Linear programming (LP) is an extremely useful tool which has been successfully applied to
solve various problems in a wide range of areas, including operations research …

Linear programming (LP) is used in many machine learning applications, such as $\ell_1 $-
regularized SVMs, basis pursuit, nonnegative matrix factorization, etc. Interior Point Methods …

Linear programming (LP) is an extremely useful tool and has been successfully applied to
solve various problems in a wide range of areas, including operations research …

This paper presents a black-box framework for accelerating packing optimization solvers.
Our method applies to packing linear programming problems and a family of convex …

Recently, in statistics and machine learning, the notion of Randomization in Numerical
Linear Algebra (RandNLA) has not only evolved as a vital new tool to design fast and …

We present novel, efficient algorithms for solving extremely large optimization problems. A
significant bottleneck today is that as the size of datasets grow, researchers across …

We present novel, efficient algorithms for solving extremely large optimization problems. A
significant bottleneck today is that as the size of datasets grow, researchers across …