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State of the Art in Global Optimization

Computational Methods and Applications

  • Book
  • © 1996

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Editors:
  1. C. A. Floudas

    1. Princeton University, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. P. M. Pardalos

    1. University of Florida, USA

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Part of the book series: Nonconvex Optimization and Its Applications (NOIA, volume 7)

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Table of contents (36 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Lagrange Duality in Partly Convex Programming

    • Sanjo Zlobec
    Pages 1-17

  3. Global Optimization Using Hyperbolic Cross Points

    • Erich Novak, Klaus Ritter
    Pages 19-33

  4. Global Minimization of Separable Concave Functions under Linear Constraints with Totally Unimodular Matrices

    • Reiner Horst, Nguyen Van Thoai
    Pages 35-45

  5. On Existence of Robust Minimizers

    • Shuzhong Shi, Quan Zheng, Deming Zhuang
    Pages 47-56

  6. A Branch and Bound Algorithm for the Quadratic Assignment Problem using a Lower Bound Based on Linear Programming

    • K. G. Ramakrishnan, Mauricio G. C. Resende, Panos M. Pardalos
    Pages 57-73

  7. Dynamic Matrix Factorization Methods for Using Formulations Derived From Higher Order Lifting Techniques in the Solution of the Quadratic Assignment Problem

    • Bala Ramachandran, J. F. Pekny
    Pages 75-92

  8. Conical Coercivity Conditions and Global Minimization on Cones. An Existence Result

    • G. Isac
    Pages 93-107

  9. The Use of Ordinary Differential Equations in Quadratic Maximization with Integer Constraints

    • Pierluigi Maponi, Maria Cristina Recchioni, Francesco Zirilli
    Pages 109-117

  10. Adaptive Control via Non-Convex Optimization

    • George H. Staus, Lorenz T. Biegler, B. Erik Ydstie
    Pages 119-137

  11. A Decomposition-Based Global Optimization Approach for Solving Bilevel Linear and Quadratic Programs

    • V. Visweswaran, C. A. Floudas, M. G. Ierapetritou, E. N. Pistikopoulos
    Pages 139-162

  12. Generalized TRUST Algorithms for Global Optimization

    • Jacob Barhen, Vladimir Protopopescu
    Pages 163-180

  13. Test Results for an Interval Branch and Bound Algorithm for Equality-Constrained Optimization

    • R. Baker Kearfott
    Pages 181-199

  14. Equivalent Methods for Global Optimization

    • Diane Maclagan, Timothy Sturge, William Baritompa
    Pages 201-211

  15. A C++ Class Library for Interval Arithmetic in Global Optimization

    • Kristina Holmqvist, Athanasios Migdalas
    Pages 213-226

  16. On the Convergence of Localisation Search

    • D. W. Bulger, G. R. Wood
    Pages 227-233

  17. Stochastic Approximation with Smoothing for Optimization of an Adaptive Recursive Filter

    • W. Edmonson, K. Srinivasan, C. Wang, J. Principe
    Pages 235-247

  18. The Grouping Genetic Algorithm

    • Emanuel Falkenauer
    Pages 249-265

  19. Accelerating Convergence of Branch-and-Bound Algorithms For Quadratically Constrained Optimization Problems

    • Tim Van Voorhis, Faiz Al-Khayyal
    Pages 267-284

  20. Distributed Decomposition-Based Approaches in Global Optimization

    • I. P. Androulakis, V. Visweswaran, C. A. Floudas
    Pages 285-301
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Keywords

About this book

Optimization problems abound in most fields of science, engineering, and tech­ nology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob­ lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver­ age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solving large classes of problems from diverse areas such as engineering design and control, computational chemistry and biology, structural optimization, computer science, operations research, and economics. This book contains refereed invited papers presented at the conference on "State of the Art in Global Optimization: Computational Methods and Applications" held at Princeton University, April 28-30, 1995. The conference presented current re­ search on global optimization and related applications in science and engineering. The papers included in this book cover a wide spectrum of approaches for solving global optimization problems and applications.

Editors and Affiliations

  • Princeton University, USA

    C. A. Floudas

  • University of Florida, USA

    P. M. Pardalos

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