Reformulation techniques in mathematical programming

Leo Liberti; Nelson Maculan

doi:10.1016/j.dam.2008.10.016

Reformulation techniques in mathematical programming

Leo Liberti
, Nelson Maculan

Research output: Contribution to journal › Editorial

Abstract

The special issue of Discrete Applied Mathematics (DAM) presents some important papers on Reformulation Techniques in Mathematical Programming. Reformulations may change any combination of parameters, variables, objectives and constraints in a mathematical program. The Reformulation-Linearization Technique (RLT) and the symbolic reformulation are both designed to obtain a tight bound for the objective function value of Mixed-Integer Nonlinear Programming (MINLP) problems. This issue contains papers by Billionnet et al., Gueye and Michelon, Hansen and Meyer, Sherali and Adams, that either propose new reformulations in mathematical programming or employ reformulation techniques to improve the state of the art in theoretical or applied fields. Eleven papers out of twenty-one submissions were accepted for publication.

Original language	English
Pages (from-to)	1165-1166
Number of pages	2
Journal	Discrete Applied Mathematics
Volume	157
Issue number	6
DOIs	https://doi.org/10.1016/j.dam.2008.10.016
Publication status	Published - 28 Mar 2009

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title = "Reformulation techniques in mathematical programming",

abstract = "The special issue of Discrete Applied Mathematics (DAM) presents some important papers on Reformulation Techniques in Mathematical Programming. Reformulations may change any combination of parameters, variables, objectives and constraints in a mathematical program. The Reformulation-Linearization Technique (RLT) and the symbolic reformulation are both designed to obtain a tight bound for the objective function value of Mixed-Integer Nonlinear Programming (MINLP) problems. This issue contains papers by Billionnet et al., Gueye and Michelon, Hansen and Meyer, Sherali and Adams, that either propose new reformulations in mathematical programming or employ reformulation techniques to improve the state of the art in theoretical or applied fields. Eleven papers out of twenty-one submissions were accepted for publication.",

author = "Leo Liberti and Nelson Maculan",

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doi = "10.1016/j.dam.2008.10.016",

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AU - Maculan, Nelson

PY - 2009/3/28

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AB - The special issue of Discrete Applied Mathematics (DAM) presents some important papers on Reformulation Techniques in Mathematical Programming. Reformulations may change any combination of parameters, variables, objectives and constraints in a mathematical program. The Reformulation-Linearization Technique (RLT) and the symbolic reformulation are both designed to obtain a tight bound for the objective function value of Mixed-Integer Nonlinear Programming (MINLP) problems. This issue contains papers by Billionnet et al., Gueye and Michelon, Hansen and Meyer, Sherali and Adams, that either propose new reformulations in mathematical programming or employ reformulation techniques to improve the state of the art in theoretical or applied fields. Eleven papers out of twenty-one submissions were accepted for publication.

U2 - 10.1016/j.dam.2008.10.016

DO - 10.1016/j.dam.2008.10.016

M3 - Editorial

AN - SCOPUS:61849166710

SN - 0166-218X

VL - 157

SP - 1165

EP - 1166

JO - Discrete Applied Mathematics

JF - Discrete Applied Mathematics

IS - 6

ER -

Reformulation techniques in mathematical programming

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