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Convexification And Global Optimization In Continuous And Mixed Integer Nonlinear Programming
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Book Synopsis Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming by : Mohit Tawarmalani
Download or read book Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming written by Mohit Tawarmalani and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in constrained optimization originated with the simple linear pro gramming model since it was practical and perhaps the only computationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models. Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems. On the brighter side, recent advances in algorithmic and computing technology make it possible to re visit these problems with the hope of solving practically relevant problems in reasonable amounts of computational time. Initial attempts at solving nonlinear programs concentrated on the de velopment of local optimization methods guaranteeing globality under the assumption of convexity. On the other hand, the integer programming liter ature has concentrated on the development of methods that ensure global optima. The aim of this book is to marry the advancements in solving nonlinear and integer programming models and to develop new results in the more general framework of mixed-integer nonlinear programs (MINLPs) with the goal of devising practically efficient global optimization algorithms for MINLPs.
Book Synopsis Mixed Integer Nonlinear Programming by : Jon Lee
Download or read book Mixed Integer Nonlinear Programming written by Jon Lee and published by Springer Science & Business Media. This book was released on 2011-12-02 with total page 692 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.
Book Synopsis Disjunctive Programming by : Egon Balas
Download or read book Disjunctive Programming written by Egon Balas and published by Springer. This book was released on 2018-11-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disjunctive Programming is a technique and a discipline initiated by the author in the early 1970's, which has become a central tool for solving nonconvex optimization problems like pure or mixed integer programs, through convexification (cutting plane) procedures combined with enumeration. It has played a major role in the revolution in the state of the art of Integer Programming that took place roughly during the period 1990-2010. The main benefit that the reader may acquire from reading this book is a deeper understanding of the theoretical underpinnings and of the applications potential of disjunctive programming, which range from more efficient problem formulation to enhanced modeling capability and improved solution methods for integer and combinatorial optimization. Egon Balas is University Professor and Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business.
Book Synopsis Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming by : Ivo Nowak
Download or read book Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming written by Ivo Nowak and published by Springer Science & Business Media. This book was released on 2005-08-15 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming by : Ivo Nowak
Download or read book Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming written by Ivo Nowak and published by Birkhäuser. This book was released on 2009-09-03 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Lectures on Global Optimization by : Thomas Frederick Coleman
Download or read book Lectures on Global Optimization written by Thomas Frederick Coleman and published by American Mathematical Soc.. This book was released on 2009 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large number of mathematical models in many diverse areas of science and engineering have lead to the formulation of optimization problems where the best solution (globally optimal) is needed. This book covers a small subset of important topics in global optimization with emphasis on theoretical developments and scientific applications.
Book Synopsis Nonlinear Integer Programming by : Duan Li
Download or read book Nonlinear Integer Programming written by Duan Li and published by Springer Science & Business Media. This book was released on 2006-08-13 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: A combination of both Integer Programming and Nonlinear Optimization, this is a powerful book that surveys the field and provides a state-of-the-art treatment of Nonlinear Integer Programming. It is the first book available on the subject. The book aims to bring the theoretical foundation and solution methods for nonlinear integer programming to students and researchers in optimization, operations research, and computer science.
Book Synopsis Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming by : Ivo Nowak
Download or read book Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming written by Ivo Nowak and published by Springer Science & Business Media. This book was released on 2006-03-28 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems by : Hanif D. Sherali
Download or read book A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems written by Hanif D. Sherali and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the theory and applications of the Reformulation- Linearization/Convexification Technique (RL T) for solving nonconvex optimization problems. A unified treatment of discrete and continuous nonconvex programming problems is presented using this approach. In essence, the bridge between these two types of nonconvexities is made via a polynomial representation of discrete constraints. For example, the binariness on a 0-1 variable x . can be equivalently J expressed as the polynomial constraint x . (1-x . ) = 0. The motivation for this book is J J the role of tight linear/convex programming representations or relaxations in solving such discrete and continuous nonconvex programming problems. The principal thrust is to commence with a model that affords a useful representation and structure, and then to further strengthen this representation through automatic reformulation and constraint generation techniques. As mentioned above, the focal point of this book is the development and application of RL T for use as an automatic reformulation procedure, and also, to generate strong valid inequalities. The RLT operates in two phases. In the Reformulation Phase, certain types of additional implied polynomial constraints, that include the aforementioned constraints in the case of binary variables, are appended to the problem. The resulting problem is subsequently linearized, except that certain convex constraints are sometimes retained in XV particular special cases, in the Linearization/Convexijication Phase. This is done via the definition of suitable new variables to replace each distinct variable-product term. The higher dimensional representation yields a linear (or convex) programming relaxation.
Book Synopsis Introduction to Global Optimization by : R. Horst
Download or read book Introduction to Global Optimization written by R. Horst and published by Springer Science & Business Media. This book was released on 2000-12-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook for an undergraduate course in mathematical programming for students with a knowledge of elementary real analysis, linear algebra, and classical linear programming (simple techniques). Focuses on the computation and characterization of global optima of nonlinear functions, rather than the locally optimal solutions addressed by most books on optimization. Incorporates the theoretical, algorithmic, and computational advances of the past three decades that help solve globally multi-extreme problems in the mathematical modeling of real world systems. Annotation copyright by Book News, Inc., Portland, OR
Book Synopsis Convex Relaxations for Mixed-Integer Nonlinear Programs by : Martin Ballerstein
Download or read book Convex Relaxations for Mixed-Integer Nonlinear Programs written by Martin Ballerstein and published by Cuvillier Verlag. This book was released on 2013-09-16 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with new techniques to construct a strong convex relaxation for a mixed-integer nonlinear program (MINLP). While local optimization software can quickly identify promising operating points of MINLPs, the solution of the convex relaxation provides a global bound on the optimal value of the MINLP that can be used to evaluate the quality of the local solution. Certainly, the efficiency of this evaluation is strongly dependent on the quality of the convex relaxation. Convex relaxations of general MINLPs can be constructed by replacing each nonlinear function occurring in the model description by convex underestimating and concave overestimating functions. In this setting, it is desired to use the best possible convex underestimator and concave overestimator of a given function over an underlying domain -- the so-called convex and concave envelope, respectively. However, the computation of these envelopes can be extremely difficult so that analytical expressions for envelopes are only available for some classes of well-structured functions. Another factor influencing the strength of the estimators is the size of the underlying domain: The smaller the domain, the better the quality of the estimators. In many applications the initial domains of the variables are chosen rather conservatively while tighter bounds are implicitly given by the constraint set of the MINLP. Thus, bound tightening techniques, which exploit the information of the constraint set, are an essential ingredient to improve the estimators and to accelerate global optimization algorithms. The focus of this thesis lies on the development and computational analysis of new convex relaxations for MINLPs, especially for two applications from chemical engineering. In detail, we derive a new bound tightening technique for a general structure used for modeling chemical processes and provide different approaches to generate strong convex relaxations for various nonlinear functions. Initially, we aim at the optimal design of hybrid distillation/melt-crystallization processes, a novel process configuration to separate a m ixture into its component. A crucial part in the formal representation of this process as well as other separation processes is to model the mass conservation within the process. We exploit the analytical properties of the corresponding equation system to reduce the domains of the involved variables. Using the proposed technique, we can accelerate the computations for hybrid distillation/melt-crystallization processes significantly compared to standard software. Then, we concentrate on the generation of convex relaxations for nonlinear functions. First, we exploit the existing theory for two interesting classes of bivariate functions. On the one hand, we elaborate, implement, and illustrate the strength of a cut-generation algorithm for bivariate functions which are convex or concave in each variable and for which the sign of the Hessian is the same over the entire domain. On the other hand, relaxation strategies for advanced equilibrium functions in chromatographic separation processes are analyzed and finally applied to completely describe the feasible separation regions of these processes. Second, we suggest to derive the envelopes in an extended space to overcome the combinatorial difficulties involved in the computation of the convex envelope in the original space. In particular, we consider a class of functions accounting for a large amount of all nonlinearities in common benchmark libraries. These functions are component-wise concave in one part of the variables and convex in the other part of the variables. For this general class of functions the convex envelopes in the original variable space have not been discovered so far. We provide closed-form expressions for the extended formulation of their convex envelopes based on the simultaneous convexification with multilinear monomials. By construction, this approach does not only yield an extended formulation for the convex envelope of a function, but also a strong simultaneous relaxation of the function and the involved multilinear monomials. Several examples show that this simultaneous relaxation can be orders of magnitude better than the individual relaxation of the functions. Finally, inspired by the strength and the computational impact of the simultaneous relaxation of a function and multilinear monomials, we further focus on the simultaneous convexification of several functions. In such an approach the relaxation of a MINLP involving several functions in the same variables is much tighter because the interdependence between the different functions is taken into account. We study the simultaneous convex hull of several functions for which we derive theoretical results concerning their inner and outer description by means of the rich theory of convex envelopes. Moreover, we apply these results to provide formulas for tight convex relaxations of several univariate convex functions. Implementations of all convexification techniques are available as plugins for the open-source MINLP solver scip. The computational results of several case studies reveal the benefit of the proposed techniques compared to state-of-the-art methods.
Book Synopsis Global Optimization and Constraint Satisfaction by : Christian Bliek
Download or read book Global Optimization and Constraint Satisfaction written by Christian Bliek and published by Springer. This book was released on 2003-10-28 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Global Constraints Optimization and Costraint Satisfaction, COCOS 2002, held in Valbonne-Sophia Antipolis, France in October 2002. The 15 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. The papers address current issues in global optimization, mathematical programming, and constraint programming; they are grouped in topical sections on optimization, constraint satisfaction, and benchmarking.
Book Synopsis Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty by : Jonas Schweiger
Download or read book Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty written by Jonas Schweiger and published by Logos Verlag Berlin GmbH. This book was released on 2018 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amazing success of computational mathematical optimization over the last decades has been driven more by insights into mathematical structures than by the advance of computing technology. In this vein, Jonas Schweiger addresses applications, where nonconvexity in the model and uncertainty in the data pose principal difficulties. In the first part, he contributes strong relaxations for non-convex problems such as the non-convex quadratic programming and the Pooling Problem. In the second part, he contributes a robust model for gas transport network extension and a custom decomposition approach. All results are backed by extensive computational studies.
Book Synopsis Modeling and Optimization in Space Engineering by : Giorgio Fasano
Download or read book Modeling and Optimization in Space Engineering written by Giorgio Fasano and published by Springer Science & Business Media. This book was released on 2012-10-23 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a selection of advanced case studies that address a substantial range of issues and challenges arising in space engineering. The contributing authors are well-recognized researchers and practitioners in space engineering and in applied optimization. The key mathematical modeling and numerical solution aspects of each application case study are presented in sufficient detail. Classic and more recent space engineering problems – including cargo accommodation and object placement, flight control of satellites, integrated design and trajectory optimization, interplanetary transfers with deep space manoeuvres, low energy transfers, magnetic cleanliness modeling, propulsion system design, sensor system placement, systems engineering, space traffic logistics, and trajectory optimization – are discussed. Novel points of view related to computational global optimization and optimal control, and to multidisciplinary design optimization are also given proper emphasis. A particular attention is paid also to scenarios expected in the context of future interplanetary explorations. Modeling and Optimization in Space Engineering will benefit researchers and practitioners working on space engineering applications. Academics, graduate and post-graduate students in the fields of aerospace and other engineering, applied mathematics, operations research and optimal control will also find the book useful, since it discusses a range of advanced model development and solution techniques and tools in the context of real-world applications and new challenges.
Author :Christodoulos A. Floudas Publisher :Springer Science & Business Media ISBN 13 :146130251X Total Pages :590 pages Book Rating :4.4/5 (613 download)
Book Synopsis Frontiers in Global Optimization by : Christodoulos A. Floudas
Download or read book Frontiers in Global Optimization written by Christodoulos A. Floudas and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global Optimization has emerged as one of the most exciting new areas of mathematical programming. Global optimization has received a wide attraction from many fields in the past few years, due to the success of new algorithms for addressing previously intractable problems from diverse areas such as computational chemistry and biology, biomedicine, structural optimization, computer sciences, operations research, economics, and engineering design and control. This book contains refereed invited papers submitted at the 4th international confer ence on Frontiers in Global Optimization held at Santorini, Greece during June 8-12, 2003. Santorini is one of the few sites of Greece, with wild beauty created by the explosion of a volcano which is in the middle of the gulf of the island. The mystic landscape with its numerous mult-extrema, was an inspiring location particularly for researchers working on global optimization. The three previous conferences on "Recent Advances in Global Opti mization", "State-of-the-Art in Global Optimization", and "Optimization in Computational Chemistry and Molecular Biology: Local and Global approaches" took place at Princeton University in 1991, 1995, and 1999, respectively. The papers in this volume focus on de terministic methods for global optimization, stochastic methods for global optimization, distributed computing methods in global optimization, and applications of global optimiza tion in several branches of applied science and engineering, computer science, computational chemistry, structural biology, and bio-informatics.
Book Synopsis Advanced Optimization for Process Systems Engineering by : Ignacio E. Grossmann
Download or read book Advanced Optimization for Process Systems Engineering written by Ignacio E. Grossmann and published by Cambridge University Press. This book was released on 2021-03-25 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential text for single-semester, graduate courses in process systems engineering in departments of chemical engineering.
Book Synopsis Integer Programming and Combinatorial Optimization by : Andrea Lodi
Download or read book Integer Programming and Combinatorial Optimization written by Andrea Lodi and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2008, held in Bertinoro, Italy, in May 2008. The 32 revised full papers presented were carefully reviewed and selected from 95 submissions. The papers cover various aspects of integer programming and combinatorial optimization and present recent developments in theory, computation, and applications in that area. Topics included are such as approximation algorithms, branch and bound algorithms, branch and cut algorithms, computational biology, computational complexity, computational geometry, cutting plane algorithms, diophantine equations, geometry of numbers, graph and network algorithms, integer programming, matroids and submodular functions, on-line algorithms and competitive analysis, polyhedral combinatorics, randomized algorithms, random graphs, scheduling theory and scheduling algorithms, and semidefinite programs.
