论文题目:A Nonlinear Conjugate Gradient Algorithm with An Optimal Property and An Improved Wolfe Line Search
作 者:Yu-Hong Dai and Cai-Xia Kou
论文摘要: In this paper, we seek the conjugate gradient direction closest to the direction of the
scaled memoryless BFGS method and propose a family of conjugate gradient methods for
unconstrained optimization. An improved Wolfe line search is also proposed, which can
avoid a numerical drawback of the Wolfe line search and guarantee the global convergence
of the conjugate gradient method under mild conditions. To accelerate the algorithm, we
introduce adaptive restarts along negative gradients based on how the function is close to
some quadratic function during some previous iterations. Numerical experiments with the
CUTEr collection show that the proposed algorithm is promising.
所属实验室或研究中心:优化与应用研究中心
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