Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix (Classic Reprint) - Couverture souple

Chaya Bleich Gurwitz

9781332194698: Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix (Classic Reprint)

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ISBN 10 : 1332194699 ISBN 13 : 9781332194698

Editeur : Forgotten Books, 2018

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Discover how projected Hessian updates drive efficient, reliable optimization in constrained problems.

This book explains sequential quadratic programming (SQP) methods that use an approximation to second-derivative information projected onto the constraint tangent space. It focuses on maintaining a positive definite representation in the reduced space, which helps stabilize updates and enable standard quasi-Newton techniques. The text covers how active and working sets are identified and how the projected Hessian guides subproblem formulations and iterations.

- How the projected Hessian enters optimality conditions and how to form and update it in practice
- Ways to manage the working set, including dropping criteria and their impact on convergence
- Update rules and safeguards that keep the projected Hessian well-behaved and numerically stable
- Convergence results for a restricted class of problems and practical insights from numerical results

Ideal for readers who want a rigorous, implementation-conscious view of SQP methods and the role of projected Hessian information in constrained optimization.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Forgotten Books
Date d'�dition: 2018
Langue: anglais
ISBN 10: 1332194699
ISBN 13: 9781332194698
Reliure: Broch�
Nombre de pages: 143
Coordonn�es du fabricant: Copytech (UK) Ltd, Trading as Printondemand-worldwide.com
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Sequential Quadratic Programming Methods Based on Approximating a Projected

Chaya Bleich Gurwitz

Edit� par Forgotten Books, 2018

ISBN 10 : 1332194699 ISBN 13 : 9781332194698

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Paperback. Etat : New. Print on Demand. This book explores an alternative to traditional sequential quadratic programming (SQP) methods for solving complex optimization problems with nonlinear constraints. The author discusses the strengths and shortcomings of the projected Hessian (PH) approach in the context of SQP frameworks, providing both theoretical and practical insights. The main focus is on developing SQP methods with projected Hessian approximations that maintain an approximation to the Hessian matrix projected onto the tangent space of the constraints. The approach aims to reduce the computational cost of updating the Hessian matrix and to enhance the stability and convergence properties of SQP methods, particularly for inequality constrained problems. The book includes numerical results and detailed descriptions of the proposed algorithms and their implementation, providing valuable resources for researchers and practitioners working in optimization theory and applications. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. N� de r�f. du vendeur 9781332194698_0

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