Multiscale Optimization Methods and Applications

  • William W. Hager
  • Shu-Jen Huang
  • Panos M. Pardalos
  • Oleg A. Prokopyev

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 82)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Tony F. Chan, Jason Cong, Joseph R. Shinnerl, Kenton Sze, Min Xie, Yan Zhang
    Pages 1-67
  3. Pando G. Georgiev, Fabian Theis, Andrzej Cichocki
    Pages 85-99
  4. Robert Michael Lewis, Stephen G. Nash
    Pages 151-172
  5. Walter Murray, Uday V. Shanbhag
    Pages 173-204
  6. Jung-ha An, Yunmei Chen, Myron N. Chang, David Wilson, Edward Geiser
    Pages 241-250
  7. Shantanu H. Joshi, Anuj Srivastava
    Pages 299-304
  8. Christopher M. Kuster, Pierre A. Gremaud
    Pages 305-312
  9. Jiawang Nie, James W. Demmel
    Pages 313-326
  10. James M. Rath
    Pages 337-366

About this book

Introduction

As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research. The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.

Audience:
Multiscale Optimization Methods and Applications is intended for graduate students and researchers in optimization, computer science, and engineering.

Keywords

Analysis Sage VLSI algorithms automation combinatorial optimization database databases image processing linear optimization modeling nonlinear optimization optimization programming

Editors and affiliations

  • William W. Hager
    • 1
  • Shu-Jen Huang
    • 1
  • Panos M. Pardalos
    • 1
  • Oleg A. Prokopyev
    • 1
  1. 1.University of FloridaGainesville

Bibliographic information

  • DOI https://doi.org/10.1007/0-387-29550-X
  • Copyright Information Springer Science+Business Media, Inc. 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-29549-7
  • Online ISBN 978-0-387-29550-3
  • Series Print ISSN 1571-568X
  • About this book