Optimality conditions and complete description of polytopes in combinatorial optimization
Alexey Antonov

TL;DR
This paper develops a comprehensive set of optimality conditions for combinatorial optimization problems, enabling a complete polyhedral description of their feasible regions through linear inequalities.
Contribution
It introduces a general analytical framework for describing the polytopes of combinatorial optimization problems, extending beyond extreme point convex hulls.
Findings
Provides conditions for optimality based on cones of vectors
Characterizes facet-inducing inequalities for polytopes
Enables complete polyhedral descriptions of combinatorial problems
Abstract
A combinatorial optimization problem (COP) has a finite groundset ), a weight vector and a family of feasible subsets with objective to find with maximal weight: : . Polyhedral combinatorics reformulates combinatorial optimization as linear program: is mapped into the set of 0/1 incidence vectors and is maximized over the convex hull of : . In theory, complementary slackness conditions for the induced linear program provide optimality conditions for the COP. However, in general case, optimality conditions for combinatorial optimization have not been formulated analytically as for many problems complete description of the induced polytopes is available only as a convex hull of extreme points rather than a system of linear…
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Taxonomy
TopicsOptimization and Packing Problems · Vehicle Routing Optimization Methods · Advanced Graph Theory Research
