Handbook of Applied Optimization

Panos M. Pardalos (Redaktør) ; Mauricio G. C. Resende (Redaktør)

«The editors draw on the expertise of researchers and application specialists from throughout the world."--Choice "This reference provides a guide for applications specialists to the most important instruments and the major recent advances in the field of applied optimization. Pardalos (industrial and systems engineering, U. of Florida) and Resende (research scientist, AT&T Laboratories) present 26 chapters in which expert contributors discuss algorithms (linear, semidefinite, quadratic, nonlinear, stochastic, and integer programming), combinatorial optimization, deterministic global optimization, decomposition methods for mathematical programming, network and hierarchical optimization, artificial neural networks and parallel algorithms in optimization, complementary and related problems, data envelopment analysis, and randomization in discrete optimization. They also cover applications (problem types, application areas) and software."--SciTech Book News»

This text focuses on the algorithmic and computational aspects of optimization, particularly the methods for solving a wide range of decision-making problems. It covers optimization problems in a wide range of settings, from the airline and aerospace industries to telecommunications, finance, health systems, biomedicine, and engineering. Les mer
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This text focuses on the algorithmic and computational aspects of optimization, particularly the methods for solving a wide range of decision-making problems. It covers optimization problems in a wide range of settings, from the airline and aerospace industries to telecommunications, finance, health systems, biomedicine, and engineering.

Fakta

Innholdsfortegnelse

PrefacePanos M. Pardalos and Mauricio G. C. Resende:
IntroductionPanos M. Pardalos and Mauricio G. C. Resende:
Part One: Algorithms
1: Linear Programming
1.1: Tamás Terlaky: Introduction
1.2: Tamás Terlaky: Simplex-Type Algorithms
1.3: Kees Roos: Interior-Point Methods for Linear Optimization
2: Henry Wolkowicz: Semidefinite Programming
3: Combinatorial Optimization
3.1: Panos M. Pardalos and Mauricio G. C. Resende: Introduction
3.2: Eva K. Lee: Branch-and-Bound Methods
3.3: John E. Mitchell: Branch-and-Cut Algorithms for Combinatorial Optimization Problems
3.4: Augustine O. Esogbue: Dynamic Programming Approaches
3.5: Mutsunori Yagiura and Toshihide Ibaraki: Local Search
3.6: Metaheuristics
3.6.1: Bruce L. Golden and Edward A. Wasil: Introduction
3.6.2: Éric D. Taillard: Ant Systems
3.6.3: John E. Beasley: Population Heuristics
3.6.4: Pablo Moscato: Memetic Algorithms
3.6.5: Leonidas S. Pitsoulis and Mauricio G. C. Resende: Greedy Randomized Adaptive Search Procedures
3.6.6: Manuel Laguna: Scatter Search
3.6.7: Fred Glover and Manuel Laguna: Tabu Search
3.6.8: E. H. L. Aarts and H. M. M. Ten Eikelder: Simulated Annealing
3.6.9: Pierre Hansen and Nenad Mladenovi'c: Variable Neighborhood Search
4: Yinyu Ye: Quadratic Programming
5: Nonlinear Programming
5.1: Gianni Di Pillo and Laura Palagi: Introduction
5.2: Gianni Di Pillo and Laura Palagi: Unconstrained Nonlinear Programming
5.3: Constrained Nonlinear Programming }a Gianni Di Pillo and Laura Palagi
5.4: Manlio Gaudioso: Nonsmooth Optimization
6: Christodoulos A. Floudas: Deterministic Global Optimizatio and Its Applications
7: Philippe Mahey: Decomposition Methods for Mathematical Programming
8: Network Optimization
8.1: Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Introduction
8.2: Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Maximum Flow Problem
8.3: Edith Cohen: Shortest-Path Algorithms
8.4: S. Thomas McCormick: Minimum-Cost Single-Commodity Flow
8.5: Pierre Chardaire and Abdel Lisser: Minimum-Cost Multicommodity Flow
8.6: Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin: Minimum Spanning Tree Problem
9: Integer Programming
9.1: Nelson Maculan: Introduction
9.2: Nelson Maculan: Linear 0-1 Programming
9.3: Yves Crama and peter L. Hammer: Psedo-Boolean Optimization
9.4: Christodoulos A. Floudas: Mixed-Integer Nonlinear Optimization
9.5: Monique Guignard: Lagrangian Relaxation
9.6: Arne Løokketangen: Heuristics for 0-1 Mixed-Integer Programming
10: Theodore B. Trafalis and Suat Kasap: Artificial Neural Networks in Optimization and Applications
11: John R. Birge: Stochastic Programming
12: Hoang Tuy: Hierarchical Optimization
13: Michael C. Ferris and Christian Kanzow: Complementarity and Related Problems
14: José H. Dulá: Data Envelopment Analysis
15: Yair Censor and Stavros A. Zenios: Parallel Algorithms in Optimization
16: Sanguthevar Rajasekaran: Randomization in Discrete Optimization: Annealing Algorithms
Part Two: Applications
17: Problem Types
17.1: Chung-Yee Lee and Michael Pinedo: Optimization and Heuristics of Scheduling
17.2: John E. Beasley, Abilio Lucena, and Marcus Poggi de Aragão: The Vehicle Routing Problem
17.3: Ding-Zhu Du: Network Designs: Approximations for Steiner Minimum Trees
17.4: Edward G. Coffman, Jr., János Csirik, and Gerhard J. Woeginger: Approximate Solutions to Bin Packing Problems
17.5: Rainer E. Burkard: The Traveling Salesmand Problem
17.6: Dukwon Kim and Boghos D. Sivazlian: Inventory Management
17.7: Zvi Drezner: Location
17.8: Jun Gu, Paul W. Purdom, John Franco, and Benjamin W. Wah: Algorithms for the Satisfiability (SAT) Problem
17.9: Eranda Çela: Assignment Problems
18: Application Areas
18.1: Warren B. Powell: Transportation and Logistics
18.2: Gang Yu and Benjamin G. Thengvall: Airline Optimization
18.3: Alexandra M. Newman, Linda K. Nozick, and Candace Arai Yano: Optimization in the Rail Industry
18.4: Andrés Weintraub Pohorille and John Hof: Forstry Industry
18.5: Stephen C. Graves: Manufacturing Planning and Control
18.6: Robert C. Leachman: Semiconductor Production Planning
18.7: Matthew E. Berge, John T. Betts, Sharon K. Filipowski, William P. Huffman, and David P. Young: Optimization in the Aerospace Industry
18.8: Energy
18.8.1: Gerson Couto de Oliveira, Sergio Granville, and Mario Pereira: Optimization in Electrical Power Systems
18.8.2: Roland N. Horne: Optimization Applications in Oil and Gas Recovery
18.8.3: Roger Z. Rios-Mercado: Natural Gas Pipeline Optimization
18.9: G. Anandalingam: Opimization of Telecommunications Networks
18.10: Stanislav Uryasev: Optimization of Test Intervals in Nuclear Engineering
18.11: Hussein A. Y. Etawil and Anthony Vannelli: Optimization in VLSI Design: Target Distance Models for Cell Placement
18.12: Michael Florian and Donald W. Hearn: Optimization Models in Transportation Planning
18.13: Guoliang Xue: Optimization in computation Molecular Biology
18.14: Anna Nagurney: Optimization in the Financial Services Industry
18.15: J. B. Rosen, John H. Glick, and E. Michael Gertz: Applied Large-Scale Nonlinear Optimization for Optimal Control of Partial Differential Equations and Differential Algebraic Equations
18.16: Kumaraswamy Ponnambalam: Optimization in Water Reservoir Systems
18.17: Ivan Dimov and Zahari Zlatev: Optimization Problems in Air-Pollution Modeling
18.18: Charles B. Moss: Applied Optimization in Agriculture
18.19: Petra Mutzel: Optimization in Graph Drawing
18.20: G. E. Stavroulakis: Optimization for Modeling of Nonlinear Interactions in Mechanics
Part Three: Software
19: Emmanuel Fragnière and Jacek Gondzio: Optimization Modeling Languages
20: Stephen J. Wright: Optimization Software Packages
21: Andreas Fink, Stefan VoB, and David L. Woodruff: Optimization Software Libraries
22: John E. Beasley: Optimization Test Problem Libraries
23: Simone de L. Martins, Celso C. Ribeiro, and Noemi Rodriguez: Parallel Computing Environment
24: Catherine C. McGeoch: Experimental Analysis of Optimization Algorithms
25: Andreas Fink, Stefan VoB, and David L. Woodruff: Object-Oriented Programming
26: Michael A. Trick: Optimization and the Internet
Directory of Contributors
Index