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A CLASS OF NONMONOTONE SPECTRAL MEMORY GRADIENT METHOD
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초록
In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.
Abstract
In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.
참고문헌 (12)
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- E. G. Birgin, "Spectral projected gradient methods, Encyclopedia of Optimization"
- E. E. Cragg, "Study on a supermemory gradient method for the minimization of functions", J. Optimization Theory Appl., 4, pp.191~205 (1969)
- L. Grippo, "A nonmonotone line search technique for Newton’s method", SIAM J. Numer. Anal., 23(4), pp.707~716 (1986)
- W. Hock, "Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, 187" (1981), Springer-Verlag
- A. Miele, "Study on a memory gradient method for the minimization of functions", J. Optimization Theory Appl., 3, pp.459~470 (1969)
- Y. Narushima, "Global convergence of a memory gradient method for unconstrained optimization", Comput. Optim. Appl., 35(3), pp.325~346 (2006)
- M. Raydan, "The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem", SIAM J. Optim., 7(1), pp.26~33 (1997)
- K. Schittkowski, "More Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, 282" (1987), Springer-Verlag
- P. L. Toint, "Non-monotone trust-region algorithms for nonlinear optimization subject to convex constraints", Math. Programming, 77(1), pp.69~94 (1997)
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