Computational experience using an edge search algorithm for linear reverse convex programs |
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| Authors: | Stephen E Jacobsen Khosrow Moshirvaziri |
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| Institution: | (1) Electrical Engineering Department, University of California, 90024 Los Angeles, California, U.S.A.;(2) Information Systems Department, California State University, 90840 Long Beach, California;(3) Electrical Engineering Department, University of California, 90024 Los Angeles, CA, U.S.A. |
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| Abstract: | This paper presents computational experience with a rather straight forward implementation of an edge search algorithm for obtaining the globally optimal solution for linear programs with an additional reverse convex constraint. The paper's purpose is to provide a collection of problems, with known optimal solutions, and performance information for an edge search implementation so that researchers may have some benchmarks with which to compare new methods for reverse convex programs or concave minimization problems. There appears to be nothing in the literature that provides computational experience with a basic edge search procedure. The edge search implementation uses a depth first strategy. As such, this paper's implementation of the edge search algorithm is a modification of Hillestad's algorithm 11]. A variety of test problems is generated by using a modification of the method of Sung and Rosen 20], as well as a new method that is presented in this paper. Test problems presented may be obtained at ftp://newton.ee.ucla.edu/nonconvex/pub/. |
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| Keywords: | Reverse convex programs nonconvex optimization global optimization test problem generation linear programming nonlinear programming computational experiments |
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