We survey optimization problems that involve the cardinality of variable vectors in
constraints or the objective function. We provide a unified viewpoint on the general problem …

This paper describes a new instance library for quadratic programming (QP), ie, the family of
continuous and (mixed)-integer optimization problems where the objective function and/or …

The sparse portfolio selection problem is one of the most famous and frequently studied
problems in the optimization and financial economics literatures. In a universe of risky …

We propose a unified framework to address a family of classical mixed-integer optimization
problems with logically constrained decision variables, including network design, facility …

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 present a flexible framework for general mixed-integer nonlinear programming (MINLP),
called Minotaur, that enables both algorithm exploration and structure exploitation without …

Switching-constrained optimization problems form a difficult class of mathematical
programmes since their feasible set is almost disconnected while standard constraint …

This paper studies mean-risk portfolio optimization models using the conditional value-at-
risk (CVaR) as a risk measure. We also employ a cardinality constraint for limiting the …

In recent years, the integration of Machine Learning (ML) models with Operation Research
(OR) tools has gained popularity in applications such as cancer treatment, algorithmic …

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