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 …

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 …

The problem of inferring Markov random fields (MRFs) with a sparsity or robustness prior
can be naturally modeled as a mixed-integer program. This motivates us to study a general …

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 …

In many applications, when building linear regression models, it is important to account for
the presence of outliers, ie, corrupted input data points. Such problems can be formulated as …

We introduce the framework of slowly varying regression under sparsity, which allows
sparse regression models to vary slowly and sparsely. We formulate the problem of …

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 …

Optimization challenges necessitate the development of strategies to address computational
complexity, aiming to increase efficiency, reduce expenses, or improve the allocation and …

Multi-objective programming problem often contains numerous efficient solutions, which
confuses the decision-maker. To assist in selecting the most desirable solution, optimizing a …