High Performance Algorithms and Software in Nonlinear Optimization
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High Performance Algorithms and Software in Nonlinear Optimization

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Description

This book contains a selection of papers presented at the conference on High Performance Software for Nonlinear Optimization (HPSN097) which was held in Ischia, Italy, in June 1997. The rapid progress of computer technologies, including new parallel architec- tures, has stimulated a large amount of research devoted to building software environments and defining algorithms able to fully exploit this new computa- tional power. In some sense, numerical analysis has to conform itself to the new tools. The impact of parallel computing in nonlinear optimization, which had a slow start at the beginning, seems now to increase at a fast rate, and it is reasonable to expect an even greater acceleration in the future. As with the first HPSNO conference, the goal of the HPSN097 conference was to supply a broad overview of the more recent developments and trends in nonlinear optimization, emphasizing the algorithmic and high performance software aspects. Bringing together new computational methodologies with theoretical ad- vances and new computer technologies is an exciting challenge that involves all scientists willing to develop high performance numerical software. This book contains several important contributions from different and com- plementary standpoints. Obviously, the articles in the book do not cover all the areas of the conference topic or all the most recent developments, because of the large number of new theoretical and computational ideas of the last few years.
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Product details

  • Hardback | 382 pages
  • 155 x 235 x 22.35mm | 1,610g
  • Dordrecht, Netherlands
  • English
  • 1999 ed.
  • X, 382 p.
  • 0792354834
  • 9780792354833

Table of contents

Preface. Some Perspectives on High-Performance Mathematical Software; D. di Serafino, et al. A Monotonous Method for Unconstrained Lipschitz Optimization; A.M. Bagirov, N.K. Gadijev. Numerical Performance of an Inexact Interior Point Algorithm; S. Bellavia. Replicator Dynamics for Evolution towards the Maximum Clique: Variations and Experiments; I.M. Bomze, F. Rendl. A Newton-Like Approach to Solving an Equilibrium Problem; V.A. Bulavski, V.V. Kalashnikov. Parallelization Strategies for the Ant System; B. Bullnheimer, et al. The Cobweb Method for Minimizing Convex Functions; V. Capalbo, et al. A New Forward Backward Auction Algorithm; R. Cerulli, et al. Modifying the Cholesky Factorization on MIMD Distributed Memory Machines; M. D'Apuzzo, et al. A Controlled Random Search Algorithm with Local Newton-type Search for Global Optimization; G. Di Pillo, et al. Adaptive Precision Control in Quadratic Programming with Simple Bounds and/or Equalities; Z. Dostal, et al. The Development of Parallel Optimisation Routines for the NAG Parallel Library; R.W. Ford, et al. Parallel Solution of Large Scale Quadratic Programs; E. Galligani, et al. A Linesearch Algorithm with Memory for Unconstrained Optimization; N.I.M. Gould, et al. The Modified Absolute-Value Factorization Norm for Trust-Region Minimization; N.I.M. Gould, J. Nocedal. The LP Dual Active Set Algorithm; W.W. Hager. The Use of Optimization Techniques for the Numerical Solution of First Kind Fredholm Equations; P. Maponi, et al. An Exact Parallel Algorithm for the Maximum Clique Problem; P.M. Pardalos, et al. A Model Development System for Global Optimization; J.D. Pinter. Support Vector Machines: A Large Scale QP; M. Pontil, et al. Orbit Determination of Binary Stars Using SimulatedAnnealing; D. Pourbaix. New Derivative Formulas for Integral and Probability Functions: Parallel Computations; S. Uryasev. The Interior-Point Revolution in Constrained Optimization; M.H. Wright.
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