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Author: DAO LI.. ZHU
Publisher:
ISBN:
Category :
Languages : fr
Pages : 138
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Book Description
ETUDE D'UNE METHODE GENERALE DE DECOMPOSITION EN OPTIMISATION SOUS-DIFFERENTIABLE. APRES UN RAPPEL SUR L'OPTIMISATION CONVEXE ON PRESENTE LE PRINCIPE DU PROBLEME AUXILIAIRE ET LA METHODE DE DECOMPOSITION DANS LE CAS CONVEXE ET LA FAMILLE D'ALGORITHMES CORRESPONDANTE, AINSI QU'UN EXEMPLE NUMERIQUE. ON TRAITE ENSUITE DES PROBLEMES D'OPTIMISATION CONVEXE SOUS CONTRAINTE. ON DONNE DES ALGORITHMES DE DECOMPOSITION A DEUX NIVEAUX PAR DUALITE. ON ETUDIE DES METHODES POUR ASSURER LA STABILITE DU LAGRANGIEN, AINSI QUE DES METHODES DE DECOMPOSITION DES LAGRANGIENS AUGMENTES. ON DONNE DES EXEMPLES NUMERIQUES
Author: DAO LI.. ZHU
Publisher:
ISBN:
Category :
Languages : fr
Pages : 138
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Book Description
ETUDE D'UNE METHODE GENERALE DE DECOMPOSITION EN OPTIMISATION SOUS-DIFFERENTIABLE. APRES UN RAPPEL SUR L'OPTIMISATION CONVEXE ON PRESENTE LE PRINCIPE DU PROBLEME AUXILIAIRE ET LA METHODE DE DECOMPOSITION DANS LE CAS CONVEXE ET LA FAMILLE D'ALGORITHMES CORRESPONDANTE, AINSI QU'UN EXEMPLE NUMERIQUE. ON TRAITE ENSUITE DES PROBLEMES D'OPTIMISATION CONVEXE SOUS CONTRAINTE. ON DONNE DES ALGORITHMES DE DECOMPOSITION A DEUX NIVEAUX PAR DUALITE. ON ETUDIE DES METHODES POUR ASSURER LA STABILITE DU LAGRANGIEN, AINSI QUE DES METHODES DE DECOMPOSITION DES LAGRANGIENS AUGMENTES. ON DONNE DES EXEMPLES NUMERIQUES
Author: Pierre Carpentier
Publisher: Springer
ISBN: 3662554283
Category : Mathematics
Languages : fr
Pages : 338
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Book Description
Ce livre considère le traitement de problèmes d'optimisation de grande taille. L'idée est d'éclater le problème d'optimisation global en sous-problèmes plus petits, donc plus faciles à résoudre, chacun impliquant l'un des sous-systèmes (décomposition), mais sans renoncer à obtenir l'optimum global, ce qui nécessite d'utiliser une procédure itérative (coordination). Ce sujet a fait l'objet de plusieurs livres publiés dans les années 70 dans le contexte de l'optimisation déterministe. Nous présentans ici les principes essentiels et méthodes de décomposition-coordination au travers de situations typiques, puis nous proposons un cadre général qui permet de construire des algorithmes corrects et d'étudier leur convergence. Cette théorie est présentée aussi bien dans le contexte de l'optimisation déterministe que stochastique. Ce matériel a été enseigné par les auteurs dans divers cours de 3ème cycle et également mis en œuvre dans de nombreuses applications industrielles. Des exercices et problèmes avec corrigés illustrent le potentiel de cette approche. This book discusses large-scale optimization problems involving systems made up of interconnected subsystems. The main viewpoint is to break down the overall optimization problem into smaller, easier-to-solve subproblems, each involving one subsystem (decomposition), without sacrificing the objective of achieving the global optimum, which requires an iterative process (coordination). This topic emerged in the 70’s in the context of deterministic optimization. The present book describes the main principles and methods of decomposition-coordination using typical situations, then proposes a general framework that makes it possible to construct well-behaved algorithms and to study their convergence. This theory is presented in the context of deterministic as well as stochastic optimization, and has been taught by the authors in graduate courses and implemented in numerous industrial applications. The book also provides exercises and problems with answers to illustrate the potential of this approach.
Author: Michael Patriksson
Publisher: Springer Science & Business Media
ISBN: 147572991X
Category : Mathematics
Languages : en
Pages : 343
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Book Description
Since I started working in the area of nonlinear programming and, later on, variational inequality problems, I have frequently been surprised to find that many algorithms, however scattered in numerous journals, monographs and books, and described rather differently, are closely related to each other. This book is meant to help the reader understand and relate algorithms to each other in some intuitive fashion, and represents, in this respect, a consolidation of the field. The framework of algorithms presented in this book is called Cost Approxi mation. (The preface of the Ph.D. thesis [Pat93d] explains the background to the work that lead to the thesis, and ultimately to this book.) It describes, for a given formulation of a variational inequality or nonlinear programming problem, an algorithm by means of approximating mappings and problems, a principle for the update of the iteration points, and a merit function which guides and monitors the convergence of the algorithm. One purpose of this book is to offer this framework as an intuitively appeal ing tool for describing an algorithm. One of the advantages of the framework, or any reasonable framework for that matter, is that two algorithms may be easily related and compared through its use. This framework is particular in that it covers a vast number of methods, while still being fairly detailed; the level of abstraction is in fact the same as that of the original problem statement.
Author: Alfred Auslender
Publisher:
ISBN:
Category : Decomposition method
Languages : fr
Pages : 190
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Author: Sofia Zaourar
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Decomposition methods are an application of the divide and conquer principle to large-scale optimization. Their idea is to decompose a given optimization problem into a sequence of easier subproblems. Although successful for many applications, these methods still present challenges. In this thesis, we propose methodological and algorithmic improvements of decomposition methods and illustrate them on several operations research problems. Our approach heavily relies on convex analysis and nonsmooth optimization. In constraint decomposition (or Lagrangian relaxation) applied to short-term electricity generation management, even the subproblems are too difficult to solve exactly. When solved approximately though, the obtained prices show an unstable noisy behaviour. We present a simple way to improve the structure of the prices by penalizing their noisy behaviour, in particular using a total variation regularization. We illustrate the consistency of our regularization on real-life problems from EDF. We then consider variable decomposition (or Benders decomposition), that can have a very slow convergence. With a nonsmooth optimization point of view on this method, we address the instability of Benders cutting-planes algorithm. We present an algorithmic stabilization inspired by bundle methods for convex optimization. The acceleration provided by this stabilization is illustrated on network design andhub location problems. We also study more general convex nonsmooth problems whose objective function is expensive to evaluate. This situation typically arises in decomposition methods. We show that it often exists extra information about the problem, cheap but with unknown accuracy, that is not used by the algorithms. We propose a way to incorporate this coarseinformation into classical nonsmooth optimization algorithms and apply it successfully to two-stage stochastic problems.Finally, we introduce a decomposition strategy for the machine reassignment problem. This decomposition leads to a new variant of vector bin packing problems, where the bins have variable sizes. We propose fast and efficient heuristics for this problem that improve on state of the art results of vector bin packing problems. An adaptation of these heuristics is also able to generate feasible solutions for Google instances of the machine reassignment problem.
Author: Antonio J. Conejo
Publisher: Springer Science & Business Media
ISBN: 3540276866
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning procedure. For the sake of cl- ity, theoretical concepts and computational algorithms are assembled based on these examples. The results are simplicity, clarity, and easy-learning. We feel that this book is needed by the engineering community that has to tackle complex optimization problems, particularly by practitioners and researchersinEngineering,OperationsResearch,andAppliedEconomics.The descriptions of most decomposition techniques are available only in complex and specialized mathematical journals, di?cult to understand by engineers. A book describing a wide range of decomposition techniques, emphasizing problem-solving, and appropriately blending theory and application, was not previously available.
Author: Michel Bierlaire
Publisher: EPFL Press
ISBN: 2880746698
Category : Mathematical optimization
Languages : fr
Pages : 552
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Book Description
Author: Vladimir Tsurkov
Publisher: Springer Science & Business Media
ISBN: 9780792368175
Category : Computers
Languages : en
Pages : 328
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Book Description
Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms. Audience: This book is addressed to specialists in operations research, optimization, and optimal control.
Author: Madan G. Singh
Publisher: Pergamon
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 800
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Book Description
This comprehensive reference work provides information on what systems thinking comprises and how it is being used to understand and to attack a wide spectrum of diverse problems ranging from, for example, the control of servo-mechanisms to applications of space technology.
Author: Michael Patriksson
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Book Description
