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On feasibility based bounds tightening

Pietro Belotti(pbelott***at***clemson.edu)
Sonia Cafieri(sonia.cafieri***at***enac.fr)
Jon Lee(jonxlee***at***umich.edu)
Leo Liberti(leoliberti***at***gmail.com)

Abstract: Mathematical programming problems involving nonconvexities are usually solved to optimality using a (spatial) Branch-and-Bound algorithm. Algorithmic efficiency depends on many factors, among which the widths of the bounding box for the problem variables at each Branch-and-Bound node naturally plays a critical role. The practically fastest box-tightening algorithm is known as FBBT (Feasibility-Based Bounds Tightening): an iterative procedure to tighten the variable ranges. Depending on the instance, FBBT may not converge finitely to its limit ranges, even in the case of linear constraints. Tolerance-based termination criteria yield finite termination, but not in worst-case polynomial-time. We model FBBT by using fixed-point equations in terms of the variable bounding box, and we treat these equations as constraints of an auxiliary mathematical program. We demonstrate that the auxiliary mathematical problem is a linear program, which can of course be solved in polynomial time. We demonstrate the usefulness of our approach by improving an existing Branch-and-Bound implementation.

Keywords: global optimization, MINLP, spatial Branch-and-Bound, range reduction

Category 1: Global Optimization (Theory )

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation:

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Entry Submitted: 01/24/2012
Entry Accepted: 01/24/2012
Entry Last Modified: 01/24/2012

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