From Linear to Semidefinite Programming, or: Using Noncommutativity for Optimization

Konrad Schrempf (Redner*in)

Aktivität: Gespräch oder VortragGeladener Gastvortrag


Linear Programming (LP) is a well known theory for optimization and easy to apply, many implementations of the classical Simplex tableau or interior point methods are available. Semidefinite Programming (SDP) is its natural generalization by using matrices instead of vectors and linear matrix-inequalities (LMIs) instead of classical inequalities. Quadratic Programming (QP) and optimization with zero-one-variables can be treated with SDP. I will give a brief introduction to the basic concepts and illustrate the formulation of a small problem to be able to use one of the existing SDP solvers.
Zeitraum24 Okt 2018
Gehalten amUniversity of Saarbrücken, Österreich