Nonlinear optimization theory and algorithms : (the proceedings of an international summer school held at University of Bergamo, Italy, in September 1979 and repeated at the Hatfield Polytechnic, England, in July 1980)

Cover of: Nonlinear optimization |

Published by Birkhauser Boston in Cambridge, MA .

Written in English

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  • Mathematical optimization -- Congresses.,
  • Nonlinear theories -- Congresses.

Edition Notes

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Book details

Statementedited by L.C.W. Dixon, E. Spedicato, G.P. Szegö.
ContributionsDixon, L. C. W., Spedicato, E., Szegö, G. P. 1934-
LC ClassificationsQA402.5
ID Numbers
Open LibraryOL21854318M
ISBN 103764330201

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This outstanding book fills the need for a recent introductory graduate textbook in nonlinear convex optimization. The book is divided into 2 parts: Part I deals with theory while Part II deals with algorithms for nonlinear convex optimization.

Topics covered in Part I include basic convex analysis, optimality conditions, and Lagrangian by: This outstanding book fills the need for a recent introductory graduate textbook in nonlinear convex optimization. The book is divided into 2 parts: Part I deals with theory while Part II deals with algorithms for nonlinear convex optimization.

Topics covered in Part I include basic convex analysis, optimality conditions, and Lagrangian duality/5(7). is a platform for academics to share research papers.

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Vanderbei, Princeton University, author of Linear Programming. Local vs. Global optimum subject to: λ1 +λ2 +λ3 =1, λ1 ≥0, λ2 ≥0, λ3 ≥0. This is a nonlinear program in three variables λ1, λ2, and λ3. There are alternative ways to approach this Size: 1MB. This textbook on nonlinear optimization focuses on model building, real world problems, and applications of optimization models to natural and social sciences.

Organized into two sections, this book may be used as a primary text for courses on convex optimization and non-convex optimization. The book is intended as a reference work for advanced research in the field of optimization theory and methods. Keywords global optimization interior point methods linear optimization nonlinear optimization nonlinear programming nonsmooth optimization optimization sequential quadratic programming.

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This book provides an up-to-date, comprehensive, and rigorous account of nonlinear programming at the first year graduate student level. It covers descent algorithms for unconstrained and constrained optimization, Lagrange multiplier theory, interior point and augmented Lagrangian methods for linear and nonlinear programs, duality theory, and major aspects of large-scale optimization.

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An introduction to algorithms for nonlinear optimization 17 Modified-Newton methods Of course, away from a local minimizer there is no reason to believe that H k will be positive definite. The thing that matters is that a nonlinear program incorporates in any event one nonlinear function, which could be the target function or a few or the majority of the constraints.

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Convex vs. Nonconvex Optimization Probs Nonlinear Programming (NLP) minimize f(x) subject to h i(x)= 0, i ∈ E, h i(x)≥ 0, i ∈ I. NLP is convex if • h i ’s in equality constraints are affine; • h i ’s in inequality constraints are concave; • f is convex; NLP is smooth if • All are twice continuously Size: KB.

(This is a live list. Edits and additions welcome) Lecture notes: Highly recommended: video lectures by Prof. Boyd at Stanford, this is a rare case where watching live lectures is better than reading a book.

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As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations.

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