UNLSJ
Solves a nonlinear least squares problem using a modified Levenberg-Marquardt algorithm and a user-supplied Jacobian.
Required Arguments
FCN — User-supplied subroutine to evaluate the function which defines the least-squares problem. The usage is
CALL FCN (M, N, X, F), where
CALL FCN (M, N, X, F), where
M – Length of F. (Input)
N – Length of X. (Input)
X – Vector of length N at which point the function is evaluated. (Input)
X should not be changed by FCN.
X should not be changed by FCN.
F – Vector of length M containing the function values at X. (Output)
FCN must be declared EXTERNAL in the calling program.
JAC — User-supplied subroutine to evaluate the Jacobian at a point X. The usage is CALL JAC (M, N, X, FJAC, LDFJAC), where
M – Length of F. (Input)
N – Length of X. (Input)
X – Vector of length N at which point the Jacobian is evaluated. (Input)
X should not be changed by JAC.
X should not be changed by JAC.
FJAC – The computed M by N Jacobian at the point X. (Output)
LDFJAC – Leading dimension of FJAC. (Input)
JAC must be declared EXTERNAL in the calling program.
M — Number of functions. (Input)
X — Vector of length N containing the approximate solution. (Output)
Optional Arguments
N — Number of variables. N must be less than or equal to M. (Input)
Default: N = SIZE (X,1).
Default: N = SIZE (X,1).
XGUESS — Vector of length N containing the initial guess. (Input)
Default: XGUESS = 0.0.
Default: XGUESS = 0.0.
XSCALE — Vector of length N containing the diagonal scaling matrix for the variables. (Input)
XSCALE is used mainly in scaling the gradient and the distance between two points. By default, the values for XSCALE are set internally. See IPARAM(6) in Comment 4.
Default: XSCALE = 1.0.
XSCALE is used mainly in scaling the gradient and the distance between two points. By default, the values for XSCALE are set internally. See IPARAM(6) in Comment 4.
Default: XSCALE = 1.0.
FSCALE — Vector of length M containing the diagonal scaling matrix for the functions. (Input)
FSCALE is used mainly in scaling the gradient. In the absence of other information, set all entries to 1.0.
Default: FSCALE = 1.0.
FSCALE is used mainly in scaling the gradient. In the absence of other information, set all entries to 1.0.
Default: FSCALE = 1.0.
IPARAM — Parameter vector of length 6. (Input/Output)
Set IPARAM(1) to zero for default values of IPARAM and RPARAM. See Comment 4.
Default: IPARAM = 0.
Set IPARAM(1) to zero for default values of IPARAM and RPARAM. See Comment 4.
Default: IPARAM = 0.
RPARAM — Parameter vector of length 7. (Input/Output)
See Comment 4.
See Comment 4.
FVEC — Vector of length M containing the residuals at the approximate solution. (Output)
FJAC — M by N matrix containing a finite-difference approximate Jacobian at the approximate solution. (Output)
LDFJAC — Leading dimension of FJAC exactly as specified in the dimension statement of the calling program. (Input)
Default: LDFJAC = SIZE (FJAC,1).
Default: LDFJAC = SIZE (FJAC,1).
FORTRAN 90 Interface
Generic: CALL UNLSJ (FCN, JAC, M, X [, …])
Specific: The specific interface names are S_UNLSJ and D_UNLSJ.
FORTRAN 77 Interface
Single: CALL UNLSJ (FCN, JAC, M, N, XGUESS, XSCALE, FSCALE, IPARAM, RPARAM, X, FVEC, FJAC, LDFJAC)
Double: The double precision name is DUNLSJ.
Description
The routine UNLSJ is based on the MINPACK routine LMDER by Moré et al. (1980). It uses a modified Levenberg-Marquardt method to solve nonlinear least squares problems. The problem is stated as follows:
where m ≥ n, F : Rn→Rm, and fi(x) is the i-th component function of F(x). From a current point, the algorithm uses the trust region approach:
subject to ∥xn ‑ xc∥2 ≤ δc
to get a new point xn, which is computed as
where
and 
otherwise. and are the function values and the Jacobian evaluated at the current point . This procedure is repeated until the stopping criteria are satisfied. For more details, see Levenberg (1944), Marquardt(1963), or Dennis and Schnabel (1983, Chapter 10).
otherwise. and are the function values and the Jacobian evaluated at the current point . This procedure is repeated until the stopping criteria are satisfied. For more details, see Levenberg (1944), Marquardt(1963), or Dennis and Schnabel (1983, Chapter 10).Comments
1. Workspace may be explicitly provided, if desired, by use of U2LSJ/DU2LSJ. The reference is:
CALL U2LSJ (FCN, JAC, M, N, XGUESS, XSCALE, FSCALE, IPARAM, RPARAM, X, FVEC, FJAC, LDFJAC, WK, IWK)
The additional arguments are as follows:
WK — Work vector of length 9 * N + 3 * M ‑ 1. WK contains the following information on output: The second N locations contain the last step taken. The third N locations contain the last Gauss-Newton step. The fourth N locations contain an estimate of the gradient at the solution.
IWK — Work vector of length N containing the permutations used in the QR factorization of the Jacobian at the solution.
2. Informational errors
Type | Code | Description |
|---|---|---|
3 | 1 | Both the actual and predicted relative reductions in the function are less than or equal to the relative function convergence tolerance. |
3 | 2 | The iterates appear to be converging to a noncritical point. |
4 | 3 | Maximum number of iterations exceeded. |
4 | 4 | Maximum number of function evaluations exceeded. |
4 | 5 | Maximum number of Jacobian evaluations exceeded. |
3 | 6 | Five consecutive steps have been taken with the maximum step length. |
2 | 7 | Scaled step tolerance satisfied; the current point may be an approximate local solution, or the algorithm is making very slow progress and is not near a solution, or STEPTL is too big. |
3. The first stopping criterion for UNLSJ occurs when the norm of the function is less than the absolute function tolerance (RPARAM(4)). The second stopping criterion occurs when the norm of the scaled gradient is less than the given gradient tolerance (RPARAM(1)). The third stopping criterion for UNLSJ occurs when the scaled distance between the last two steps is less than the step tolerance (RPARAM(2)).
4. If the default parameters are desired for UNLSJ, then set IPARAM(1) to zero and call the routine UNLSJ. Otherwise, if any nondefault parameters are desired for IPARAM or RPARAM, then the following steps should be taken before calling UNLSJ:
CALL U4LSF (IPARAM, RPARAM)
Set nondefault values for desired IPARAM, RPARAM elements.
Note that the call to U4LSF will set IPARAM and RPARAM to their default values, so only nondefault values need to be set above. |
The following is a list of the parameters and the default values:
IPARAM — Integer vector of length 6.
IPARAM(1) = Initialization flag.
IPARAM(2) = Number of good digits in the function.
Default: Machine dependent.
Default: Machine dependent.
IPARAM(3) = Maximum number of iterations.
Default: 100.
Default: 100.
IPARAM(4) = Maximum number of function evaluations.
Default: 400.
Default: 400.
IPARAM(5) = Maximum number of Jacobian evaluations.
Default: 100.
Default: 100.
IPARAM(6) = Internal variable scaling flag.
If IPARAM(6) = 1, then the values for XSCALE are set internally.
Default: 1.
If IPARAM(6) = 1, then the values for XSCALE are set internally.
Default: 1.
RPARAM — Real vector of length 7.
RPARAM(1) = Scaled gradient tolerance.
The i-th component of the scaled gradient at x is calculated as
The i-th component of the scaled gradient at x is calculated as
where
J(x) is the Jacobian, s = XSCALE, and fs = FSCALE.
Default:
Default:
in double where ɛ is the machine precision.
RPARAM(2) = Scaled step tolerance. (STEPTL)
The i-th component of the scaled step between two points x and y is computed as
The i-th component of the scaled step between two points x and y is computed as
where s = XSCALE.
Default: ɛ2/3 where ɛ is the machine precision.
Default: ɛ2/3 where ɛ is the machine precision.
RPARAM(3) = Relative function tolerance.
Default: max(10-10, ɛ2/3), max (10-20, ɛ2/3) in double where ɛ is the machine precision.
Default: max(10-10, ɛ2/3), max (10-20, ɛ2/3) in double where ɛ is the machine precision.
RPARAM(4) = Absolute function tolerance.
Default: max (10-20, ɛ2), max(10-40, ɛ2) in double where ɛ is the machine precision.
Default: max (10-20, ɛ2), max(10-40, ɛ2) in double where ɛ is the machine precision.
RPARAM(5) = False convergence tolerance.
Default: 100ɛ where ɛ is the machine precision.
Default: 100ɛ where ɛ is the machine precision.
RPARAM(6) = Maximum allowable step size.
Default: 1000 max(ɛ1, ɛ2) where
Default: 1000 max(ɛ1, ɛ2) where
ɛ2 = ∥s∥2, s = XSCALE, and t = XGUESS.
RPARAM(7) = Size of initial trust region radius.
Default: based on the initial scaled Cauchy step.
Default: based on the initial scaled Cauchy step.
If double precision is desired, then DU4LSF is called and RPARAM is declared double precision.
5. Users wishing to override the default print/stop attributes associated with error messages issued by this routine are referred to “Error Handling” in the Introduction.
Example
The nonlinear least-squares problem
where
is solved; default values for parameters are used.
USE UNLSJ_INT
USE UMACH_INT
IMPLICIT NONE
! Declaration of variables
INTEGER LDFJAC, M, N
PARAMETER (LDFJAC=2, M=2, N=2)
!
INTEGER IPARAM(6), NOUT
REAL FVEC(M), X(N), XGUESS(N)
EXTERNAL ROSBCK, ROSJAC
! Compute the least squares for the
! Rosenbrock function.
DATA XGUESS/-1.2E0, 1.0E0/
IPARAM(1) = 0
!
CALL UNLSJ (ROSBCK, ROSJAC, M, X, XGUESS=XGUESS, &
IPARAM=IPARAM, FVEC=FVEC)
! Print results
CALL UMACH (2, NOUT)
WRITE (NOUT,99999) X, FVEC, IPARAM(3), IPARAM(4), IPARAM(5)
!
99999 FORMAT (' The solution is ', 2F9.4, //, ' The function ', &
'evaluated at the solution is ', /, 18X, 2F9.4, //, &
' The number of iterations is ', 10X, I3, /, ' The ', &
'number of function evaluations is ', I3, /, ' The ', &
'number of Jacobian evaluations is ', I3, /)
END
!
SUBROUTINE ROSBCK (M, N, X, F)
INTEGER M, N
REAL X(N), F(M)
!
F(1) = 10.0E0*(X(2)-X(1)*X(1))
F(2) = 1.0E0 - X(1)
RETURN
END
!
SUBROUTINE ROSJAC (M, N, X, FJAC, LDFJAC)
INTEGER M, N, LDFJAC
REAL X(N), FJAC(LDFJAC,N)
!
FJAC(1,1) = -20.0E0*X(1)
FJAC(2,1) = -1.0E0
FJAC(1,2) = 10.0E0
FJAC(2,2) = 0.0E0
RETURN
END
Output
The solution is 1.0000 1.0000
The function evaluated at the solution is
0.0000 0.0000
The number of iterations is 23
The number of function evaluations is 32
The number of Jacobian evaluations is 24
Published date: 03/19/2020
Last modified date: 03/19/2020
