Semidefinite Programming
The field of Semidefinite Programming (SDP) or Semidefinite Optimization (SDO) deals with optimization problems over symmetric positive semidefinite matrix variables with
linear cost function and linear constraints. Popular special
cases are linear programming and convex quadratic programming with
convex quadratic constraints. This page collected links to
papers, software, announcements, etc. that were of relevance for
people working in Semidefinite Programming in its initial phase.
Today SDP is an established basic optimization technique with applications in
a multitude of scientific fields. With the increasing number of publications it became impossible to keep track of all relevant developments. In addition, the need for such a page deminished with the availability of strong search engines, so this page is no longer updated. In the hope that it still serves to illustrate the historic development of the field it is, however, still available here.
Contents
(dd.mm.year) gave the date of the last change in the section.