Computational Issues in High Performance Software for Nonlinear Optimization

Author:   Almerico Murli ,  Gerardo Toraldo
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1997
ISBN:  

9781475784398


Pages:   158
Publication Date:   21 March 2013
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Computational Issues in High Performance Software for Nonlinear Optimization


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Overview

Computational Issues in High Performance Software for Nonlinear Research brings together in one place important contributions and up-to-date research results in this important area. Computational Issues in High Performance Software for Nonlinear Research serves as an excellent reference, providing insight into some of the most important research issues in the field.

Full Product Details

Author:   Almerico Murli ,  Gerardo Toraldo
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1997
Dimensions:   Width: 15.50cm , Height: 0.90cm , Length: 23.50cm
Weight:   0.266kg
ISBN:  

9781475784398


ISBN 10:   1475784392
Pages:   158
Publication Date:   21 March 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

A Comparison of Large Scale Mixed Complementarity Problem Solvers.- Impact of Partial Separability on Large-Scale Optimization.- On the Number of Inner Iterations Per Outer Iteration of a Globally Convergent Algorithm for Optimization with General Nonlinear Inequality Constraints and Simple Bounds.- Numerical Experiences with New Truncated Newton Methods in Large Scale Unconstrained Optimization.- Sparse Linear Least Squares Problems in Optimization.- Simulated Annealing and Genetic Algorithms for the Facility Layout Problem: A Survey.- Sequential Quadratic Programming Methods for Large-Scale Problems.- A Scalable Parallel Interior Point Algorithm for Stochastic Linear Programming and Robust Optimization.

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