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 ...
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 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.
Read Less
Add this copy of Lagrange-Type Functions in Constrained Non-Convex to cart. $47.24, good condition, Sold by PlumCircle rated 5.0 out of 5 stars, ships from West Mifflin, PA, UNITED STATES, published 2003 by Springer.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Good. Somewhat damaged. May have bumped corner, torn or missing dust cover, folded pages, light dust soil, remainder mark, price sticker, other damage, or be bent. 99% of orders arrive in 4-10 days.
Add this copy of Lagrange-Type Functions in Constrained Non-Convex to cart. $100.75, very good condition, Sold by Reuseabook rated 4.0 out of 5 stars, ships from Gloucester, GLOS, UNITED KINGDOM, published 2014 by Springer.
Edition:
Softcover reprint of the original 1st edition 2003
Publisher:
Springer-Verlag New York Inc.
Published:
09/12/2014
Language:
English
Alibris ID:
18023332453
Shipping Options:
Standard Shipping: $4.99
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine.
Add this copy of Lagrange-type Functions in Constrained Non-Convex to cart. $103.32, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2013 by Springer-Verlag New York Inc..
Add this copy of Lagrange-Type Functions in Constrained Non-Convex to cart. $103.32, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2003 by Springer.