Relaxation And Decomposition Methods For Mixed Integer Nonlinear Programming by Ivo NowakRelaxation And Decomposition Methods For Mixed Integer Nonlinear Programming by Ivo Nowak

Relaxation And Decomposition Methods For Mixed Integer Nonlinear Programming

byIvo Nowak

Hardcover | September 27, 2005

Pricing and Purchase Info

$214.32 online 
$247.50 list price save 13%
Earn 1,072 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


Nonlinearoptimizationproblemscontainingbothcontinuousanddiscretevariables are called mixed integer nonlinear programs (MINLP). Such problems arise in many ?elds, such as process industry, engineering design, communications, and ?nance. There is currently a huge gap between MINLP and mixed integer linear programming(MIP) solvertechnology.With a modernstate-of-the-artMIP solver itispossibletosolvemodelswithmillionsofvariablesandconstraints,whereasthe dimensionofsolvableMINLPsisoftenlimitedbyanumberthatissmallerbythree or four orders of magnitude. It is theoretically possible to approximate a general MINLP by a MIP with arbitrary precision. However, good MIP approximations are usually much larger than the original problem. Moreover, the approximation of nonlinear functions by piecewise linear functions can be di?cult and ti- consuming. In this book relaxation and decomposition methods for solving nonconvex structured MINLPs are proposed. In particular, a generic branch-cut-and-price (BCP) framework for MINLP is presented. BCP is the underlying concept in almost all modern MIP solvers. Providing a powerful decomposition framework for both sequential and parallel solvers, it made the success of the current MIP technology possible. So far generic BCP frameworks have been developed only for MIP, for example,COIN/BCP (IBM, 2003) andABACUS (OREAS GmbH, 1999). In order to generalize MIP-BCP to MINLP-BCP, the following points have to be taken into account: • A given (sparse) MINLP is reformulated as a block-separable program with linear coupling constraints.The block structure makes it possible to generate Lagrangian cuts and to apply Lagrangian heuristics. • In order to facilitate the generation of polyhedral relaxations, nonlinear c- vex relaxations are constructed. • The MINLP separation and pricing subproblems for generating cuts and columns are solved with specialized MINLP solvers.
Title:Relaxation And Decomposition Methods For Mixed Integer Nonlinear ProgrammingFormat:HardcoverDimensions:213 pagesPublished:September 27, 2005Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3764372389

ISBN - 13:9783764372385


Table of Contents

Basic Concepts.- Problem Formulations.- Convex and Lagrangian Relaxations.- Decomposition Methods.- Semidefinite Relaxations.- Convex Underestimators.- Cuts, Lower Bounds and Box Reduction.- Local and Global Optimality Criteria.- Adaptive Discretization of Infinite Dimensional MINLPs.- Algorithms.- Overview of Global Optimization Methods.- Deformation Heuristics.- Rounding, Partitioning and Lagrangian Heuristics.- Branch-Cut-and-Price Algorithms.- LaGO - An Object-Oriented Library for Solving MINLPs.