A Conjugate Gradient Type Method for the Nonnegative Constraints Optimization Problems | Zendy

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A Conjugate Gradient Type Method for the Nonnegative Constraints Optimization Problems

Author(s) -

Can Li

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/986317

Subject(s) - conjugate gradient method , mathematics , nonlinear conjugate gradient method , mathematical optimization , gradient method , conjugate residual method , gradient descent , derivation of the conjugate gradient method , optimization problem , current (fluid) , point (geometry) , type (biology) , proximal gradient methods , constrained optimization , computer science , artificial neural network , artificial intelligence , geometry , electrical engineering , engineering , ecology , biology

We are concerned with the nonnegative constraints optimization problems. It is well known that the conjugate gradient methods are efficient methods for solving large-scale unconstrained optimization problems due to their simplicity and low storage. Combining the modified Polak-Ribière-Polyak method proposed by Zhang, Zhou, and Li with the Zoutendijk feasible direction method, we proposed a conjugate gradient type method for solving the nonnegative constraints optimization problems. If the current iteration is a feasible point, the direction generated by the proposed method is always a feasible descent direction at the current iteration. Under appropriate conditions, we show that the proposed method is globally convergent. We also present some numerical results to show the efficiency of the proposed method

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