Inherently Parallel Algorithms in Feasibility and Optimization and Their Applications

Volume 8

By: D. Butnariu, S. Reich, Y. Censor

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Hardcover | 18 June 2001

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516 Pages

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24.13 x 17.15 x 2.54

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The Haifa 2000 Workshop on "Inherently Parallel Algorithms for Feasibility and Optimization and their Applications" brought together top scientists in this area. The objective of the Workshop was to discuss, analyze and compare the latest developments in this fast growing field of applied mathematics and to identify topics of research which are of special interest for industrial applications and for further theoretical study.

Inherently parallel algorithms, that is, computational methods which are, by their mathematical nature, parallel, have been studied in various contexts for more than fifty years. However, it was only during the last decade that they have mostly proved their practical usefulness because new generations of computers made their implementation possible in order to solve complex feasibility and optimization problems involving huge amounts of data via parallel processing. These led to an accumulation of computational experience and theoretical information and opened new and challenging questions concerning the behavior of inherently parallel algorithms for feasibility and optimization, their convergence in new environments and in circumstances in which they were not considered before their stability and reliability. Several research groups all over the world focused on these questions and it was the general feeling among scientists involved in this effort that the time has come to survey the latest progress and convey a perspective for further development and concerted scientific investigations. Thus, the editors of this volume, with the support of the Israeli Academy for Sciences and Humanities, took the initiative of organizing a Workshop intended to bring together the leading scientists in the field. The current volume is the Proceedings of the Workshop representing the discussions, debates and communications that took place. Having all that information collected in a single book will provide mathematicians and engineers interested in the theoretical and practical aspects of the inherently parallel algorithms for feasibility and optimization with a tool for determining when, where and which algorithms in this class are fit for solving specific problems, how reliable they are, how they behave and how efficient they were in previous applications. Such a tool will allow software creators to choose ways of better implementing these methods by learning from existing experience.

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You Can Find This Book In

Non-Fiction Mathematics Algebra Computing & I.T.Computer Science Mathematical Theory of Computation Reference, Information & Interdisciplinary Subjects Research & Information Information theory Applied Mathematics Medicine Medicine in General

Optimisation Linear Programming Computer Programming & Software Development

Preface
A log-quadratic projection method for convex feasibility problems
Projection algorithms: Results and open problems
Joint and separate convexity of the bregman distance
A parallel algorithm for non-cooperative resource allocation games
Asymptotic behavior of quasi-nonexpansive mappings
The outer bregman projection method for stochastic feasibility problems in banach spaces
Bregman-legendre multidistance projection algorithms for convex feasibility and optimization
Averaging strings of sequential iterations for convex feasibility problems
Quasi-fejerian analysis of some optimization algorithms
On the theory and practice of row relaxation methods
From parallel to sequential projection methods and vice versa in convex feasibility: Results and conjectures
Accelerating the convergence of the method of alternating projections via line search: A brief survey
PICO: An object-oriented framework for parallel branch and bound
Approaching equilibrium in parallel
Generic convergence of algorithms for solving stochastic feasibility problems
Superlinear rate of convergence and optimal acceleration schemes in the solution of convex inequality problems
Algebraic reconstruction techniques using smooth basis functions for helical cone-beam tomography
Compact operators as products of projections
Parallel subgradient methods for convex optimization
Directional halley and quasi-halley methods in N variables
Ergodic convergence to a zero of the extended sum of two maximal monotone operators
Distributed asynchronous incremental subgradient methods
Random algorithms for solving convex inequalities
Parallel iterative methods for sparse linear systems
On the relation between bundle methods for maximal monotone inclusions and hybrid proximal point algorithms
New optimized and accelerated PAM methods for solving large non-symmetric linear systems: Theory and practice
The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings
Table of Contents provided by Publisher. All Rights Reserved.

Inherently Parallel Algorithms in Feasibility and Optimization and Their Applications

Volume 8

At a Glance

Hardcover

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