Using Piecewise Linear Functions for Solving MINLP s

In this chapter we want to demonstrate that in certain cases general mixed integer nonlinear programs (MINLPs) can be solved by just applying purely techniques from the mixed integer linear world. The way to achieve this is to approximate the nonlinearities by piecewise linear functions. The advantage of applying mixed integer lin- ear techniques are that these methods are nowadays very mature, that is, they are fast, robust, and are able to solve problems with up to millions of variables. In addition, these methods have the potential of finding globally optimal solutions or at least to provide solution guarantees. On the other hand, one tends to say at this point “If you have a hammer, everything is a nail.”[15], because one tries to reformulate or to approximate an ac- tual nonlinear problem until one obtains a model that is tractable by the methods one is common with. Besides the fact that this is a very typical approach in mathematics the question stays whether this is a reasonable approach for the solution of MINLPs or whether the nature of the nonlin- earities inherent to the problem gets lost and the solutions obtained from the mixed integer linear problem have no meaning for the MINLP. The purpose of this chapter is to discuss this question. We will see that the truth lies somewhere in between and that there are problems where this is indeed a reasonable way to go and others where it is not.

[1]  J. Jensen Sur les fonctions convexes et les inégalités entre les valeurs moyennes , 1906 .

[2]  A. S. Manne,et al.  On the Solution of Discrete Programming Problems , 1956 .

[3]  G. Dantzig ON THE SIGNIFICANCE OF SOLVING LINEAR PROGRAMMING PROBLEMS WITH SOME INTEGER VARIABLES , 1960 .

[4]  E. M. L. Beale,et al.  Global optimization using special ordered sets , 1976, Math. Program..

[5]  R. R. Meyer,et al.  Mixed integer minimization models for piecewise-linear functions of a single variable , 1976, Discret. Math..

[6]  M. Todd Hamiltonian triangulations of Rn , 1979 .

[7]  Stephen C. Graves,et al.  A composite algorithm for a concave-cost network flow problem , 1989, Networks.

[8]  Christodoulos A Floudas,et al.  Global minimum potential energy conformations of small molecules , 1994, J. Glob. Optim..

[9]  Manfred Padberg,et al.  Location, Scheduling, Design and Integer Programming , 2011, J. Oper. Res. Soc..

[10]  Manfred W. Padberg,et al.  Approximating Separable Nonlinear Functions Via Mixed Zero-One Programs , 1998, Oper. Res. Lett..

[11]  Warren D. Smith A Lower Bound for the Simplexity of then-Cube via Hyperbolic Volumes , 2000, Eur. J. Comb..

[12]  Arkadi Nemirovski,et al.  On Polyhedral Approximations of the Second-Order Cone , 2001, Math. Oper. Res..

[13]  Jon Lee,et al.  Polyhedral methods for piecewise-linear functions I: the lambda method , 2001, Discret. Appl. Math..

[14]  George L. Nemhauser,et al.  Models for representing piecewise linear cost functions , 2004, Oper. Res. Lett..

[15]  John J. Bartholdi,et al.  The vertex-adjacency dual of a triangulated irregular network has a Hamiltonian cycle , 2004, Oper. Res. Lett..

[16]  F. Tardella On the existence of polyhedral convex envelopes , 2004 .

[17]  George L. Nemhauser,et al.  A Branch-and-Cut Algorithm Without Binary Variables for Nonconvex Piecewise Linear Optimization , 2006, Oper. Res..

[18]  Thomas L. Magnanti,et al.  Variable Disaggregation in Network Flow Problems with Piecewise Linear Costs , 2007, Oper. Res..

[19]  George L. Nemhauser,et al.  Nonconvex, lower semicontinuous piecewise linear optimization , 2008, Discret. Optim..

[20]  Hai Zhao,et al.  A special ordered set approach for optimizing a discontinuous separable piecewise linear function , 2008, Oper. Res. Lett..

[21]  Robert Weismantel,et al.  The Convex Envelope of (n--1)-Convex Functions , 2008, SIAM J. Optim..

[22]  George L. Nemhauser,et al.  Modeling disjunctive constraints with a logarithmic number of binary variables and constraints , 2008, Math. Program..

[23]  Christodoulos A. Floudas,et al.  Tight convex underestimators for $${\mathcal{C}^2}$$ -continuous problems: II. multivariate functions , 2008, J. Glob. Optim..

[24]  Christodoulos A. Floudas,et al.  Tight convex underestimators for $${{\mathcal C}^2}$$-continuous problems: I. univariate functions , 2008, J. Glob. Optim..

[25]  George L. Nemhauser,et al.  Mixed-Integer Models for Nonseparable Piecewise-Linear Optimization: Unifying Framework and Extensions , 2010, Oper. Res..

[26]  Alexander Martin,et al.  A mixed integer approach for time-dependent gas network optimization , 2010, Optim. Methods Softw..