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Lagrange-Type Functions in Constrained Non-Convex Optimization
Alexander M. (University Of Ballarat) Rubinov
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Description for Lagrange-Type Functions in Constrained Non-Convex Optimization
Paperback. Series: Applied Optimization. Num Pages: 300 pages, biography. BIC Classification: KJT; PBMW; PBU. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 16. Weight in Grams: 468.
Lagrange and penalty function methods provide a powerful approach, both as a theoretical tool and a computational vehicle, for the study of constrained optimization problems. However, for a nonconvex constrained optimization problem, the classical Lagrange primal-dual method may fail to find a mini mum as a zero duality gap is not always guaranteed. A large penalty parameter is, in general, required for classical quadratic penalty functions in order that minima of penalty problems are a good approximation to those of the original constrained optimization problems. It is well-known that penaity functions with too large parameters cause an obstacle for numerical ... Read more
Lagrange and penalty function methods provide a powerful approach, both as a theoretical tool and a computational vehicle, for the study of constrained optimization problems. However, for a nonconvex constrained optimization problem, the classical Lagrange primal-dual method may fail to find a mini mum as a zero duality gap is not always guaranteed. A large penalty parameter is, in general, required for classical quadratic penalty functions in order that minima of penalty problems are a good approximation to those of the original constrained optimization problems. It is well-known that penaity functions with too large parameters cause an obstacle for numerical ... Read more
Product Details
Format
Paperback
Publication date
2013
Publisher
Springer-Verlag New York Inc. United States
Number of pages
300
Condition
New
Series
Applied Optimization
Number of Pages
286
Place of Publication
New York, NY, United States
ISBN
9781461348214
SKU
V9781461348214
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Lagrange-Type Functions in Constrained Non-Convex Optimization
From the reviews: "Lagrange and penalty functions provide a powerful approach for study of constrained optimization problems. … The book gives a systematic and unified presentation of many important results that have been obtained in this area during last several years. … The book develops a unified approach to duality and penalization and to convergence ... Read more