Gabor Pataki, UNC-Chapel Hill, Bad semidefinite programs, linear algebra, and short proofs
SAS 4201Semidefinite programs (SDPs) -- optimization problems with linear constraints, linear objective, and semidefinite matrix variables -- are some of the most useful, versatile, and pervasive optimization problems to emerge in the last 30 years. They find applications in combinatorial optimization, machine learning, and statistics, to name just a few areas. Unfortunately, SDPs often behave pathologically: the optimal values of the primal…