Research on interior-point methods (IPMs) has dominated the field of mathematical programming since the 1980s. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity.
The iterate is then updated, with the iterates staying in a certain neighbourhood of the central path until an approximate solution to the problem is f
Jiming Peng is Professor of Mathematics at McMaster University and has published widely on nonlinear programming and interior-points methods. Cornelis Roos holds joint professorships at Delft University of Technology and Leiden University. He is an editor of several journals, coauthor of more than 100 papers, and coauthor (with Tamas Terlaky and Jean-Philippe Vial) of "Theory and Algorithms for Linear Optimization". Tamas Terlaky is Professor in the Department of Computing and Software at McMaster University, founding Editor in Chief of "Optimization and Engineering", coauthor of more than 100 papers, and an editor of several journals and two books.