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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.
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참고문헌 (12)
참고문헌 신청
A. Miele / 1969 / Study on a memory gradient method for the minimization of functions / J. Optimization Theory Appl. 3 : 459 ~ 470
E. E. Cragg / 1969 / Study on a supermemory gradient method for the minimization of functions / J. Optimization Theory Appl. 4 : 191 ~ 205
E. G. Birgin / / Spectral projected gradient methods, Encyclopedia of Optimization
E. G. Birgin / 2000 / Nonmonotone spectral projected gradient methods on convex sets / SIAM J. Optim. 10 (4) : 1196 ~ 1211
E. G. Birgin / 2001 / A spectral conjugate gradient method for unconstrained optimization / Appl. Math. Optim. 43 (2) : 117 ~ 128
E. E. Cragg / 1969 / Study on a supermemory gradient method for the minimization of functions / J. Optimization Theory Appl. 4 : 191 ~ 205
E. G. Birgin / / Spectral projected gradient methods, Encyclopedia of Optimization
E. G. Birgin / 2000 / Nonmonotone spectral projected gradient methods on convex sets / SIAM J. Optim. 10 (4) : 1196 ~ 1211
E. G. Birgin / 2001 / A spectral conjugate gradient method for unconstrained optimization / Appl. Math. Optim. 43 (2) : 117 ~ 128
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