Fortran程序标准化，有偿，RMB5000 - Fortran Coder

The Levenberg-Marquardt and trust-region-reflective methods are based on the nonlinear least-squares algorithms also used in lsqnonlin. Use one of these methods if the system may not have a zero. The algorithm still returns a point where the residual is small. However, if the Jacobian of the system is singular, the algorithm might converge to a point that is not a solution of the system of equations (see Limitations).

By default fsolve chooses the trust-region dogleg algorithm. The algorithm is a variant of the Powell dogleg method described in [8]. It is similar in nature to the algorithm implemented in [7]. See Trust-Region Dogleg Method.
The trust-region-reflective algorithm is a subspace trust-region method and is based on the interior-reflective Newton method described in [1] and [2]. Each iteration involves the approximate solution of a large linear system using the method of preconditioned conjugate gradients (PCG). See Trust-Region Reflective fsolve Algorithm.
The Levenberg-Marquardt method is described in references [4], [5], and [6]. See Levenberg-Marquardt Method.
Optimization Problem Setup
Equation Solving Algorithms
References

[1] Coleman, T.F. and Y. Li, "An Interior, Trust Region Approach for Nonlinear Minimization Subject to Bounds," SIAM Journal on Optimization, Vol. 6, pp. 418-445, 1996.

[2] Coleman, T.F. and Y. Li, "On the Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Bounds," Mathematical Programming, Vol. 67, Number 2, pp. 189-224, 1994.

[3] Dennis, J. E. Jr., "Nonlinear Least-Squares," State of the Art in Numerical Analysis, ed. D. Jacobs, Academic Press, pp. 269-312.

[4] Levenberg, K., "A Method for the Solution of Certain Problems in Least-Squares," Quarterly Applied Mathematics 2, pp. 164-168, 1944.

[5] Marquardt, D., "An Algorithm for Least-squares Estimation of Nonlinear Parameters," SIAM Journal Applied Mathematics, Vol. 11, pp. 431-441, 1963.

[6] Moré, J. J., "The Levenberg-Marquardt Algorithm: Implementation and Theory," Numerical Analysis, ed. G. A. Watson, Lecture Notes in Mathematics 630, Springer Verlag, pp. 105-116, 1977.

[7] Moré, J. J., B. S. Garbow, and K. E. Hillstrom, User Guide for MINPACK 1, Argonne National Laboratory, Rept. ANL-80-74, 1980.

[8] Powell, M. J. D., "A Fortran Subroutine for Solving Systems of Nonlinear Algebraic Equations," Numerical Methods for Nonlinear Algebraic Equations, P. Rabinowitz, ed., Ch.7, 1970.

See Also

fzero | lsqcurvefit | lsqnonlin | optimoptions

36.9 References and Further Reading

The original version of the Hybrid method is described in the following articles by Powell,

M.J.D. Powell, “A Hybrid Method for Nonlinear Equations” (Chap 6, p 87–114) and “A Fortran Subroutine for Solving systems of Nonlinear Algebraic Equations” (Chap 7, p 115–161), in Numerical Methods for Nonlinear Algebraic Equations, P. Rabinowitz, editor. Gordon and Breach, 1970.
The following papers are also relevant to the algorithms described in this section,

J.J. Moré, M.Y. Cosnard, “Numerical Solution of Nonlinear Equations”, ACM Transactions on Mathematical Software, Vol 5, No 1, (1979), p 64–85
C.G. Broyden, “A Class of Methods for Solving Nonlinear Simultaneous Equations”, Mathematics of Computation, Vol 19 (1965), p 577–593
J.J. Moré, B.S. Garbow, K.E. Hillstrom, “Testing Unconstrained Optimization Software”, ACM Transactions on Mathematical Software, Vol 7, No 1 (1981), p 17–41