BCLSJ
Solves a nonlinear least squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm and a user-supplied Jacobian.
Required Arguments
FCN — User-supplied subroutine to evaluate the function to be minimized. The usage is CALL FCN (M, N, X, F), where
M – Length of F. (Input)
N – Length of X. (Input)
X – The point at which the function is evaluated. (Input)
X should not be changed by FCN.
F – The computed function at the point 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 – The point at which the function is evaluated. (Input)
X should not be changed by FCN.
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)
IBTYPE — Scalar indicating the types of bounds on variables. (Input)
|
IBTYPE |
Action |
|
0 |
User will supply all the bounds. |
|
1 |
All variables are nonnegative. |
|
2 |
All variables are nonpositive. |
|
3 |
User supplies only the bounds on 1st variable, all other variables will have the same bounds. |
XLB — Vector of length N containing the lower bounds on variables. (Input, if IBTYPE = 0; output, if IBTYPE = 1 or 2; input/output, if IBTYPE = 3)
XUB — Vector of length N containing the upper bounds on variables. (Input, if IBTYPE = 0; output, if IBTYPE = 1 or 2; input/output, if IBTYPE = 3)
X — Vector of length N containing the approximate solution. (Output)
Optional Arguments
N — Number of variables. (Input)
N must be less than or equal to M.
Default: N = SIZE (X,1).
XGUESS — Vector of length N containing the initial guess. (Input)
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.
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.
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.
RPARAM — Parameter vector of length 7. (Input/Output)
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).
FORTRAN 90 Interface
Generic: CALL BCLSJ (FCN, JAC, M, IBTYPE, XLB, XUB, X [, …])
Specific: The specific interface names are S_BCLSJ and D_BCLSJ.
FORTRAN 77 Interface
Single: CALL BCLSJ (FCN, JAC, M, N, XGUESS, IBTYPE, XLB, XUB, XSCALE, FSCALE, IPARAM, RPARAM, X, FVEC, FJAC, LDFJAC)
Double: The double precision name is DBCLSJ.
Description
The routine BCLSJ uses a modified Levenberg-Marquardt method and an active set strategy to solve nonlinear least squares problems subject to simple bounds on the variables. The problem is stated as follows:
subject to l ≤ x ≤ u
where m ≥ n, F : Rn→Rm, and fi(x) is the i-th component function of F(x). From a given starting point, an active set IA, which contains the indices of the variables at their bounds, is built. A variable is called a “free variable” if it is not in the active set. The routine then computes the search direction for the free variables according to the formula
d = -(JT J + μI)-1 JT F
where μ is the Levenberg-Marquardt parameter, F = F (x), and J is the Jacobian with respect to the free variables. The search direction for the variables in IA is set to zero. The trust region approach discussed by Dennis and Schnabel (1983) is used to find the new point. Finally, the optimality conditions are checked. The conditions are
∥g(xi)∥ ≤ ɛ, li < xi< ui
g(xi) < 0, xi = ui
g(xi) > 0, xi = li
where ɛ is a gradient tolerance. This process is repeated until the optimality criterion is achieved.
The active set is changed only when a free variable hits its bounds during an iteration or the optimality condition is met for the free variables but not for all variables in IA, the active set. In the latter case, a variable that violates the optimality condition will be dropped out of IA. For more detail on the Levenberg-Marquardt method, see Levenberg (1944) or Marquardt (1963). For more detailed information on active set strategy, see Gill and Murray (1976).
Comments
1. Workspace may be explicitly provided, if desired, by use of B2LSJ/DB2LSJ. The reference is:
CALL B2LSJ (FCN, JAC, M, N, XGUESS, IBTYPE, XLB, XUB, XSCALE, FSCALE, IPARAM, RPARAM, X, FVEC, FJAC, LDFJAC, WK, IWK)
The additional arguments are as follows:
WK — Work vector of length 11 * 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 2 * 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. |
|
3 |
6 |
Five consecutive steps have been taken with the maximum step length. |
|
4 |
5 |
Maximum number of Jacobian evaluations exceeded. |
|
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 BCLSJ occurs when the norm of the function is less than the absolute function tolerance. The second stopping criterion occurs when the norm of the scaled gradient is less than the given gradient tolerance. The third stopping criterion for BCLSJ occurs when the scaled distance between the last two steps is less than the step tolerance.
4. If the default parameters are desired for BCLSJ, then set IPARAM(1) to zero and call the routine BCLSJ. Otherwise, if any nondefault parameters are desired for IPARAM or RPARAM, then the following steps should be taken before calling BCLSJ:
CALL U4LSF (IPARAM, RPARAM)
Set nondefault values for desired IPARAM, RPARAM elements.
Note: The call to U4INF 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.
IPARAM(3) = Maximum number of iterations.
Default: 100.
IPARAM(4) = Maximum number of function evaluations.
Default: 400.
IPARAM(5) = Maximum number of Jacobian evaluations.
Default: 100.
IPARAM(6) = Internal variable scaling flag.
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
where
J(x) is the Jacobian, s = XSCALE, and fs = FSCALE.
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
where s = XSCALE.
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.
RPARAM(4) = Absolute function tolerance.
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.
RPARAM(6) = Maximum allowable step SIZE.
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.
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
subject to ‑2 ≤ x1 ≤ 0.5
–1 ≤ x2 ≤ 2
where
is solved with an initial guess (‑1.2, 1.0) and default values for parameters.
USE BCLSJ_INT
USE UMACH_INT
IMPLICIT NONE
! Declaration of variables
INTEGER LDFJAC, M, N
PARAMETER (LDFJAC=2, M=2, N=2)
!
INTEGER IPARAM(7), ITP, NOUT
REAL FVEC(M), RPARAM(7), X(N), XGUESS(N), XLB(N), XUB(N)
EXTERNAL ROSBCK, ROSJAC
! Compute the least squares for the
! Rosenbrock function.
DATA XGUESS/-1.2E0, 1.0E0/
DATA XLB/-2.0E0, -1.0E0/, XUB/0.5E0, 2.0E0/
! All the bounds are provided
ITP = 0
! Default parameters are used
IPARAM(1) = 0
!
CALL BCLSJ (ROSBCK,ROSJAC,M,ITP,XLB,XUB,X,XGUESS=XGUESS, &
IPARAM=IPARAM, FVEC=FVEC)
! Print results
CALL UMACH (2, NOUT)
WRITE (NOUT,99999) X, FVEC, IPARAM(3), IPARAM(4)
!
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, /)
END
!
SUBROUTINE ROSBCK (M, N, X, F)
INTEGER M, N
REAL X(N), F(M)
!
F(1) = 1.0E1*(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
The solution is 0.5000 0.2500
The function evaluated at the solution is
0.0000 0.5000
The number of iterations is 13
The number of function evaluations is 21
