Sparse regression models are increasingly prevalent due to their ease of interpretability and
superior out-of-sample performance. However, the exact model of sparse regression with an …

We describe strong convex valid inequalities for conic quadratic mixed 0–1 optimization.
These inequalities can be utilized for solving numerous practical nonlinear discrete …

We study quadratic optimization with indicator variables and an M-matrix, ie, a PSD matrix
with non-positive off-diagonal entries, which arises directly in image segmentation and …

In this paper, we consider convex quadratic optimization problems with indicator variables
when the matrix Q defining the quadratic term in the objective is sparse. We use a graphical …

Motivated by modern regression applications, in this paper, we study the convexification of a
class of convex optimization problems with indicator variables and combinatorial constraints …

We consider the convex quadratic optimization problem in R n with indicator variables and
arbitrary constraints on the indicators. We show that a convex hull description of the …

We study the minimization of a rank-one quadratic with indicators and show that the
underlying set function obtained by projecting out the continuous variables is supermodular …

In this paper, we study the convex quadratic optimization problem with indicator variables.
For the 2× 2 case, we describe the convex hull of the epigraph in the original space of …

Low-rank matrix completion consists of computing a matrix of minimal complexity that
recovers a given set of observations as accurately as possible. Unfortunately, existing …

A key question in many low-rank problems throughout optimization, machine learning, and
statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply …