[Opt-net] Opt-net Digest, Vol 9, Issue 3

Zdeněk Dostál zdenek.dostal at vsb.cz
Wed Oct 7 15:06:54 MEST 2009


Dear colleagues,

I would greatly appreciate if you could place into the Digest the following announcement on my book.

Many thanks,

Zdenek Dostal


Z. Dostál, Technical University of Ostrava, Czech Republic
Optimal Quadratic Programming Algorithms
With Applications to Variational Inequalities
http://www.springer.com/math/book/978-0-387-84805-1


Solving optimization problems in complex systems often requires the implementation
of advanced mathematical techniques. Quadratic programming (QP) is one technique
that allows for the optimization of a quadratic function in several variables in
the presence of linear constraints. QP problems arise in fields as diverse as electrical
engineering, agricultural planning, and optics. Given its broad applicability, a comprehensive
understanding of quadratic programming is a valuable resource in nearly
every scientific field. Optimal Quadratic Programming Algorithms presents recently
developed algorithms for solving large QP problems. The presentation focuses on algorithms
which are, in a sense optimal, i.e., they can solve important classes of problems
at a cost proportional to the number of unknowns. For each algorithm presented, the
book details its classical predecessor, describes its drawbacks, introduces modifications
that improve its performance, and demonstrates these improvements through numerical
experiments. This self-contained monograph can serve as an introductory text on
quadratic programming for graduate students and researchers. Additionally, since the
solution of many nonlinear problems can be reduced to the solution of a... more on
http://springer.com/978-0-387-84805-1
▶ The first monograph to present the solution to quadratic programming problems, a
topic usually addressed only in journal publications ▶ Offers theoretical and practical
results in the field of bound-constrained and equality-constrained optimization ▶ Provides
algorithms with the rate of convergence independent of constraints ▶ Develops
theoretically supported scalable algorithms for variational inequalities ▶ Comprehensive
presentation of working set methods and inexact augmented Lagrangians
 

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