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The memory gradient method is used for unconstrained optimization, especially large scale problems. In this paper, we develop a nonmonotone memory gradient method for unconstrained optimization, where a class of memory gradient direction is combined efficiently. The global and Rlinear convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show...
Conjugate gradient methods are well known and popular in unconstrained optimization. Numerous studies and modifications have been devoted by researchers to improve this method. In this paper, we introduced a new conjugate gradient coefficient (βk) and tested its performance using exact line search. Numerical results based on number of iterations have shown our new βk performance is better or equivalent...
Conjugate gradient methods are important for large-scale unconstrained optimization. In this paper, we propose anew formula ??k for unconstrained optimization, which is the hybrid from HS method, LS method and CD method. From the construction of the new formula ??k, we use a direction which is different from traditional dk. The direction satisfies descent conditions naturally. And dkTgk=-??gk??2...
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