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Compact Disjunctive Approximations to Nonconvex Quadratically Constrained Programs

Hongbo Dong (hongbo.dong***at***wsu.edu)
Yunqi Luo (yunqi.luo***at***wsu.edu)

Abstract: Decades of advances in mixed-integer linear programming (MILP) and recent development in mixed-integer second-order-cone programming (MISOCP) have translated very mildly to progresses in global solving nonconvex mixed-integer quadratically constrained programs (MIQCP). In this paper we propose a new approach, namely Compact Disjunctive Approximation (CDA), to approximate nonconvex MIQCP to arbitrary precision by convex MIQCPs, which can be solved by MISOCP solvers. For nonconvex MIQCP with $n$ variables and $m$ general quadratic constraints, our method yields relaxations with at most $O(n\log(1/\varepsilon))$ number of continuous/binary variables and linear constraints, together with $m$ convex quadratic constraints, where $\varepsilon$ is the approximation accuracy. The main novelty of our method lies in a very compact lifted mixed-integer formulation for approximating the (scalar) square function. This is derived by first embedding the square function into the boundary of a three-dimensional second-order cone, and then exploiting rotational symmetry in a similar way as in the construction of BenTal-Nemirovski approximation. We further show that this lifted formulation characterize the union of finite number of simple convex sets, which naturally relax the square function in a piecewise manner with properly placed knots. We implement (with JuMP) a simple adaptive refinement algorithm. Numerical experiments on synthetic instances used in the literature show that our prototypical implementation (with hundreds of lines of Julia code) can already close a significant portion of gap left by various state-of-the-art global solvers on more difficult instances, indicating strong promises of our proposed approach.

Keywords: Mixed-integer quadratically constrained programs; lifted formulations; symmetry

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Global Optimization (Theory )

Category 3: Linear, Cone and Semidefinite Programming (Second-Order Cone Programming )

Citation: Working paper, Department of Mathematics and Statistics, Washington State University, 11/2018.

Download: [PDF]

Entry Submitted: 11/20/2018
Entry Accepted: 11/20/2018
Entry Last Modified: 12/04/2018

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