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

Chaya Bleich Gurwitz

Hardcover

ISBN 10: 0656569611 ISBN 13: 9780656569618

Publisher: Forgotten Books, 2018

Synopsis

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.

Edition details

• Publisher: Forgotten Books
• Publication date: 2018
• Language: English
• ISBN 10: 0656569611
• ISBN 13: 9780656569618
• Binding: Hardcover
• Number of pages: 144