Global convergence of nonmonotone descent methods for unconstrained optimization problems

摘要

Global convergence results are established for unconstrained optimization algorithms that utilize a nonmonotone line search procedure. This procedure allows the user to specify a flexible forcing function and includes the nonmonotone Armijo rule, the nonmonotone Goldstein rule, and the nonmonotone Wolfe rule as special cases.