Literature DB >> 27547676

An efficient method for generalized linear multiplicative programming problem with multiplicative constraints.

Yingfeng Zhao1, Sanyang Liu2.   

Abstract

We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

Keywords:  Branch and bound; Generalized linear multiplicative programming; Global optimization

Year:  2016        PMID: 27547676      PMCID: PMC4978663          DOI: 10.1186/s40064-016-2984-9

Source DB:  PubMed          Journal:  Springerplus        ISSN: 2193-1801


  1 in total

1.  Convergence of batch gradient learning with smoothing regularization and adaptive momentum for neural networks.

Authors:  Qinwei Fan; Wei Wu; Jacek M Zurada
Journal:  Springerplus       Date:  2016-03-08
  1 in total

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