Linearization Strategies for a Class of Zero-One Mixed Integer Programming Problems

This paper is concerned with a new linearization strategy for a class of zero-one mixed integer programming problems that contains quadratic cross-product terms between continuous and binary variables, and between the binary variables themselves. This linearization scheme provides an equivalent mixe...

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Bibliographic Details
Published in:Operations research Vol. 38; no. 2; pp. 217 - 226
Main Authors: Adams, Warren P, Sherali, Hanif D
Format: Journal Article
Language:English
Published: Linthicum, MD INFORMS 01.03.1990
Operations Research Society of America
Institute for Operations Research and the Management Sciences
Subjects:
Algorithms
Applied sciences
Constraints
Continuous variables
Exact sciences and technology
Hammers
integer
Integer programming
Integers
Linear programming
Linearization
Mathematical models
Mathematical programming
nonlinear: mixed integer programming problems
Objective functions
Operational research and scientific management
Operational research. Management science
Operations research
Polynomials
programming
programming: linear reformulations of nonlinear integer programs
Quadratic programming
Techniques
Theory
Integer programming
Relaxation
Mixed programming
Unified theory
Comparative study
Linearization
Mathematical programming
ISSN:0030-364X, 1526-5463
Online Access:Get full text
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Summary:This paper is concerned with a new linearization strategy for a class of zero-one mixed integer programming problems that contains quadratic cross-product terms between continuous and binary variables, and between the binary variables themselves. This linearization scheme provides an equivalent mixed integer linear programming problem which yields a tighter continuous relaxation than that obtainable via the alternative linearization techniques available in the literature. Moreover, the proposed technique provides a unifying framework in the sense that all the alternate methods lead to formulations that are accessible through appropriate surrogates of the constraints of the new linearized formulation. Extensions to various other types of mixed integer nonlinear programming problems are also discussed.
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ISSN:0030-364X
1526-5463
DOI:10.1287/opre.38.2.217