Theoretical filtering of RLT bound-factor constraints for solving polynomial programming problems to global optimality

Evrim Dalkiran () and Hanif Sherali ()

Journal of Global Optimization, 2013, vol. 57, issue 4, 1147-1172

Abstract: In this paper, we propose two sets of theoretically filtered bound-factor constraints for constructing reformulation-linearization technique (RLT)-based linear programming (LP) relaxations for solving polynomial programming problems. We establish related theoretical results for convergence to a global optimum for these reduced sized relaxations, and provide insights into their relative sizes and tightness. Extensive computational results are provided to demonstrate the relative effectiveness of the proposed theoretical filtering strategies in comparison to the standard RLT and a prior heuristic filtering technique using problems from the literature as well as randomly generated test cases. Copyright Springer Science+Business Media New York 2013

Keywords: Reformulation-linearization technique (RLT); Filtering strategies; Polynomial programming; Branch-and-bound (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-012-0024-z

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