Abstract We consider the convex quadratic optimization problem in$$\mathbb {R}^{n}$$ with indicator variables and arbitrary constraints on the indicators. We show that a convex hull description of the associated mixed-integer set in an extended space with a quadratic number of additional variables consists of an$$(n+1) \times (n+1)$$ positive semidefinite constraint (explicitly stated) and linear constraints. In particular, convexification of this class of problems reduces to describing a polyhedral set in an extended formulation. While the vertex representation of this polyhedral set is exponential and an explicit linear inequality description may not be readily available in general, we derive a compact mixed-integer linear formulation whose solutions coincide with the vertices of the polyhedral set. We also give descriptions in the original space of variables: we provide a description based on an infinite number of conic-quadratic inequalities, which are “finitely generated.” In particular, it is possible to characterize whether a given inequality is necessary to describe the convex hull. The new theory presented here unifies several previously established results, and paves the way toward utilizing polyhedral methods to analyze the convex hull of mixed-integer nonlinear sets.
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Maximum Consensus Floating Point Solutions for Infeasible Low-Dimensional Linear Programs with Convex Hull as the Intermediate Representation
This paper proposes a novel method to efficiently solveinfeasiblelow-dimensional linear programs (LDLPs) with billions of constraints and a small number of unknown variables, where all the constraints cannot be satisfied simultaneously. We focus on infeasible linear programs generated in the RLibmproject for creating correctly rounded math libraries. Specifically, we are interested in generating a floating point solution that satisfies the maximum number of constraints. None of the existing methods can solve such large linear programs while producing floating point solutions. We observe that the convex hull can serve as an intermediate representation (IR) for solving infeasible LDLPs using the geometric duality between linear programs and convex hulls. Specifically, some of the constraints that correspond to points on the convex hull are precisely those constraints that make the linear program infeasible. Our key idea is to split the entire set of constraints into two subsets using the convex hull IR: (a) a set of feasible constraints and (b) a superset of infeasible constraints. Using the special structure of the RLibmconstraints and the presence of a method to check whether a system is feasible or not. we identify a superset of infeasible constraints by computing the convex hull in 2-dimensions. Subsequently, we identify the key constraints (i.e., basis constraints) in the set of feasible constraints and use them to create a new linear program whose solution identifies the maximum set of constraints satisfiable in while satisfying all the constraints in . This new solver enabled us to improve the performance of the resulting RLibmpolynomials while solving the corresponding linear programs significantly faster.
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- PAR ID:
- 10612731
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 8
- Issue:
- PLDI
- ISSN:
- 2475-1421
- Format(s):
- Medium: X Size: p. 1239-1263
- Size(s):
- p. 1239-1263
- Sponsoring Org:
- National Science Foundation
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