Quadratic programming (QP) is one advanced mathematical
technique that allows for the optimization of a quadratic function
in several variables in the presence of linear constraints. This
book presents recently developed algorithms for solving large QP
problems and 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 sequence of QP problems, it can also
be used as a convenient introduction to nonlinear programming.
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