A New Spectral Conjugate Gradient Algorithm for Unconstrained Optimization | Zendy

Yoksal A. Laylani et al. Yoksal A. Laylani et al. | Zendy

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A New Spectral Conjugate Gradient Algorithm for Unconstrained Optimization

Author(s) -

Yoksal A. Laylani et al. Yoksal A. Laylani et al.

Publication year - 2017

Publication title -

international journal of mathematics and computer applications research

Language(s) - English

Resource type - Journals

eISSN - 2249-6955

pISSN - 2249-8060

DOI - 10.24247/ijmcarfeb20181

Subject(s) - conjugate gradient method , conjugate , algorithm , nonlinear conjugate gradient method , computer science , gradient method , derivation of the conjugate gradient method , mathematics , mathematical optimization , gradient descent , artificial intelligence , mathematical analysis , artificial neural network

Conjugate gradient methods represent an important class of unconstrained optimization algorithms with strong local and global convergence properties and modest memory requirements. An excellent survey of the development of different versions of nonlinear conjugate-gradient methods, with special attention to global convergence properties, is presented by Hager and Zhang [8]. This family of algorithms includes a lot of variants, well known in the literature, with important convergence properties and numerical efficiency. The purpose of this chapter is to present these algorithms as well as their performances to solve a large variety of large-scale unconstrained optimization problems. For solving the nonlinear unconstrained optimization problem

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