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This paper discusses computational experiments with linear optimization problems involving semidefinite, quadratic, and linear cone constraints (SQLPs). Many test problems of this type are solved using a new release of SDPT3, a Matlab implementation of infeasible primal-dual path-following algorithms. The software developed by the authors uses Mehrotra-type… (More)

In this paper we continue the development of a theoretical foundation for efficient primal-dual interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled (see [NT97]). The class of problems under consideration includes linear programming, semidefinite programming and convex… (More)

Interior point methods were widely used in the past in the form of barrier methods. In linear programming, the simplex method dominated, mainly due to inefficiencies of barrier methods. Interior point methods became quit popular again after 1984, when Karmarkar announced a fast polynomial-time interior method for nonlinear programming [Karmarkar, 1984]. In… (More)

- J S Marron, Michael J Todd, Jeongyoun Ahn
- 2002

High Dimension Low Sample Size statistical analysis is becoming increasingly important in a wide range of applied contexts. In such situations, it is seen that the popular Support Vector Machine suffers from " data piling " at the margin, which can diminish generalizability. This leads naturally to the development of Distance Weighted Discrimination, which… (More)

- Arkadi S Nemirovski, Michael J Todd
- 2008

This article describes the current state of the art of interior-point methods (IPMs) for convex, conic, and general nonlinear optimization. We discuss the theory, outline the algorithms, and comment on the applicability of this class of methods, which have revolutionized the field over the last twenty years.

In February 1979 a note by L. G. Khachiyan indicated how an ellipsoid method for linear programming can be implemented in polynomial time. This result has caused great excitement and stimulated a flood of technical papers. Ordinarily there would be no need for a survey of work so recent, but the current circumstances are obviously exceptional. Word of… (More)

We show the linear convergence of a simple first-order algorithm for the minimum-volume enclosing ellipsoid problem and its dual, the D-optimal design problem of statistics. Computational tests confirm the attractive features of this method.