Algorithms for network piecewise-linear programs: A comparative study

doi:10.1016/S0377-2217(96)00109-9

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Algorithms for network piecewise-linear programs: A comparative study

1997-02-01

PRE

Fernando Augusto Silva Marins

Edson Luiz França Senne

K Darby-Dowman

Arlene F. Machado

Clovis Perin

DOI：10.1016/S0377-2217(96)00109-9

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Abstract

En 中文

Piecewise-Linear Programming (PLP) is an important area of Mathematical Programming and concerns the minimisation of a convex separable piecewise-linear objective function, subject to linear constraints. In this paper a subarea of PLP called Network Piecewise-Linear Programming (NPLP) is explored. The paper presents four specialised algorithms for NPLP: (Strongly Feasible) Primal Simplex, Dual Method, Out-of-Kilter and (Strongly Polynomial) Cost-Scaling and their relative efficiency is studied. A statistically designed experiment is used to perform a computational comparison of the algorithms. The response variable observed in the experiment is the CPU time to solve randomly generated network piecewise-linear problems classified according to problem class (Transportation, Transshipment and Circulation), problem size, extent of capacitation, and number of breakpoints per are. Results and conclusions on performance of the algorithms are reported.

Keywords:

network programming

convex piecewise-linear costs

algorithms

computational analysis

experimental design

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