On M-step Memory Gradient Method with Wolfe's Line Search

摘要

The paper presents an m-step memory gradient method for unconstrained optimization problem, and proves its global convergence under Wolfe's line searches. The linear convergence rate is investigated when the objective function is uniformly convex. Numerical experiments show that the new algorithm is very effective in practice.