On solving the sum-of-ratios problem

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This paper addresses the development of efficient global search methods for fractional programming problems. Such problems are, in general, nonconvex (with numerous local extremums) and belong to a class of global optimization problems. First, we reduce a rather general fractional programming problem with d.c. functions to solving an equation with a vector parameter that satisfies some nonnegativity assumption. This theorem allows the justified use of the generalized Dinkelbach’s approach for solving fractional programming problems with a d.c. goal function. Based on solving of some d.c. minimization problem, we developed a global search algorithm for fractional programming problems, which was tested on a set of low-dimensional test problems taken from the literature as well as on randomly generated problems with up to 200 variables or 200 terms in the sum.

论文关键词:Fractional optimization,Nonconvex optimization,Difference of convex functions,Equation with vector parameter,Global search algorithm

论文评审过程:Received 9 December 2016, Revised 16 June 2017, Accepted 31 July 2017, Available online 21 August 2017, Version of Record 18 October 2017.

论文官网地址:https://doi.org/10.1016/j.amc.2017.07.074