Nonmonotone Quasi–Newton-based conjugate gradient methods with application to signal processing

In order to increase the probability of applying more recent information, a forgetting factor is embedded in the nonmonotone line search technique for minimization of the multiobjective problem concerning the partial order induced by a closed, convex, and pointed cone. The method is shown to be globally convergent without convexity assumption on the objective function. Moreover, to improve behavior of the classical steepest descent method, an accelerated scheme is presented. Ultimately, computational advantages of the algorithms are depicted on a class of standard test problems.

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Nonmonotone Quasi–Newton-based conjugate gradient methods with application to signal processing

Cited by 2 publications

References 58 publications

A projected hybridization of the Hestenes–Stiefel and Dai–Yuan conjugate gradient methods with application to nonnegative matrix factorization

A projected hybridization of the Hestenes–Stiefel and Dai–Yuan conjugate gradient methods with application to nonnegative matrix factorization

Accelerated nonmonotone line search technique for multiobjective optimization

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