An inner approximation method incorporating with a penalty function method for a reverse convex programming problem

In this paper, we consider a reverse convex programming problem constrained by a convex set and a reverse convex set which is defined by the complement of the interior of a compact convex set X. When X is not necessarily a polytope, an inner approximation method has been proposed (J. Optim. Theory A...

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Bibliographic Details
Published in:Journal of computational and applied mathematics Vol. 146; no. 1; pp. 57 - 75
Main Authors: Yamada, Syuuji, Tanino, Tetsuzo, Inuiguchi, Masahiro
Format: Journal Article Conference Proceeding
Language:English
Published: Amsterdam Elsevier B.V 01.09.2002
Elsevier
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ISSN:0377-0427, 1879-1778
Online Access:Get full text
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Summary:In this paper, we consider a reverse convex programming problem constrained by a convex set and a reverse convex set which is defined by the complement of the interior of a compact convex set X. When X is not necessarily a polytope, an inner approximation method has been proposed (J. Optim. Theory Appl. 107(2) (2000) 357). The algorithm utilizes inner approximation of X by a sequence of polytopes to generate relaxed problems. Then, every accumulation point of the sequence of optimal solutions of relaxed problems is an optimal solution of the original problem. In this paper, we improve the proposed algorithm. By underestimating the optimal value of the relaxed problem, the improved algorithms have the global convergence.
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content type line 23
ISSN:0377-0427
1879-1778
DOI:10.1016/S0377-0427(02)00418-1