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Large-Scale Optimization with Applications

Part III: Molecular Structure and Optimization

  • Book
  • © 1997

Overview

Editors:
  1. Lorenz T. Biegler

    1. Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, USA

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  2. Andrew R. Conn

    1. Thomas J. Watson Research Center, Yorktown Heights, USA

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  3. Thomas F. Coleman

    1. Computer Science Department, Cornell University, Ithaca, USA

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  4. Fadil N. Santosa

    1. School of Mathematics, University of Minnesota, Minneapolis, USA

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Part of the book series: The IMA Volumes in Mathematics and its Applications (IMA, volume 94)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xvi
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  2. CGU: An Algorithm for Molecular Structure Prediction

    • K. A. Dill, A. T. Phillips, J. B. Rosen
    Pages 1-21

  3. Potential Transformation Method for Global Optimization

    • Robert A. Donnelly
    Pages 23-45

  4. Multispace Search For Protein Folding

    • Jun Gu, Bin Du, Panos Pardalos
    Pages 47-67

  5. Modeling The Structure of Ice as a Problem in Global Minimization

    • Jan Hermans
    Pages 69-71

  6. Novel Applications of Optimization to Molecule Design

    • J. C. Meza, T. D. Plantenga, R. S. Judson
    Pages 73-97

  7. Issues in Large-Scale Global Molecular Optimization

    • Jorge J. Moré, Zhijun Wu
    Pages 99-121

  8. Global Minimization of Lennard-Jones Functions on Transputer Networks

    • Klaus Ritter, Stephen M. Robinson, Stefan Schäffler
    Pages 123-133

  9. Variable-Scale Coarse-Graining in Macromolecular Global Optimization

    • David Shalloway
    Pages 135-161

  10. Global Optimization for Molecular Clusters Using a New Smoothing Approach

    • C.-S. Shao, R. H. Byrd, E. Eskow, R. B. Schnabel
    Pages 163-199

  11. Back Matter

    Pages 201-207
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Keywords

About this book

Many important molecular conformation problems, such as protein folding, are expressed as global minimization problems. It is the fact that local minimization is insufficient, that markedly differentiates this volume from the previous two. Unfortunately, global minimization problems that result from models of molecular conformation are usually intractable. For example, simple 1-dimensional versions of distance conformation problems are NP-hard. Nevertheless, there has been significant recent progress in the design of promising heuristic strategies (often involving the use of high- performance parallel computers) for computing approximate global minimizers. The purpose of the sessions represented in this volume was to discuss the new algorithmic advances for global minimization in the context of protein folding and related molecular minimization problems. Emphasis was on practical shortcomings of current approaches, outstanding problems and questions, and the use of high-performance parallel computers.

Editors and Affiliations

  • Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, USA

    Lorenz T. Biegler

  • Thomas J. Watson Research Center, Yorktown Heights, USA

    Andrew R. Conn

  • Computer Science Department, Cornell University, Ithaca, USA

    Thomas F. Coleman

  • School of Mathematics, University of Minnesota, Minneapolis, USA

    Fadil N. Santosa

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