5 papers · 1 filter
Trading off 1-norm and sparsity against rank for linear models using mathematical optimization: 1-norm minimizing partially reflexive ah-symmetric generalized inverses
Marcia Fampa, Jon Lee, Gabriel Ponte
The M-P (Moore-Penrose) pseudoinverse has as a key application the computation of least-squares solutions of inconsistent systems of linear equations. Irrespective of whether a giv…
1-norm minimization and minimum-rank structured sparsity for symmetric and ah-symmetric generalized inverses: rank one and two
Luze Xu, Marcia Fampa, Jon Lee
Generalized inverses are important in statistics and other areas of applied matrix algebra. A \emph{generalized inverse} of a real matrix is a matrix that satisfies the Moo…
Mixing convex-optimization bounds for maximum-entropy sampling
Zhongzhu Chen, Marcia Fampa, Amélie Lambert +1
The maximum-entropy sampling problem is a fundamental and challenging combinatorial-optimization problem, with application in spatial statistics. It asks to find a maximum-determin…
Approximate 1-norm minimization and minimum-rank structured sparsity for various generalized inverses via local search
Luze Xu, Marcia Fampa, Jon Lee +1
Fundamental in matrix algebra and its applications, a \emph{generalized inverse} of a real matrix is a matrix that satisfies the Moore-Penrose (M-P) property . If $H…
Efficient treatment of bilinear forms in global optimization
Marcia Fampa, Jon Lee
We efficiently treat bilinear forms in the context of global optimization, by applying McCormick convexification and by extending an approach of Saxena, Bonami and Lee for symmetri…