Strong duality for standard convex programs

被引：0
|
作者：
Kenneth O. Kortanek
Guolin Yu
Qinghong Zhang
机构：
[1] University of Pittsburgh,Department of Industrial Engineering
[2] North Minzu University,Institute of Applied Mathematics
[3] Northern Michigan University,Department of Mathematics and Computer Science
来源：
Mathematical Methods of Operations Research | 2021年 / 94卷
关键词：
Convex optimization; Strong duality; Bifunction; Image space analysis; Polynomial ring; 90C25; 90C46; 49N15;
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学科分类号：
摘要：
A primal optimization problem and its dual are in strong duality if either one of the problems has a finite optimal value, then the other one is consistent and has the same optimal value, and the optimal value of the dual problem is attained. In this paper, we study the strong duality without constraint qualifications for a standard convex optimization problem using the bifunction, image space analysis, and polynomial ring approaches. We obtain new strong duals for the primal convex optimization problem, which to the best of our knowledge have not been appeared in the related literature.
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页码：413 / 436
页数：23
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