Neural networks are a computing paradigm that is finding increasing attention among
computer scientists. In this book, theoretical laws and models previously scattered in the …

Linear programming has been the dominant paradigm in optimization since Dantzig's
development of the simplex method in the 1940s. In 1984, the publication of a paper by …

This book provides a solid foundation and an extensive study for an important class of
constrained optimization problems known as Mathematical Programs with Equilibrium …

Just as in its 1st edition, this book starts with illustrations of the ubiquitous character of
optimization, and describes numerical algorithms in a tutorial way. It covers fundamental …

The modern era of interior-point methods dates to 1984, when Karmarkar proposed his
algorithm for linear programming. In the years since then, algorithms and software for linear …

The first comprehensive review of the theory and practice of one oftoday's most powerful
optimization techniques. The explosive growth of research into and development of …

This article provides a synopsis of the major developments in interior point methods for
mathematical programming in the last thirteen years, and discusses current and future …

This note summarizes a report with the same title, where a study was carried out regarding a
unified approach, proposed by Kojima, Mizuno and Yoshise, for interior point algorithms for …

The SDLCP (semidefinite linear complementarity problem) in symmetric matrices introduced
in this paper provides a unified mathematical model for various problems arising from …

As in many primal—dual interior-point algorithms, a primal—dual infeasible-interior-point
algorithm chooses a new point along the Newton direction towards a point on the central …