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A Penalized Quadratic Convex Reformulation Method for Random Quadratic Unconstrained Binary Optimization

Karthik Natarajan(natarajan_karthik***at***sutd.edu.sg)
Dongjian Shi(shidongjian***at***nus.edu.sg)
Kim Chuan Toh(mattohkc***at***nus.edu.sg)

Abstract: The Quadratic Convex Reformulation (QCR) method is used to solve quadratic unconstrained binary optimization problems. In this method, the semidefinite relaxation is used to reformulate it to a convex binary quadratic program which is solved using mixed integer quadratic programming solvers. We extend this method to random quadratic unconstrained binary optimization problems. We develop a Penalized QCR method where the objective function in the semidefinite program is penalized with a separable term to account for the randomness in the objective.

Keywords: quadratic unconstrained binary optimization, penalized quadratic convex reformulation, semidefinite relaxation

Category 1: Integer Programming (0-1 Programming )

Category 2: Global Optimization (Stochastic Approaches )

Citation:

Download: [PDF]

Entry Submitted: 06/14/2013
Entry Accepted: 06/15/2013
Entry Last Modified: 06/14/2013

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