Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems

A conic optimization problem is a problem involving a constraint that the optimization variable be in some closed convex cone. Prominent examples are linear programs (LP), second order cone programs (SOCP), semidefinite problems (SDP), and copositive problems. We survey recent progress made in this...

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Main Authors: Mirjam Dür, Franz Rendl
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:EURO Journal on Computational Optimization
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2192440621001489
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author Mirjam Dür
Franz Rendl
author_facet Mirjam Dür
Franz Rendl
author_sort Mirjam Dür
collection DOAJ
description A conic optimization problem is a problem involving a constraint that the optimization variable be in some closed convex cone. Prominent examples are linear programs (LP), second order cone programs (SOCP), semidefinite problems (SDP), and copositive problems. We survey recent progress made in this area. In particular, we highlight the connections between nonconvex quadratic problems, binary quadratic problems, and copositive optimization. We review how tight bounds can be obtained by relaxing the copositivity constraint to semidefiniteness, and we discuss the effect that different modelling techniques have on the quality of the bounds. We also provide some new techniques for lifting linear constraints and show how these can be used for stable set and coloring relaxations.
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spelling doaj.art-f136d5fba5254566a11a3a59133648da2022-12-21T18:45:43ZengElsevierEURO Journal on Computational Optimization2192-44062021-01-019100021Conic optimization: A survey with special focus on copositive optimization and binary quadratic problemsMirjam Dür0Franz Rendl1Corresponding author.; Department of Mathematics, Augsburg University, Augsburg 86135, GermanyDepartment of Mathematics, Alpen-Adria-University Klagenfurt, Klagenfurt 9020, AustriaA conic optimization problem is a problem involving a constraint that the optimization variable be in some closed convex cone. Prominent examples are linear programs (LP), second order cone programs (SOCP), semidefinite problems (SDP), and copositive problems. We survey recent progress made in this area. In particular, we highlight the connections between nonconvex quadratic problems, binary quadratic problems, and copositive optimization. We review how tight bounds can be obtained by relaxing the copositivity constraint to semidefiniteness, and we discuss the effect that different modelling techniques have on the quality of the bounds. We also provide some new techniques for lifting linear constraints and show how these can be used for stable set and coloring relaxations.http://www.sciencedirect.com/science/article/pii/S219244062100148990C2290C2090C27
spellingShingle Mirjam Dür
Franz Rendl
Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
EURO Journal on Computational Optimization
90C22
90C20
90C27
title Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
title_full Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
title_fullStr Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
title_full_unstemmed Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
title_short Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems
title_sort conic optimization a survey with special focus on copositive optimization and binary quadratic problems
topic 90C22
90C20
90C27
url http://www.sciencedirect.com/science/article/pii/S2192440621001489
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