Nonlinear Programming: Sequential Unconstrained Minimization Techniques (Classics in Applied Mathematics, Series Number 4)

Fiacco, Anthony V.; McCormick, Garth P.

ISBN 10: 0898712548 / ISBN 13: 9780898712544

Published by Society for Industrial and Applied Mathematics, 1987

About this Item

This is a well-cared-for used book with light signs of previous use. There may be minor cover wear, a faint crease, or slight spine wear, but overall it's in great shape and fully readable.Please note:-May contain library or rental stickers.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not include original exterior box.-Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ happy reading!

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Bibliographic Details

• Title: Nonlinear Programming: Sequential Unconstrained Minimization Techniques (Classics in Applied Mathematics, Series Number 4)
• Publisher: Society for Industrial and Applied Mathematics
• Publication Date: 1987
• Language: English
• ISBN 10: 0898712548
• ISBN 13: 9780898712544
• Binding: Soft cover
• Condition: very_good

About this title

Synopsis

A reprint of the original volume, which won the Lanchester Prize awarded by the Operations Research Society of America for the best work of 1968. Although out of print for nearly 15 years, it remains one of the most referenced volumes in the field of mathematical programming. Recent interest in interior point methods generated by Karmarkar's Projective Scaling Algorithm has created a new demand for this book because the methods that have followed from Karmarkar's bear a close resemblance to those described. There is no other source for the theoretical background of the logarithmic barrier function and other classical penalty functions. Analyzes in detail the 'central' or 'dual' trajectory used by modern path following and primal/dual methods for convex and general linear programming. As researchers begin to extend these methods to convex and general nonlinear programming problems, this book will become indispensable to them.