(LDFJAC,N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC. DO NOT ALTER FJAC.
IF IFLAG = 2 CALCULATE THE JACOBIAN AT X AND
RETURN THIS MATRIX IN FJAC. DO NOT ALTER FVEC.
----------
RETURN
END
1
0 Page
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMDER. In this case set
IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 FJAC is an output M by N array. The upper N by N submatrix of
FJAC contains an upper triangular matrix R with diagonal ele-
ments of nonincreasing magnitude such that
0 T T T
P *(JAC *JAC)*P = R *R,
0 where P is a permutation matrix and JAC is the final calcu-
lated Jacobian. Column j of P is column IPVT(j) (see below)
of the identity matrix. The lower trapezoidal part of FJAC
contains information generated during the computation of R.
0 LDFJAC is a positive integer input variable not less than M
which specifies the leading dimension of the array FJAC.
0 FTOL is a nonnegative input variable. Termination occurs when
both the actual and predicted relative reductions in the sum
of squares are at most FTOL. Therefore, FTOL measures the
relative error desired in the sum of squares. Section 4 con-
tains more details about FTOL.
0 XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at mos
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
0 GTOL is a nonnegative input variable. Termination occurs when
the cosine of the angle between FVEC and any column of the
Jacobian is at most GTOL in absolute value. Therefore, GTOL
measures the orthogonality desired between the function vecto
and the columns of the Jacobian. Section 4 contains more
details about GTOL.
0 MAXFEV is a positive integer input variable. Termination occur
when the number of calls to FCN with IFLAG = 1 has reached
MAXFEV.
1
0 Page
0 DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
0 MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is speci
fied by the input DIAG. Other values of MODE are equivalent
to MODE = 1.
0 FACTOR is a positive input variable used in determining the ini
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTO
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
0 NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X, FVEC, and FJAC available for printing.
FVEC and FJAC should not be altered. If NPRINT is not posi-
tive, no special calls of FCN with IFLAG = 0 are made.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Both actual and predicted relative reductions in th
sum of squares are at most FTOL.
0 INFO = 2 Relative error between two consecutive iterates is
at most XTOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 The cosine of the angle between FVEC and any column
of the Jacobian is at most GTOL in absolute value.
0 INFO = 5 Number of calls to FCN with IFLAG = 1 has reached
MAXFEV.
0 INFO = 6 FTOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 XTOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 8 GTOL is too small. FVEC is orthogonal to the
columns of the Jacobian to machine precision.
0 Sections 4 and 5 contain more details about INFO.
1
0 Page
0 NFEV is an integer output variable set to the number of calls t
FCN with IFLAG = 1.
0 NJEV is an integer output variable set to the number of calls t
FCN with IFLAG = 2.
0 IPVT is an integer output array of length N. IPVT defines a
permutation matrix P such that JAC*P = Q*R, where JAC is the
final calculated Jacobian, Q is orthogonal (not stored), and
is upper triangular with diagonal elements of nonincreasing
magnitude. Column j of P is column IPVT(j) of the identity
matrix.
0 QTF is an output array of length N which contains the first N
elements of the vector (Q transpose)*FVEC.
0 WA1, WA2, and WA3 are work arrays of length N.
0 WA4 is a work array of length M.
0
4. Successful completion.
0 The accuracy of LMDER is controlled by the convergence parame-
ters FTOL, XTOL, and GTOL. These parameters are used in tests
which make three types of comparisons between the approximation
X and a solution XSOL. LMDER terminates when any of the tests
is satisfied. If any of the convergence parameters is less tha
the machine precision (as defined by the MINPACK function
SPMPAR(1)), then LMDER only attempts to satisfy the test define
by the machine precision. Further progress is not usually pos-
sible.
0 The tests assume that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then LMDER may
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning LMDER with tighter toler-
ances.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+FTOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with FTOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
INFO is set to 1 (or to 3 if the second test is also satis-
fied). Unless high precision solutions are required, the
recommended value for FTOL is the square root of the machine
precision.
1
0 Page
0 Second convergence test. If D is the diagonal matrix whose
entries are defined by the array DIAG, then this test attempt
to guarantee that
0 ENORM(D*(X-XSOL)) .LE. XTOL*ENORM(D*XSOL).
0 If this condition is satisfied with XTOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but if MODE = 1, then the
accuracy of the components of X is usually related to their
sensitivity. Unless high precision solutions are required,
the recommended value for XTOL is the square root of the
machine precision.
0 Third convergence test. This test is satisfied when the cosine
of the angle between FVEC and any column of the Jacobian at X
is at most GTOL in absolute value. There is no clear rela-
tionship between this test and the accuracy of LMDER, and
furthermore, the test is equally well satisfied at other crit
ical points, namely maximizers and saddle points. Therefore,
termination caused by this test (INFO = 4) should be examined
carefully. The recommended value for GTOL is zero.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMDER can be due to improper input
parameters, arithmetic interrupts, or an excessive number of
function evaluations.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or LDFJAC .LT. M, or FTOL .LT. 0.E0, or
XTOL .LT. 0.E0, or GTOL .LT. 0.E0, or MAXFEV .LE. 0, or
FACTOR .LE. 0.E0.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMDER. In this
case, it may be possible to remedy the situation by rerunning
LMDER with a smaller value of FACTOR.
0 Excessive number of function evaluations. A reasonable value
for MAXFEV is 100*(N+1). If the number of calls to FCN with
IFLAG = 1 reaches MAXFEV, then this indicates that the routin
is converging very slowly as measured by the progress of FVEC
and INFO is set to 5. In this case, it may be helpful to
restart LMDER with MODE set to 1.
0
6. Characteristics of the algorithm.
0 LMDER is a modification of the Levenberg-Marquardt algorithm.
1
0 Page
0 Two of its main characteristics involve the proper use of
implicitly scaled variables (if MODE = 1) and an optimal choice
for the correction. The use of implicitly scaled variables
achieves scale invariance of LMDER and limits the size of the
correction in any direction where the functions are changing
rapidly. The optimal choice of the correction guarantees (unde
reasonable conditions) global convergence from starting points
far from the solution and a fast rate of convergence for prob-
lems with small residuals.
0 Timing. The time required by LMDER to solve a given problem
depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMDER is about N**3 to process eac
evaluation of the functions (call to FCN with IFLAG = 1) and
M*(N**2) to process each evaluation of the Jacobian (call to
FCN with IFLAG = 2). Unless FCN can be evaluated quickly, th
timing of LMDER will be strongly influenced by the time spent
in FCN.
0 Storage. LMDER requires M*N + 2*M + 6*N single precision sto-
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,LMPAR,QRFAC,QRSOLV
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
1
0 Page
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMDER EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,M,N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV,NWRITE
INTEGER IPVT(3)
REAL FTOL,XTOL,GTOL,FACTOR,FNORM
REAL X(3),FVEC(15),FJAC(15,3),DIAG(3),QTF(3),
* WA1(3),WA2(3),WA3(3),WA4(15)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LDFJAC = 15
C
C SET FTOL AND XTOL TO THE SQUARE ROOT OF THE MACHINE PRECISION
C AND GTOL TO ZERO. UNLESS HIGH PRECISION SOLUTIONS ARE
C REQUIRED, THESE ARE THE RECOMMENDED SETTINGS.
C
FTOL = SQRT(SPMPAR(1))
XTOL = SQRT(SPMPAR(1))
GTOL = 0.E0
C
MAXFEV = 400
MODE = 1
FACTOR = 1.E2
NPRINT = 0
C
CALL LMDER(FCN,M,N,X,FVEC,FJAC,LDFJAC,FTOL,XTOL,GTOL,
* MAXFEV,DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,
* IPVT,QTF,WA1,WA2,WA3,WA4)
FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,NFEV,NJEV,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
1
0 Page
0 * 5X,31H NUMBER OF FUNCTION EVALUATIONS,I10 //
* 5X,31H NUMBER OF JACOBIAN EVALUATIONS,I10 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
C LAST CARD OF DRIVER FOR LMDER EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER M,N,LDFJAC,IFLAG
REAL X(N),FVEC(M),FJAC(LDFJAC,N)
C
C SUBROUTINE FCN FOR LMDER EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3,TMP4
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .NE. 0) GO TO 5
C
C INSERT PRINT STATEMENTS HERE WHEN NPRINT IS POSITIVE.
C
RETURN
5 CONTINUE
IF (IFLAG .EQ. 2) GO TO 20
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
GO TO 40
20 CONTINUE
DO 30 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
TMP4 = (X(2)*TMP2 + X(3)*TMP3)**2
FJAC(I,1) = -1.E0
FJAC(I,2) = TMP1*TMP2/TMP4
FJAC(I,3) = TMP1*TMP3/TMP4
30 CONTINUE
40 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
1
0 Page
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 NUMBER OF FUNCTION EVALUATIONS 6
0 NUMBER OF JACOBIAN EVALUATIONS 5
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 0.8241058E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMSTR1
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMSTR1 is to minimize the sum of the squares of
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm which uses minimal storage. This
is done by using the more general least-squares solver LMSTR.
The user must provide a subroutine which calculates the func-
tions and the rows of the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE LMSTR1(FCN,M,N,X,FVEC,FJAC,LDFJAC,TOL,
* INFO,IPVT,WA,LWA)
INTEGER M,N,LDFJAC,INFO,LWA
INTEGER IPVT(N)
REAL TOL
REAL X(N),FVEC(M),FJAC(LDFJAC,N),WA(LWA)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMSTR1 and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMSTR1.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the rows of the Jacobian. FCN must be
declared in an EXTERNAL statement in the user calling program
and should be written as follows.
0 SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M),FJROW(N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
IF IFLAG = I CALCULATE THE (I-1)-ST ROW OF THE
JACOBIAN AT X AND RETURN THIS VECTOR IN FJROW.
----------
RETURN
1
0 Page
0 END
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMSTR1. In this case se
IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 FJAC is an output N by N array. The upper triangle of FJAC con
tains an upper triangular matrix R such that
0 T T T
P *(JAC *JAC)*P = R *R,
0 where P is a permutation matrix and JAC is the final calcu-
lated Jacobian. Column j of P is column IPVT(j) (see below)
of the identity matrix. The lower triangular part of FJAC
contains information generated during the computation of R.
0 LDFJAC is a positive integer input variable not less than N
which specifies the leading dimension of the array FJAC.
0 TOL is a nonnegative input variable. Termination occurs when
the algorithm estimates either that the relative error in the
sum of squares is at most TOL or that the relative error
between X and the solution is at most TOL. Section 4 contain
more details about TOL.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Algorithm estimates that the relative error in the
sum of squares is at most TOL.
0 INFO = 2 Algorithm estimates that the relative error between
X and the solution is at most TOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 FVEC is orthogonal to the columns of the Jacobian t
1
0 Page
0 machine precision.
0 INFO = 5 Number of calls to FCN with IFLAG = 1 has reached
100*(N+1).
0 INFO = 6 TOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 TOL is too small. No further improvement in the
approximate solution X is possible.
0 Sections 4 and 5 contain more details about INFO.
0 IPVT is an integer output array of length N. IPVT defines a
permutation matrix P such that JAC*P = Q*R, where JAC is the
final calculated Jacobian, Q is orthogonal (not stored), and
is upper triangular. Column j of P is column IPVT(j) of the
identity matrix.
0 WA is a work array of length LWA.
0 LWA is a positive integer input variable not less than 5*N+M.
0
4. Successful completion.
0 The accuracy of LMSTR1 is controlled by the convergence parame-
ter TOL. This parameter is used in tests which make three type
of comparisons between the approximation X and a solution XSOL.
LMSTR1 terminates when any of the tests is satisfied. If TOL i
less than the machine precision (as defined by the MINPACK func
tion SPMPAR(1)), then LMSTR1 only attempts to satisfy the test
defined by the machine precision. Further progress is not usu-
ally possible. Unless high precision solutions are required,
the recommended value for TOL is the square root of the machine
precision.
0 The tests assume that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then LMSTR1 ma
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning LMSTR1 with a tighter toler-
ance.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+TOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with TOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
1
0 Page
0 INFO is set to 1 (or to 3 if the second test is also satis-
fied).
0 Second convergence test. If D is a diagonal matrix (implicitly
generated by LMSTR1) whose entries contain scale factors for
the variables, then this test attempts to guarantee that
0 ENORM(D*(X-XSOL)) .LE. TOL*ENORM(D*XSOL).
0 If this condition is satisfied with TOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but the choice of D is such
that the accuracy of the components of X is usually related t
their sensitivity.
0 Third convergence test. This test is satisfied when FVEC is
orthogonal to the columns of the Jacobian to machine preci-
sion. There is no clear relationship between this test and
the accuracy of LMSTR1, and furthermore, the test is equally
well satisfied at other critical points, namely maximizers an
saddle points. Therefore, termination caused by this test
(INFO = 4) should be examined carefully.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMSTR1 can be due to improper input
parameters, arithmetic interrupts, or an excessive number of
function evaluations.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or LDFJAC .LT. N, or TOL .LT. 0.E0, or
LWA .LT. 5*N+M.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMSTR1. In this
case, it may be possible to remedy the situation by not evalu
ating the functions here, but instead setting the components
of FVEC to numbers that exceed those in the initial FVEC,
thereby indirectly reducing the step length. The step length
can be more directly controlled by using instead LMSTR, which
includes in its calling sequence the step-length- governing
parameter FACTOR.
0 Excessive number of function evaluations. If the number of
calls to FCN with IFLAG = 1 reaches 100*(N+1), then this indi
cates that the routine is converging very slowly as measured
by the progress of FVEC, and INFO is set to 5. In this case,
it may be helpful to restart LMSTR1, thereby forcing it to
disregard old (and possibly harmful) information.
1
0 Page
0
6. Characteristics of the algorithm.
0 LMSTR1 is a modification of the Levenberg-Marquardt algorithm.
Two of its main characteristics involve the proper use of
implicitly scaled variables and an optimal choice for the cor-
rection. The use of implicitly scaled variables achieves scale
invariance of LMSTR1 and limits the size of the correction in
any direction where the functions are changing rapidly. The
optimal choice of the correction guarantees (under reasonable
conditions) global convergence from starting points far from th
solution and a fast rate of convergence for problems with small
residuals.
0 Timing. The time required by LMSTR1 to solve a given problem
depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMSTR1 is about N**3 to process
each evaluation of the functions (call to FCN with IFLAG = 1)
and 1.5*(N**2) to process each row of the Jacobian (call to
FCN with IFLAG .GE. 2). Unless FCN can be evaluated quickly,
the timing of LMSTR1 will be strongly influenced by the time
spent in FCN.
0 Storage. LMSTR1 requires N**2 + 2*M + 6*N single precision sto
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,LMSTR,LMPAR,QRFAC,QRSOLV,
RWUPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
1
0 Page
0 to the data
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMSTR1 EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,M,N,LDFJAC,INFO,LWA,NWRITE
INTEGER IPVT(3)
REAL TOL,FNORM
REAL X(3),FVEC(15),FJAC(3,3),WA(30)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LDFJAC = 3
LWA = 30
C
C SET TOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
TOL = SQRT(SPMPAR(1))
C
CALL LMSTR1(FCN,M,N,X,FVEC,FJAC,LDFJAC,TOL,
* INFO,IPVT,WA,LWA)
FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
1
0 Page
0 C LAST CARD OF DRIVER FOR LMSTR1 EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M),FJROW(N)
C
C SUBROUTINE FCN FOR LMSTR1 EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3,TMP4
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .GE. 2) GO TO 20
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
GO TO 40
20 CONTINUE
I = IFLAG - 1
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
TMP4 = (X(2)*TMP2 + X(3)*TMP3)**2
FJROW(1) = -1.E0
FJROW(2) = TMP1*TMP2/TMP4
FJROW(3) = TMP1*TMP3/TMP4
30 CONTINUE
40 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 0.8241058E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMSTR
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMSTR is to minimize the sum of the squares of M
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm which uses minimal storage. The
user must provide a subroutine which calculates the functions
and the rows of the Jacobian.
0
2. Subroutine and type statements.
0 SUBROUTINE LMSTR(FCN,M,N,X,FVEC,FJAC,LDFJAC,FTOL,XTOL,GTOL,
* MAXFEV,DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,
* IPVT,QTF,WA1,WA2,WA3,WA4)
INTEGER M,N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV
INTEGER IPVT(N)
REAL FTOL,XTOL,GTOL,FACTOR
REAL X(N),FVEC(M),FJAC(LDFJAC,N),DIAG(N),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(M)
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMSTR and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMSTR.
0 FCN is the name of the user-supplied subroutine which calculate
the functions and the rows of the Jacobian. FCN must be
declared in an EXTERNAL statement in the user calling program
and should be written as follows.
0 SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M),FJROW(N)
----------
IF IFLAG = 1 CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
IF IFLAG = I CALCULATE THE (I-1)-ST ROW OF THE
JACOBIAN AT X AND RETURN THIS VECTOR IN FJROW.
----------
RETURN
1
0 Page
0 END
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMSTR. In this case set
IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 FJAC is an output N by N array. The upper triangle of FJAC con
tains an upper triangular matrix R such that
0 T T T
P *(JAC *JAC)*P = R *R,
0 where P is a permutation matrix and JAC is the final calcu-
lated Jacobian. Column j of P is column IPVT(j) (see below)
of the identity matrix. The lower triangular part of FJAC
contains information generated during the computation of R.
0 LDFJAC is a positive integer input variable not less than N
which specifies the leading dimension of the array FJAC.
0 FTOL is a nonnegative input variable. Termination occurs when
both the actual and predicted relative reductions in the sum
of squares are at most FTOL. Therefore, FTOL measures the
relative error desired in the sum of squares. Section 4 con-
tains more details about FTOL.
0 XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at mos
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
0 GTOL is a nonnegative input variable. Termination occurs when
the cosine of the angle between FVEC and any column of the
Jacobian is at most GTOL in absolute value. Therefore, GTOL
measures the orthogonality desired between the function vecto
and the columns of the Jacobian. Section 4 contains more
details about GTOL.
0 MAXFEV is a positive integer input variable. Termination occur
when the number of calls to FCN with IFLAG = 1 has reached
1
0 Page
0 MAXFEV.
0 DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
0 MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is speci
fied by the input DIAG. Other values of MODE are equivalent
to MODE = 1.
0 FACTOR is a positive input variable used in determining the ini
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTO
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
0 NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X and FVEC available for printing. If NPRINT
is not positive, no special calls of FCN with IFLAG = 0 are
made.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Both actual and predicted relative reductions in th
sum of squares are at most FTOL.
0 INFO = 2 Relative error between two consecutive iterates is
at most XTOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 The cosine of the angle between FVEC and any column
of the Jacobian is at most GTOL in absolute value.
0 INFO = 5 Number of calls to FCN with IFLAG = 1 has reached
MAXFEV.
0 INFO = 6 FTOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 XTOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 8 GTOL is too small. FVEC is orthogonal to the
columns of the Jacobian to machine precision.
1
0 Page
0 Sections 4 and 5 contain more details about INFO.
0 NFEV is an integer output variable set to the number of calls t
FCN with IFLAG = 1.
0 NJEV is an integer output variable set to the number of calls t
FCN with IFLAG = 2.
0 IPVT is an integer output array of length N. IPVT defines a
permutation matrix P such that JAC*P = Q*R, where JAC is the
final calculated Jacobian, Q is orthogonal (not stored), and
is upper triangular. Column j of P is column IPVT(j) of the
identity matrix.
0 QTF is an output array of length N which contains the first N
elements of the vector (Q transpose)*FVEC.
0 WA1, WA2, and WA3 are work arrays of length N.
0 WA4 is a work array of length M.
0
4. Successful completion.
0 The accuracy of LMSTR is controlled by the convergence parame-
ters FTOL, XTOL, and GTOL. These parameters are used in tests
which make three types of comparisons between the approximation
X and a solution XSOL. LMSTR terminates when any of the tests
is satisfied. If any of the convergence parameters is less tha
the machine precision (as defined by the MINPACK function
SPMPAR(1)), then LMSTR only attempts to satisfy the test define
by the machine precision. Further progress is not usually pos-
sible.
0 The tests assume that the functions and the Jacobian are coded
consistently, and that the functions are reasonably well
behaved. If these conditions are not satisfied, then LMSTR may
incorrectly indicate convergence. The coding of the Jacobian
can be checked by the MINPACK subroutine CHKDER. If the Jaco-
bian is coded correctly, then the validity of the answer can be
checked, for example, by rerunning LMSTR with tighter toler-
ances.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+FTOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with FTOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
INFO is set to 1 (or to 3 if the second test is also satis-
fied). Unless high precision solutions are required, the
recommended value for FTOL is the square root of the machine
1
0 Page
0 precision.
0 Second convergence test. If D is the diagonal matrix whose
entries are defined by the array DIAG, then this test attempt
to guarantee that
0 ENORM(D*(X-XSOL)) .LE. XTOL*ENORM(D*XSOL).
0 If this condition is satisfied with XTOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but if MODE = 1, then the
accuracy of the components of X is usually related to their
sensitivity. Unless high precision solutions are required,
the recommended value for XTOL is the square root of the
machine precision.
0 Third convergence test. This test is satisfied when the cosine
of the angle between FVEC and any column of the Jacobian at X
is at most GTOL in absolute value. There is no clear rela-
tionship between this test and the accuracy of LMSTR, and
furthermore, the test is equally well satisfied at other crit
ical points, namely maximizers and saddle points. Therefore,
termination caused by this test (INFO = 4) should be examined
carefully. The recommended value for GTOL is zero.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMSTR can be due to improper input
parameters, arithmetic interrupts, or an excessive number of
function evaluations.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or LDFJAC .LT. N, or FTOL .LT. 0.E0, or
XTOL .LT. 0.E0, or GTOL .LT. 0.E0, or MAXFEV .LE. 0, or
FACTOR .LE. 0.E0.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMSTR. In this
case, it may be possible to remedy the situation by rerunning
LMSTR with a smaller value of FACTOR.
0 Excessive number of function evaluations. A reasonable value
for MAXFEV is 100*(N+1). If the number of calls to FCN with
IFLAG = 1 reaches MAXFEV, then this indicates that the routin
is converging very slowly as measured by the progress of FVEC
and INFO is set to 5. In this case, it may be helpful to
restart LMSTR with MODE set to 1.
0
6. Characteristics of the algorithm.
1
0 Page
0 LMSTR is a modification of the Levenberg-Marquardt algorithm.
Two of its main characteristics involve the proper use of
implicitly scaled variables (if MODE = 1) and an optimal choice
for the correction. The use of implicitly scaled variables
achieves scale invariance of LMSTR and limits the size of the
correction in any direction where the functions are changing
rapidly. The optimal choice of the correction guarantees (unde
reasonable conditions) global convergence from starting points
far from the solution and a fast rate of convergence for prob-
lems with small residuals.
0 Timing. The time required by LMSTR to solve a given problem
depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMSTR is about N**3 to process eac
evaluation of the functions (call to FCN with IFLAG = 1) and
1.5*(N**2) to process each row of the Jacobian (call to FCN
with IFLAG .GE. 2). Unless FCN can be evaluated quickly, the
timing of LMSTR will be strongly influenced by the time spent
in FCN.
0 Storage. LMSTR requires N**2 + 2*M + 6*N single precision sto-
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,LMPAR,QRFAC,QRSOLV,RWUPDT
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
1
0 Page
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMSTR EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,M,N,LDFJAC,MAXFEV,MODE,NPRINT,INFO,NFEV,NJEV,NWRITE
INTEGER IPVT(3)
REAL FTOL,XTOL,GTOL,FACTOR,FNORM
REAL X(3),FVEC(15),FJAC(3,3),DIAG(3),QTF(3),
* WA1(3),WA2(3),WA3(3),WA4(15)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LDFJAC = 3
C
C SET FTOL AND XTOL TO THE SQUARE ROOT OF THE MACHINE PRECISION
C AND GTOL TO ZERO. UNLESS HIGH PRECISION SOLUTIONS ARE
C REQUIRED, THESE ARE THE RECOMMENDED SETTINGS.
C
FTOL = SQRT(SPMPAR(1))
XTOL = SQRT(SPMPAR(1))
GTOL = 0.E0
C
MAXFEV = 400
MODE = 1
FACTOR = 1.E2
NPRINT = 0
C
CALL LMSTR(FCN,M,N,X,FVEC,FJAC,LDFJAC,FTOL,XTOL,GTOL,
* MAXFEV,DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,NJEV,
* IPVT,QTF,WA1,WA2,WA3,WA4)
FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,NFEV,NJEV,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
1
0 Page
0 * 5X,31H NUMBER OF FUNCTION EVALUATIONS,I10 //
* 5X,31H NUMBER OF JACOBIAN EVALUATIONS,I10 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
C LAST CARD OF DRIVER FOR LMSTR EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M),FJROW(N)
C
C SUBROUTINE FCN FOR LMSTR EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3,TMP4
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .NE. 0) GO TO 5
C
C INSERT PRINT STATEMENTS HERE WHEN NPRINT IS POSITIVE.
C
RETURN
5 CONTINUE
IF (IFLAG .GE. 2) GO TO 20
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
GO TO 40
20 CONTINUE
I = IFLAG - 1
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
TMP4 = (X(2)*TMP2 + X(3)*TMP3)**2
FJROW(1) = -1.E0
FJROW(2) = TMP1*TMP2/TMP4
FJROW(3) = TMP1*TMP3/TMP4
30 CONTINUE
40 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
1
0 Page
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 NUMBER OF FUNCTION EVALUATIONS 6
0 NUMBER OF JACOBIAN EVALUATIONS 5
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 0.8241058E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMDIF1
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMDIF1 is to minimize the sum of the squares of
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm. This is done by using the more
general least-squares solver LMDIF. The user must provide a
subroutine which calculates the functions. The Jacobian is the
calculated by a forward-difference approximation.
0
2. Subroutine and type statements.
0 SUBROUTINE LMDIF1(FCN,M,N,X,FVEC,TOL,INFO,IWA,WA,LWA)
INTEGER M,N,INFO,LWA
INTEGER IWA(N)
REAL TOL
REAL X(N),FVEC(M),WA(LWA)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMDIF1 and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMDIF1.
0 FCN is the name of the user-supplied subroutine which calculate
the functions. FCN must be declared in an EXTERNAL statement
in the user calling program, and should be written as follows
0 SUBROUTINE FCN(M,N,X,FVEC,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M)
----------
CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
----------
RETURN
END
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMDIF1. In this case se
1
0 Page
0 IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 TOL is a nonnegative input variable. Termination occurs when
the algorithm estimates either that the relative error in the
sum of squares is at most TOL or that the relative error
between X and the solution is at most TOL. Section 4 contain
more details about TOL.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Algorithm estimates that the relative error in the
sum of squares is at most TOL.
0 INFO = 2 Algorithm estimates that the relative error between
X and the solution is at most TOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 FVEC is orthogonal to the columns of the Jacobian t
machine precision.
0 INFO = 5 Number of calls to FCN has reached or exceeded
200*(N+1).
0 INFO = 6 TOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 TOL is too small. No further improvement in the
approximate solution X is possible.
0 Sections 4 and 5 contain more details about INFO.
0 IWA is an integer work array of length N.
0 WA is a work array of length LWA.
0 LWA is a positive integer input variable not less than
1
0 Page
0 M*N+5*N+M.
0
4. Successful completion.
0 The accuracy of LMDIF1 is controlled by the convergence parame-
ter TOL. This parameter is used in tests which make three type
of comparisons between the approximation X and a solution XSOL.
LMDIF1 terminates when any of the tests is satisfied. If TOL i
less than the machine precision (as defined by the MINPACK func
tion SPMPAR(1)), then LMDIF1 only attempts to satisfy the test
defined by the machine precision. Further progress is not usu-
ally possible. Unless high precision solutions are required,
the recommended value for TOL is the square root of the machine
precision.
0 The tests assume that the functions are reasonably well behaved
If this condition is not satisfied, then LMDIF1 may incorrectly
indicate convergence. The validity of the answer can be
checked, for example, by rerunning LMDIF1 with a tighter toler-
ance.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+TOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with TOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
INFO is set to 1 (or to 3 if the second test is also satis-
fied).
0 Second convergence test. If D is a diagonal matrix (implicitly
generated by LMDIF1) whose entries contain scale factors for
the variables, then this test attempts to guarantee that
0 ENORM(D*(X-XSOL)) .LE. TOL*ENORM(D*XSOL).
0 If this condition is satisfied with TOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but the choice of D is such
that the accuracy of the components of X is usually related t
their sensitivity.
0 Third convergence test. This test is satisfied when FVEC is
orthogonal to the columns of the Jacobian to machine preci-
sion. There is no clear relationship between this test and
the accuracy of LMDIF1, and furthermore, the test is equally
well satisfied at other critical points, namely maximizers an
saddle points. Also, errors in the functions (see below) may
result in the test being satisfied at a point not close to th
1
0 Page
0 minimum. Therefore, termination caused by this test
(INFO = 4) should be examined carefully.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMDIF1 can be due to improper input
parameters, arithmetic interrupts, an excessive number of func-
tion evaluations, or errors in the functions.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or TOL .LT. 0.E0, or LWA .LT. M*N+5*N+M.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMDIF1. In this
case, it may be possible to remedy the situation by not evalu
ating the functions here, but instead setting the components
of FVEC to numbers that exceed those in the initial FVEC,
thereby indirectly reducing the step length. The step length
can be more directly controlled by using instead LMDIF, which
includes in its calling sequence the step-length-governing
parameter FACTOR.
0 Excessive number of function evaluations. If the number of
calls to FCN reaches 200*(N+1), then this indicates that the
routine is converging very slowly as measured by the progress
of FVEC, and INFO is set to 5. In this case, it may be help-
ful to restart LMDIF1, thereby forcing it to disregard old
(and possibly harmful) information.
0 Errors in the functions. The choice of step length in the for-
ward-difference approximation to the Jacobian assumes that th
relative errors in the functions are of the order of the
machine precision. If this is not the case, LMDIF1 may fail
(usually with INFO = 4). The user should then use LMDIF
instead, or one of the programs which require the analytic
Jacobian (LMDER1 and LMDER).
0
6. Characteristics of the algorithm.
0 LMDIF1 is a modification of the Levenberg-Marquardt algorithm.
Two of its main characteristics involve the proper use of
implicitly scaled variables and an optimal choice for the cor-
rection. The use of implicitly scaled variables achieves scale
invariance of LMDIF1 and limits the size of the correction in
any direction where the functions are changing rapidly. The
optimal choice of the correction guarantees (under reasonable
conditions) global convergence from starting points far from th
solution and a fast rate of convergence for problems with small
residuals.
0 Timing. The time required by LMDIF1 to solve a given problem
1
0 Page
0 depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMDIF1 is about N**3 to process
each evaluation of the functions (one call to FCN) and
M*(N**2) to process each approximation to the Jacobian (N
calls to FCN). Unless FCN can be evaluated quickly, the tim-
ing of LMDIF1 will be strongly influenced by the time spent i
FCN.
0 Storage. LMDIF1 requires M*N + 2*M + 6*N single precision sto-
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,FDJAC2,LMDIF,LMPAR,
QRFAC,QRSOLV
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMDIF1 EXAMPLE.
C SINGLE PRECISION VERSION
C
1
0 Page
0 C **********
INTEGER J,M,N,INFO,LWA,NWRITE
INTEGER IWA(3)
REAL TOL,FNORM
REAL X(3),FVEC(15),WA(75)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LWA = 75
C
C SET TOL TO THE SQUARE ROOT OF THE MACHINE PRECISION.
C UNLESS HIGH PRECISION SOLUTIONS ARE REQUIRED,
C THIS IS THE RECOMMENDED SETTING.
C
TOL = SQRT(SPMPAR(1))
C
CALL LMDIF1(FCN,M,N,X,FVEC,TOL,INFO,IWA,WA,LWA)
FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
C LAST CARD OF DRIVER FOR LMDIF1 EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M)
C
C SUBROUTINE FCN FOR LMDIF1 EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
1
0 Page
0 DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
0 0.8241057E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine LMDIF
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of LMDIF is to minimize the sum of the squares of M
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm. The user must provide a subrou-
tine which calculates the functions. The Jacobian is then cal-
culated by a forward-difference approximation.
0
2. Subroutine and type statements.
0 SUBROUTINE LMDIF(FCN,M,N,X,FVEC,FTOL,XTOL,GTOL,MAXFEV,EPSFCN,
* DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,FJAC,LDFJAC,
* IPVT,QTF,WA1,WA2,WA3,WA4)
INTEGER M,N,MAXFEV,MODE,NPRINT,INFO,NFEV,LDFJAC
INTEGER IPVT(N)
REAL FTOL,XTOL,GTOL,EPSFCN,FACTOR
REAL X(N),FVEC(M),DIAG(N),FJAC(LDFJAC,N),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(M)
EXTERNAL FCN
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to LMDIF and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMDIF.
0 FCN is the name of the user-supplied subroutine which calculate
the functions. FCN must be declared in an EXTERNAL statement
in the user calling program, and should be written as follows
0 SUBROUTINE FCN(M,N,X,FVEC,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M)
----------
CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
----------
RETURN
END
1
0 Page
0 The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMDIF. In this case set
IFLAG to a negative integer.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables. N must not exceed M.
0 X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
0 FVEC is an output array of length M which contains the function
evaluated at the output X.
0 FTOL is a nonnegative input variable. Termination occurs when
both the actual and predicted relative reductions in the sum
of squares are at most FTOL. Therefore, FTOL measures the
relative error desired in the sum of squares. Section 4 con-
tains more details about FTOL.
0 XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at mos
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
0 GTOL is a nonnegative input variable. Termination occurs when
the cosine of the angle between FVEC and any column of the
Jacobian is at most GTOL in absolute value. Therefore, GTOL
measures the orthogonality desired between the function vecto
and the columns of the Jacobian. Section 4 contains more
details about GTOL.
0 MAXFEV is a positive integer input variable. Termination occur
when the number of calls to FCN is at least MAXFEV by the end
of an iteration.
0 EPSFCN is an input variable used in determining a suitable step
for the forward-difference approximation. This approximation
assumes that the relative errors in the functions are of the
order of EPSFCN. If EPSFCN is less than the machine preci-
sion, it is assumed that the relative errors in the functions
are of the order of the machine precision.
0 DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
0 MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is
1
0 Page
0 specified by the input DIAG. Other values of MODE are equiva
lent to MODE = 1.
0 FACTOR is a positive input variable used in determining the ini
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTO
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
0 NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X and FVEC available for printing. If NPRINT
is not positive, no special calls of FCN with IFLAG = 0 are
made.
0 INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
0 INFO = 0 Improper input parameters.
0 INFO = 1 Both actual and predicted relative reductions in th
sum of squares are at most FTOL.
0 INFO = 2 Relative error between two consecutive iterates is
at most XTOL.
0 INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
0 INFO = 4 The cosine of the angle between FVEC and any column
of the Jacobian is at most GTOL in absolute value.
0 INFO = 5 Number of calls to FCN has reached or exceeded
MAXFEV.
0 INFO = 6 FTOL is too small. No further reduction in the sum
of squares is possible.
0 INFO = 7 XTOL is too small. No further improvement in the
approximate solution X is possible.
0 INFO = 8 GTOL is too small. FVEC is orthogonal to the
columns of the Jacobian to machine precision.
0 Sections 4 and 5 contain more details about INFO.
0 NFEV is an integer output variable set to the number of calls t
FCN.
0 FJAC is an output M by N array. The upper N by N submatrix of
FJAC contains an upper triangular matrix R with diagonal ele-
ments of nonincreasing magnitude such that
1
0 Page
0 T T T
P *(JAC *JAC)*P = R *R,
0 where P is a permutation matrix and JAC is the final calcu-
lated Jacobian. Column j of P is column IPVT(j) (see below)
of the identity matrix. The lower trapezoidal part of FJAC
contains information generated during the computation of R.
0 LDFJAC is a positive integer input variable not less than M
which specifies the leading dimension of the array FJAC.
0 IPVT is an integer output array of length N. IPVT defines a
permutation matrix P such that JAC*P = Q*R, where JAC is the
final calculated Jacobian, Q is orthogonal (not stored), and
is upper triangular with diagonal elements of nonincreasing
magnitude. Column j of P is column IPVT(j) of the identity
matrix.
0 QTF is an output array of length N which contains the first N
elements of the vector (Q transpose)*FVEC.
0 WA1, WA2, and WA3 are work arrays of length N.
0 WA4 is a work array of length M.
0
4. Successful completion.
0 The accuracy of LMDIF is controlled by the convergence parame-
ters FTOL, XTOL, and GTOL. These parameters are used in tests
which make three types of comparisons between the approximation
X and a solution XSOL. LMDIF terminates when any of the tests
is satisfied. If any of the convergence parameters is less tha
the machine precision (as defined by the MINPACK function
SPMPAR(1)), then LMDIF only attempts to satisfy the test define
by the machine precision. Further progress is not usually pos-
sible.
0 The tests assume that the functions are reasonably well behaved
If this condition is not satisfied, then LMDIF may incorrectly
indicate convergence. The validity of the answer can be
checked, for example, by rerunning LMDIF with tighter toler-
ances.
0 First convergence test. If ENORM(Z) denotes the Euclidean norm
of a vector Z, then this test attempts to guarantee that
0 ENORM(FVEC) .LE. (1+FTOL)*ENORM(FVECS),
0 where FVECS denotes the functions evaluated at XSOL. If this
condition is satisfied with FTOL = 10**(-K), then the final
residual norm ENORM(FVEC) has K significant decimal digits an
INFO is set to 1 (or to 3 if the second test is also satis-
fied). Unless high precision solutions are required, the
1
0 Page
0 recommended value for FTOL is the square root of the machine
precision.
0 Second convergence test. If D is the diagonal matrix whose
entries are defined by the array DIAG, then this test attempt
to guarantee that
0 ENORM(D*(X-XSOL)) .LE. XTOL*ENORM(D*XSOL).
0 If this condition is satisfied with XTOL = 10**(-K), then the
larger components of D*X have K significant decimal digits an
INFO is set to 2 (or to 3 if the first test is also satis-
fied). There is a danger that the smaller components of D*X
may have large relative errors, but if MODE = 1, then the
accuracy of the components of X is usually related to their
sensitivity. Unless high precision solutions are required,
the recommended value for XTOL is the square root of the
machine precision.
0 Third convergence test. This test is satisfied when the cosine
of the angle between FVEC and any column of the Jacobian at X
is at most GTOL in absolute value. There is no clear rela-
tionship between this test and the accuracy of LMDIF, and
furthermore, the test is equally well satisfied at other crit
ical points, namely maximizers and saddle points. Therefore,
termination caused by this test (INFO = 4) should be examined
carefully. The recommended value for GTOL is zero.
0
5. Unsuccessful completion.
0 Unsuccessful termination of LMDIF can be due to improper input
parameters, arithmetic interrupts, or an excessive number of
function evaluations.
0 Improper input parameters. INFO is set to 0 if N .LE. 0, or
M .LT. N, or LDFJAC .LT. M, or FTOL .LT. 0.E0, or
XTOL .LT. 0.E0, or GTOL .LT. 0.E0, or MAXFEV .LE. 0, or
FACTOR .LE. 0.E0.
0 Arithmetic interrupts. If these interrupts occur in the FCN
subroutine during an early stage of the computation, they may
be caused by an unacceptable choice of X by LMDIF. In this
case, it may be possible to remedy the situation by rerunning
LMDIF with a smaller value of FACTOR.
0 Excessive number of function evaluations. A reasonable value
for MAXFEV is 200*(N+1). If the number of calls to FCN
reaches MAXFEV, then this indicates that the routine is con-
verging very slowly as measured by the progress of FVEC, and
INFO is set to 5. In this case, it may be helpful to restart
LMDIF with MODE set to 1.
0
1
0 Page
0 6. Characteristics of the algorithm.
0 LMDIF is a modification of the Levenberg-Marquardt algorithm.
Two of its main characteristics involve the proper use of
implicitly scaled variables (if MODE = 1) and an optimal choice
for the correction. The use of implicitly scaled variables
achieves scale invariance of LMDIF and limits the size of the
correction in any direction where the functions are changing
rapidly. The optimal choice of the correction guarantees (unde
reasonable conditions) global convergence from starting points
far from the solution and a fast rate of convergence for prob-
lems with small residuals.
0 Timing. The time required by LMDIF to solve a given problem
depends on M and N, the behavior of the functions, the accu-
racy requested, and the starting point. The number of arith-
metic operations needed by LMDIF is about N**3 to process eac
evaluation of the functions (one call to FCN) and M*(N**2) to
process each approximation to the Jacobian (N calls to FCN).
Unless FCN can be evaluated quickly, the timing of LMDIF will
be strongly influenced by the time spent in FCN.
0 Storage. LMDIF requires M*N + 2*M + 6*N single precision sto-
rage locations and N integer storage locations, in addition t
the storage required by the program. There are no internally
declared storage arrays.
0
7. Subprograms required.
0 USER-supplied ...... FCN
0 MINPACK-supplied ... SPMPAR,ENORM,FDJAC2,LMPAR,QRFAC,QRSOLV
0 FORTRAN-supplied ... ABS,AMAX1,AMIN1,SQRT,MOD
0
8. References.
0 Jorge J. More, The Levenberg-Marquardt Algorithm, Implementatio
and Theory. Numerical Analysis, G. A. Watson, editor.
Lecture Notes in Mathematics 630, Springer-Verlag, 1977.
0
9. Example.
0 The problem is to determine the values of x(1), x(2), and x(3)
which provide the best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
1
0 Page
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
0 C **********
C
C DRIVER FOR LMDIF EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER J,M,N,MAXFEV,MODE,NPRINT,INFO,NFEV,LDFJAC,NWRITE
INTEGER IPVT(3)
REAL FTOL,XTOL,GTOL,EPSFCN,FACTOR,FNORM
REAL X(3),FVEC(15),DIAG(3),FJAC(15,3),QTF(3),
* WA1(3),WA2(3),WA3(3),WA4(15)
REAL ENORM,SPMPAR
EXTERNAL FCN
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING STARTING VALUES PROVIDE A ROUGH FIT.
C
X(1) = 1.E0
X(2) = 1.E0
X(3) = 1.E0
C
LDFJAC = 15
C
C SET FTOL AND XTOL TO THE SQUARE ROOT OF THE MACHINE PRECISION
C AND GTOL TO ZERO. UNLESS HIGH PRECISION SOLUTIONS ARE
C REQUIRED, THESE ARE THE RECOMMENDED SETTINGS.
C
FTOL = SQRT(SPMPAR(1))
XTOL = SQRT(SPMPAR(1))
GTOL = 0.E0
C
MAXFEV = 800
EPSFCN = 0.E0
MODE = 1
FACTOR = 1.E2
NPRINT = 0
C
CALL LMDIF(FCN,M,N,X,FVEC,FTOL,XTOL,GTOL,MAXFEV,EPSFCN,
* DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,FJAC,LDFJAC,
* IPVT,QTF,WA1,WA2,WA3,WA4)
1
0 Page
0 FNORM = ENORM(M,FVEC)
WRITE (NWRITE,1000) FNORM,NFEV,INFO,(X(J),J=1,N)
STOP
1000 FORMAT (5X,31H FINAL L2 NORM OF THE RESIDUALS,E15.7 //
* 5X,31H NUMBER OF FUNCTION EVALUATIONS,I10 //
* 5X,15H EXIT PARAMETER,16X,I10 //
* 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3E15.7)
C
C LAST CARD OF DRIVER FOR LMDIF EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,IFLAG)
INTEGER M,N,IFLAG
REAL X(N),FVEC(M)
C
C SUBROUTINE FCN FOR LMDIF EXAMPLE.
C
INTEGER I
REAL TMP1,TMP2,TMP3
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .NE. 0) GO TO 5
C
C INSERT PRINT STATEMENTS HERE WHEN NPRINT IS POSITIVE.
C
RETURN
5 CONTINUE
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be slightly different.
0 FINAL L2 NORM OF THE RESIDUALS 0.9063596E-01
0 NUMBER OF FUNCTION EVALUATIONS 21
0 EXIT PARAMETER 1
0 FINAL APPROXIMATE SOLUTION
1
0 Page
0 0.8241057E-01 0.1133037E+01 0.2343695E+01
1
0
1
0 Page
0 Documentation for MINPACK subroutine CHKDER
0 Single precision version
0 Argonne National Laboratory
0 Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
0 March 1980
0
1. Purpose.
0 The purpose of CHKDER is to check the gradients of M nonlinear
functions in N variables, evaluated at a point X, for consis-
tency with the functions themselves. The user must call CHKDER
twice, first with MODE = 1 and then with MODE = 2.
0
2. Subroutine and type statements.
0 SUBROUTINE CHKDER(M,N,X,FVEC,FJAC,LDFJAC,XP,FVECP,MODE,ERR)
INTEGER M,N,LDFJAC,MODE
REAL X(N),FVEC(M),FJAC(LDFJAC,N),XP(N),FVECP(M),ERR(M)
0
3. Parameters.
0 Parameters designated as input parameters must be specified on
entry to CHKDER and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from CHKDER.
0 M is a positive integer input variable set to the number of
functions.
0 N is a positive integer input variable set to the number of
variables.
0 X is an input array of length N.
0 FVEC is an array of length M. On input when MODE = 2, FVEC mus
contain the functions evaluated at X.
0 FJAC is an M by N array. On input when MODE = 2, the rows of
FJAC must contain the gradients of the respective functions
evaluated at X.
0 LDFJAC is a positive integer input variable not less than M
which specifies the leading dimension of the array FJAC.
0 XP is an array of length N. On output when MODE = 1, XP is set
to a neighboring point of X.
1
0 Page
0 FVECP is an array of length M. On input when MODE = 2, FVECP
must contain the functions evaluated at XP.
0 MODE is an integer input variable set to 1 on the first call an
2 on the second. Other values of MODE are equivalent to
MODE = 1.
0 ERR is an array of length M. On output when MODE = 2, ERR con-
tains measures of correctness of the respective gradients. I
there is no severe loss of significance, then if ERR(I) is 1.
the I-th gradient is correct, while if ERR(I) is 0.0 the I-th
gradient is incorrect. For values of ERR between 0.0 and 1.0
the categorization is less certain. In general, a value of
ERR(I) greater than 0.5 indicates that the I-th gradient is
probably correct, while a value of ERR(I) less than 0.5 indi-
cates that the I-th gradient is probably incorrect.
0
4. Successful completion.
0 CHKDER usually guarantees that if ERR(I) is 1.0, then the I-th
gradient at X is consistent with the I-th function. This sug-
gests that the input X be such that consistency of the gradient
at X implies consistency of the gradient at all points of inter
est. If all the components of X are distinct and the fractiona
part of each one has two nonzero digits, then X is likely to be
a satisfactory choice.
0 If ERR(I) is not 1.0 but is greater than 0.5, then the I-th gra
dient is probably consistent with the I-th function (the more s
the larger ERR(I) is), but the conditions for ERR(I) to be 1.0
have not been completely satisfied. In this case, it is recom-
mended that CHKDER be rerun with other input values of X. If
ERR(I) is always greater than 0.5, then the I-th gradient is
consistent with the I-th function.
0
5. Unsuccessful completion.
0 CHKDER does not perform reliably if cancellation or rounding
errors cause a severe loss of significance in the evaluation of
a function. Therefore, none of the components of X should be
unusually small (in particular, zero) or any other value which
may cause loss of significance. The relative differences
between corresponding elements of FVECP and FVEC should be at
least two orders of magnitude greater than the machine precisio
(as defined by the MINPACK function SPMPAR(1)). If there is a
severe loss of significance in the evaluation of the I-th func-
tion, then ERR(I) may be 0.0 and yet the I-th gradient could be
correct.
0 If ERR(I) is not 0.0 but is less than 0.5, then the I-th gra-
dient is probably not consistent with the I-th function (the
more so the smaller ERR(I) is), but the conditions for ERR(I) t
1
0 Page
0 be 0.0 have not been completely satisfied. In this case, it is
recommended that CHKDER be rerun with other input values of X.
If ERR(I) is always less than 0.5 and if there is no severe los
of significance, then the I-th gradient is not consistent with
the I-th function.
0
6. Characteristics of the algorithm.
0 CHKDER checks the I-th gradient for consistency with the I-th
function by computing a forward-difference approximation along
suitably chosen direction and comparing this approximation with
the user-supplied gradient along the same direction. The prin-
cipal characteristic of CHKDER is its invariance to changes in
scale of the variables or functions.
0 Timing. The time required by CHKDER depends only on M and N.
The number of arithmetic operations needed by CHKDER is about
N when MODE = 1 and M*N when MODE = 2.
0 Storage. CHKDER requires M*N + 3*M + 2*N single precision stor
age locations, in addition to the storage required by the pro
gram. There are no internally declared storage arrays.
0
7. Subprograms required.
0 MINPACK-supplied ... SPMPAR
0 FORTRAN-supplied ... ABS,ALOG10,SQRT
0
8. References.
0 None.
0
9. Example.
0 This example checks the Jacobian matrix for the problem that
determines the values of x(1), x(2), and x(3) which provide the
best fit (in the least squares sense) of
0 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15
0 to the data
0 y = (0.14,0.18,0.22,0.25,0.29,0.32,0.35,0.39,
0.37,0.58,0.73,0.96,1.34,2.10,4.39),
0 where u(i) = i, v(i) = 16 - i, and w(i) = min(u(i),v(i)). The
i-th component of FVEC is thus defined by
0 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))).
1
0 Page
0 C **********
C
C DRIVER FOR CHKDER EXAMPLE.
C SINGLE PRECISION VERSION
C
C **********
INTEGER I,M,N,LDFJAC,MODE,NWRITE
REAL X(3),FVEC(15),FJAC(15,3),XP(3),FVECP(15),ERR(15)
C
C LOGICAL OUTPUT UNIT IS ASSUMED TO BE NUMBER 6.
C
DATA NWRITE /6/
C
M = 15
N = 3
C
C THE FOLLOWING VALUES SHOULD BE SUITABLE FOR
C CHECKING THE JACOBIAN MATRIX.
C
X(1) = 9.2E-1
X(2) = 1.3E-1
X(3) = 5.4E-1
C
LDFJAC = 15
C
MODE = 1
CALL CHKDER(M,N,X,FVEC,FJAC,LDFJAC,XP,FVECP,MODE,ERR)
MODE = 2
CALL FCN(M,N,X,FVEC,FJAC,LDFJAC,1)
CALL FCN(M,N,X,FVEC,FJAC,LDFJAC,2)
CALL FCN(M,N,XP,FVECP,FJAC,LDFJAC,1)
CALL CHKDER(M,N,X,FVEC,FJAC,LDFJAC,XP,FVECP,MODE,ERR)
C
DO 10 I = 1, M
FVECP(I) = FVECP(I) - FVEC(I)
10 CONTINUE
WRITE (NWRITE,1000) (FVEC(I),I=1,M)
WRITE (NWRITE,2000) (FVECP(I),I=1,M)
WRITE (NWRITE,3000) (ERR(I),I=1,M)
STOP
1000 FORMAT (/5X,5H FVEC // (5X,3E15.7))
2000 FORMAT (/5X,13H FVECP - FVEC // (5X,3E15.7))
3000 FORMAT (/5X,4H ERR // (5X,3E15.7))
C
C LAST CARD OF DRIVER FOR CHKDER EXAMPLE.
C
END
SUBROUTINE FCN(M,N,X,FVEC,FJAC,LDFJAC,IFLAG)
INTEGER M,N,LDFJAC,IFLAG
REAL X(N),FVEC(M),FJAC(LDFJAC,N)
C
C SUBROUTINE FCN FOR CHKDER EXAMPLE.
C
INTEGER I
1
0 Page
0 REAL TMP1,TMP2,TMP3,TMP4
REAL Y(15)
DATA Y(1),Y(2),Y(3),Y(4),Y(5),Y(6),Y(7),Y(8),
* Y(9),Y(10),Y(11),Y(12),Y(13),Y(14),Y(15)
* /1.4E-1,1.8E-1,2.2E-1,2.5E-1,2.9E-1,3.2E-1,3.5E-1,3.9E-1,
* 3.7E-1,5.8E-1,7.3E-1,9.6E-1,1.34E0,2.1E0,4.39E0/
C
IF (IFLAG .EQ. 2) GO TO 20
DO 10 I = 1, 15
TMP1 = I
TMP2 = 16 - I
TMP3 = TMP1
IF (I .GT. 8) TMP3 = TMP2
FVEC(I) = Y(I) - (X(1) + TMP1/(X(2)*TMP2 + X(3)*TMP3))
10 CONTINUE
GO TO 40
20 CONTINUE
DO 30 I = 1, 15
TMP1 = I
TMP2 = 16 - I
C
C ERROR INTRODUCED INTO NEXT STATEMENT FOR ILLUSTRATION.
C CORRECTED STATEMENT SHOULD READ TMP3 = TMP1 .
C
TMP3 = TMP2
IF (I .GT. 8) TMP3 = TMP2
TMP4 = (X(2)*TMP2 + X(3)*TMP3)**2
FJAC(I,1) = -1.E0
FJAC(I,2) = TMP1*TMP2/TMP4
FJAC(I,3) = TMP1*TMP3/TMP4
30 CONTINUE
40 CONTINUE
RETURN
C
C LAST CARD OF SUBROUTINE FCN.
C
END
0 Results obtained with different compilers or machines
may be different. In particular, the differences
FVECP - FVEC are machine dependent.
0 FVEC
0 -0.1181606E+01 -0.1429655E+01 -0.1606344E+01
-0.1745269E+01 -0.1840654E+01 -0.1921586E+01
-0.1984141E+01 -0.2022537E+01 -0.2468977E+01
-0.2827562E+01 -0.3473582E+01 -0.4437612E+01
-0.6047662E+01 -0.9267761E+01 -0.1891806E+02
0 FVECP - FVEC
0 -0.7724666E-08 -0.3432405E-08 -0.2034843E-09
0.2313685E-08 0.4331078E-08 0.5984096E-08
1
0 Page
0 0.7363281E-08 0.8531470E-08 0.1488591E-07
0.2335850E-07 0.3522012E-07 0.5301255E-07
0.8266660E-07 0.1419747E-06 0.3198990E-06
0 ERR
0 0.1141397E+00 0.9943516E-01 0.9674474E-01
0.9980447E-01 0.1073116E+00 0.1220445E+00
0.1526814E+00 0.1000000E+01 0.1000000E+01
0.1000000E+01 0.1000000E+01 0.1000000E+01
0.1000000E+01 0.1000000E+01 0.1000000E+01