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Page 1 Documentation for MINPACK subroutine LMSTR1 Double precision version Argonne National Laboratory Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More March 1980 1. Purpose. The purpose of LMSTR1 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. 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. 2. Subroutine and type statements. SUBROUTINE LMSTR1(FCN,M,N,X,FVEC,FJAC,LDFJAC,TOL, * INFO,IPVT,WA,LWA) INTEGER M,N,LDFJAC,INFO,LWA INTEGER IPVT(N) DOUBLE PRECISION TOL DOUBLE PRECISION X(N),FVEC(M),FJAC(LDFJAC,N),WA(LWA) EXTERNAL FCN 3. Parameters. 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. FCN is the name of the user-supplied subroutine which calculates 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. SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG) INTEGER M,N,IFLAG DOUBLE PRECISION 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 Page 2 END The value of IFLAG should not be changed by FCN unless the user wants to terminate execution of LMSTR1. In this case set IFLAG to a negative integer. M is a positive integer input variable set to the number of functions. N is a positive integer input variable set to the number of variables. N must not exceed M. 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. FVEC is an output array of length M which contains the functions evaluated at the output X. FJAC is an output N by N array. The upper triangle of FJAC con- tains an upper triangular matrix R such that T T T P *(JAC *JAC)*P = R *R, 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. LDFJAC is a positive integer input variable not less than N which specifies the leading dimension of the array FJAC. 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 contains more details about TOL. 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. INFO = 0 Improper input parameters. INFO = 1 Algorithm estimates that the relative error in the sum of squares is at most TOL. INFO = 2 Algorithm estimates that the relative error between X and the solution is at most TOL. INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold. INFO = 4 FVEC is orthogonal to the columns of the Jacobian to Page 3 machine precision. INFO = 5 Number of calls to FCN with IFLAG = 1 has reached 100*(N+1). INFO = 6 TOL is too small. No further reduction in the sum of squares is possible. INFO = 7 TOL is too small. No further improvement in the approximate solution X is possible. Sections 4 and 5 contain more details about INFO. 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 R is upper triangular. Column j of P is column IPVT(j) of the identity matrix. WA is a work array of length LWA. LWA is a positive integer input variable not less than 5*N+M. 4. Successful completion. The accuracy of LMSTR1 is controlled by the convergence parame- ter TOL. This parameter is used in tests which make three types of comparisons between the approximation X and a solution XSOL. LMSTR1 terminates when any of the tests is satisfied. If TOL is less than the machine precision (as defined by the MINPACK func- tion DPMPAR(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. 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 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 LMSTR1 with a tighter toler- ance. First convergence test. If ENORM(Z) denotes the Euclidean norm of a vector Z, then this test attempts to guarantee that ENORM(FVEC) .LE. (1+TOL)*ENORM(FVECS), 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 and Page 4 INFO is set to 1 (or to 3 if the second test is also satis- fied). 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 ENORM(D*(X-XSOL)) .LE. TOL*ENORM(D*XSOL). If this condition is satisfied with TOL = 10**(-K), then the larger components of D*X have K significant decimal digits and 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 to their sensitivity. 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 and saddle points. Therefore, termination caused by this test (INFO = 4) should be examined carefully. 5. Unsuccessful completion. Unsuccessful termination of LMSTR1 can be due to improper input parameters, arithmetic interrupts, or an excessive number of function evaluations. Improper input parameters. INFO is set to 0 if N .LE. 0, or M .LT. N, or LDFJAC .LT. N, or TOL .LT. 0.D0, or LWA .LT. 5*N+M. 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. 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. Page 5 6. Characteristics of the algorithm. 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 the solution and a fast rate of convergence for problems with small residuals. 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. Storage. LMSTR1 requires N**2 + 2*M + 6*N double precision sto- rage locations and N integer storage locations, in addition to the storage required by the program. There are no internally declared storage arrays. 7. Subprograms required. USER-supplied ...... FCN MINPACK-supplied ... DPMPAR,ENORM,LMSTR,LMPAR,QRFAC,QRSOLV, RWUPDT FORTRAN-supplied ... DABS,DMAX1,DMIN1,DSQRT,MOD 8. References. Jorge J. More, The Levenberg-Marquardt Algorithm, Implementation and Theory. Numerical Analysis, G. A. Watson, editor. Lecture Notes in Mathematics 630, Springer-Verlag, 1977. 9. Example. 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 x(1) + u(i)/(v(i)*x(2) + w(i)*x(3)), i = 1, 15 Page 6 to the data 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), 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 y(i) - (x(1) + u(i)/(v(i)*x(2) + w(i)*x(3))). C ********** C C DRIVER FOR LMSTR1 EXAMPLE. C DOUBLE PRECISION VERSION C C ********** INTEGER J,M,N,LDFJAC,INFO,LWA,NWRITE INTEGER IPVT(3) DOUBLE PRECISION TOL,FNORM DOUBLE PRECISION X(3),FVEC(15),FJAC(3,3),WA(30) DOUBLE PRECISION ENORM,DPMPAR 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.D0 X(2) = 1.D0 X(3) = 1.D0 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 = DSQRT(DPMPAR(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,D15.7 // * 5X,15H EXIT PARAMETER,16X,I10 // * 5X,27H FINAL APPROXIMATE SOLUTION // 5X,3D15.7) C Page 7 C LAST CARD OF DRIVER FOR LMSTR1 EXAMPLE. C END SUBROUTINE FCN(M,N,X,FVEC,FJROW,IFLAG) INTEGER M,N,IFLAG DOUBLE PRECISION X(N),FVEC(M),FJROW(N) C C SUBROUTINE FCN FOR LMSTR1 EXAMPLE. C INTEGER I DOUBLE PRECISION TMP1,TMP2,TMP3,TMP4 DOUBLE PRECISION 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) * /1.4D-1,1.8D-1,2.2D-1,2.5D-1,2.9D-1,3.2D-1,3.5D-1,3.9D-1, * 3.7D-1,5.8D-1,7.3D-1,9.6D-1,1.34D0,2.1D0,4.39D0/ 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.D0 FJROW(2) = TMP1*TMP2/TMP4 FJROW(3) = TMP1*TMP3/TMP4 30 CONTINUE 40 CONTINUE RETURN C C LAST CARD OF SUBROUTINE FCN. C END Results obtained with different compilers or machines may be slightly different. FINAL L2 NORM OF THE RESIDUALS 0.9063596D-01 EXIT PARAMETER 1 FINAL APPROXIMATE SOLUTION 0.8241058D-01 0.1133037D+01 0.2343695D+01