Lagrange-type Functions in Constrained Non-Convex Optimization

Name: Lagrange-type Functions in Constrained Non-Convex Optimization
Brand: Springer-Verlag New York Inc.
SKU: 9781461348214
Price: 99.78 EUR
Availability: InStock

Autor: Alexander M. Rubinov, iao-qi Yang

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer-Verlag New York Inc.

Dostupnosť: Skladom u dodávateľa

Odosielame za 5-8 dní

99.78 €

Lagrange and penalty function methods provide a powerful approach, both as a theoretical tool and a...

Informácie o knihe

Autor

Alexander M. Rubinov, iao-qi Yang

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2013

Stránok

286

EAN

9781461348214

ISBN

1461348218

Enbook ID

05181536

Vydavateľ

Springer-Verlag New York Inc.

Hmotnosť

468

Rozmery

155 x 235 x 17

Kompletný popis

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 implementation. Thus the question arises how to generalize classical Lagrange and penalty functions, in order to obtain an appropriate scheme for reducing constrained optimiza tion problems to unconstrained ones that will be suitable for sufficiently broad classes of optimization problems from both the theoretical and computational viewpoints. Some approaches for such a scheme are studied in this book. One of them is as follows: an unconstrained problem is constructed, where the objective function is a convolution of the objective and constraint functions of the original problem. While a linear convolution leads to a classical Lagrange function, different kinds of nonlinear convolutions lead to interesting generalizations. We shall call functions that appear as a convolution of the objective function and the constraint functions, Lagrange-type functions.

Mohlo by vás zaujímať

Zákazníci, ktorí si kúpili túto knihu, kúpili tiež

Najčastejšie vyhľadávané

Categories

Authors

Publishers

Produkty

Produkty

Najčastejšie vyhľadávané

Categories

Authors

Publishers

Produkty

Produkty

Lagrange-type Functions in Constrained Non-Convex Optimization

Informácie o knihe

Kompletný popis

Mohlo by vás zaujímať

Zákazníci, ktorí si kúpili túto knihu, kúpili tiež