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Large Scale Optimization : State of the Art - William W. Hager

Large Scale Optimization

State of the Art

By: William W. Hager (Editor), Donald W. Hearn (Editor), Panos M. Pardalos (Editor)


Published: 31st May 1994
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On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con­ ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program­ ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At­ tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com­ puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abroad. Accurate modeling of scientific problems often leads to the formulation of large­ scale optimization problems involving thousands of continuous and/or discrete vari­ ables. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 1960's, are now implement able in distributed process­ ing systems, and today many optimization codes have been implemented on parallel machines.

Restarting Strategies for the DQA Algorithmp. 1
Mathematical Equivalence of the Auction Algorithm for Assignment and the [epsilon]-Relaxation (Preflow-Push) Method for Min Cost Flowp. 26
Preliminary Computational Experience with Modified Log-Barrier Functions for Large-Scale Nonlinear Programmingp. 45
A New Stochastic/Perturbation Method for Large-Scale Global Optimization and its Application to Water Cluster Problemsp. 68
Improving the Decomposition of Partially Separable Functions in the Context of Large-Scale Optimization: a First Approachp. 82
Gradient-Related Constrained Minimization Algorithms in Function Spaces: Convergence Properties and Computational Implicationsp. 95
Some Reformulations and Applications of the Alternating Direction Method of Multipliersp. 115
Experience with a Primal Presolve Algorithmp. 135
A Trust Region Method for Constrained Nonsmooth Equationsp. 155
On the Complexity of a Column Generation Algorithm for Convex or Quasiconvex Feasibility Problemsp. 182
Identification of the Support of Nonsmoothnessp. 192
On Very Large Scale Assignment Problemsp. 206
Numerical Solution of Parabolic State Constrained Control Problems using SQP- and Interior-Point-Methodsp. 245
A Global Optimization Method For Weber's Problem With Attraction and Repulsionp. 259
Large-Scale Diversity Minimization via Parallel Genetic Algorithmsp. 294
A Numerical Comparison of Barrier and Modified Barrier Methods for Large-Scale Bound-Constrained Optimizationp. 319
A Numerical Study of Some Data Association Problems Arising in Multitarget Trackingp. 339
Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Methodp. 362
Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithmsp. 388
A Simple, Quadratically Convergent Interior Point Algorithm for Linear Programming and Convex Quadratic Programmingp. 411
On Two Algorithms for Nonconvex Nonsmooth Optimization Problems in Structural Mechanicsp. 428
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792327981
ISBN-10: 0792327985
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 456
Published: 31st May 1994
Country of Publication: NL
Dimensions (cm): 23.39 x 15.6  x 2.69
Weight (kg): 0.84