M
Michael J. Todd
Researcher at Arizona State University
Publications - 300
Citations - 17253
Michael J. Todd is an academic researcher from Arizona State University. The author has contributed to research in topics: Linear programming & Interior point method. The author has an hindex of 58, co-authored 295 publications receiving 15721 citations. Previous affiliations of Michael J. Todd include Université catholique de Louvain & University of Connecticut Health Center.
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SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
TL;DR: In this article, a MATLAB implementation of infeasible path-following algorithms for solving standard semidefinite programs (SDP) is presented, and Mehrotra-type predictor-corrector variants are included.
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Solving semidefinite-quadratic-linear programs using SDPT3
TL;DR: Computational experiments with linear optimization problems involving semidefinite, quadratic, and linear cone constraints (SQLPs) are discussed and computational results on problems from the SDPLIB and DIMACS Challenge collections are reported.
SDPT3 -- A Matlab Software Package for Semidefinite Programming
TL;DR: This invention relates to stabilizing compositions that comprises a vinyl chloride or vinylidene chloride homopolymer or copolymer and a stabilizing amount of an organotin halide exhibiting the formula RSnX3.
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Self-scaled barriers and interior-point methods for convex programming
Yu. Nesterov,Michael J. Todd +1 more
TL;DR: This paper provides a theoretical foundation for efficient interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled, with long-step and symmetric primal-dual methods.
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Primal-Dual Interior-Point Methods for Self-Scaled Cones
Yu. Nesterov,Michael J. Todd +1 more
TL;DR: This paper presents efficiency estimates for several symmetric primal-dual methods that can loosely be classified as path-following methods for convex programming problems expressed in conic form when the cone and its associated barrier are self-scaled.