Fractional 0–1 programs: links between mixed-integer linear and conic quadratic formulations

Erfan Mehmanchi (), Andrés Gómez () and Oleg A. Prokopyev ()
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Erfan Mehmanchi: University of Pittsburgh
Andrés Gómez: University of Southern California
Oleg A. Prokopyev: University of Pittsburgh

Journal of Global Optimization, 2019, vol. 75, issue 2, No 3, 273-339

Abstract: Abstract This paper focuses on methods that improve the performance of solution approaches for multiple-ratio fractional 0–1 programs (FPs) in their general structure. In particular, we explore the links between equivalent mixed-integer linear programming and conic quadratic programming reformulations of FPs. Thereby, we show that integrating the ideas behind these two types of reformulations of FPs allows us to push further the limits of the current state-of-the-art results and tackle larger-size problems. We perform extensive computational experiments to compare the proposed approaches against the current reformulations from the literature.

Keywords: Fractional 0–1 programming; Conic quadratic programming; Mixed-integer linear programming; Polymatroid cuts; Binary-expansion; Assortment optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-019-00817-7

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