Sequential Quadratic Programming Methods Based on Indefinite Hessian Approximations
Author : Meredith J. Goldsmith
Publisher :
Page : 125 pages
File Size : 12,47 MB
Release : 1999
Category :
ISBN :
Author : Meredith J. Goldsmith
Publisher :
Page : 125 pages
File Size : 12,47 MB
Release : 1999
Category :
ISBN :
Author :
Publisher :
Page : 222 pages
File Size : 45,20 MB
Release : 2015
Category :
ISBN :
Author : Chaya Bleich Gurwitz
Publisher : Forgotten Books
Page : 142 pages
File Size : 43,35 MB
Release : 2018-02-14
Category : Mathematics
ISBN : 9780656569618
Excerpt from Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix We are interested in developing sequential quadratic programming methods which maintain an approximation to second derivative information projected onto the tangent space of the constraints. The main motivation for our work is that only the projected matrix enters into the optimality conditions for the nonlinear problem. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author : Chaya Bleich Gurwitz
Publisher :
Page : 0 pages
File Size : 19,20 MB
Release : 2022-10-27
Category : History
ISBN : 9781016857901
Author : Chaya Bleich Gurwitz
Publisher :
Page : 274 pages
File Size : 17,46 MB
Release : 1986
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ISBN :
Author :
Publisher :
Page : 91 pages
File Size : 19,97 MB
Release : 1992
Category :
ISBN :
The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
Author : Jorge Nocedal
Publisher : Springer Science & Business Media
Page : 686 pages
File Size : 31,85 MB
Release : 2006-12-11
Category : Mathematics
ISBN : 0387400656
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
Author : Stanford University. Department of Operations Research. Systems Optimization Laboratory
Publisher :
Page : 98 pages
File Size : 26,78 MB
Release : 1992
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Author : Arieh Iserles
Publisher : Cambridge University Press
Page : 584 pages
File Size : 16,18 MB
Release : 2005-06-30
Category : Mathematics
ISBN : 9780521858076
A high-impact factor, prestigious annual publication containing invited surveys by subject leaders: essential reading for all practitioners and researchers.
Author : Chaya Bleich Gurwitz
Publisher : Nabu Press
Page : 138 pages
File Size : 31,90 MB
Release : 2013-10
Category :
ISBN : 9781293063996
This is a reproduction of a book published before 1923. This book may have occasional imperfections such as missing or blurred pages, poor pictures, errant marks, etc. that were either part of the original artifact, or were introduced by the scanning process. We believe this work is culturally important, and despite the imperfections, have elected to bring it back into print as part of our continuing commitment to the preservation of printed works worldwide. We appreciate your understanding of the imperfections in the preservation process, and hope you enjoy this valuable book.