Author: "Chun-Feng, Wang" / Publisher: elsevier b.v. / Search Limiters: Peer Reviewed / Topic: linear programming - Searchworks@Jio Institute Digital Library Search Results

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Start Over Author "Chun-Feng, Wang" Search Limiters Peer Reviewed Topic linear programming Publisher elsevier b.v.

Author: Chun-Feng, Wang, San-Yang, Liu, and Pei-Ping, Shen
Subjects: *GEOMETRIC analysis, *ALGORITHMS, *LINEAR programming, *NUMERICAL analysis, *MATHEMATICAL transformations, *MATHEMATICAL optimization, *INITIAL value problems, *STOCHASTIC convergence
Abstract: Abstract: This paper presents an efficient branch and bound algorithm for globally solving sum of geometric fractional functions under geometric constraints, which arise in various practical problems. By using an equivalent transformation and a new linear relaxation technique, a linear relaxation programming problem of the equivalent problem is obtained. The proposed algorithm is convergent to the global optimal solution by means of the subsequent solutions of a series of linear programming problems. Numerical results are reported to show the feasibility of our algorithm. [Copyright &y& Elsevier]
Published: 2010
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Author: Peiping Shen and Chun-Feng Wang
Subjects: Mathematical optimization, Linear programming, Linear relaxation, Applied Mathematics, Generalized fractional programming, Nonlinear programming, Linear-fractional programming, Computational Mathematics, Fractional programming, Linearization, Global optimization, Branch and bound, Criss-cross algorithm, Geometric programming, Mathematics
Abstract: This paper considers the solution of generalized fractional programming (GFP) problem which contains various variants such as a sum or product of a finite number of ratios of linear functions, polynomial fractional programming, generalized geometric programming, etc. over a polytope. For such problems, we present an efficient unified method. In this method, by utilizing a transformation and a two-part linearization method, a sequence of linear programming relaxations of the initial nonconvex programming problem are derived which are embedded in a branch-and-bound algorithm. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm.
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