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.
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