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 consider the best subset selection problem in linear regression—that is, finding a
parsimonious subset of the regression variables that provides the best fit to the data …

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 …

This paper studies several solution paths of sparse quadratic minimization problems as a
function of the weighing parameter of the bi-objective of estimation loss versus solution …

Bayesian Networks (BNs) represent conditional probability relations among a set of random
variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse …

This paper investigates convex quadratic optimization problems involving $ n $ indicator
variables, each associated with a continuous variable, particularly focusing on scenarios …

Optimization problems involving minimization of a rank-one convex function over constraints
modeling restrictions on the support of the decision variables emerge in various machine …

We study the cone of factor-width-matrices, where the factor width of a positive semidefinite
matrix is defined as the smallest number allowing it to be expressed as a sum of positive …