On Multidimensonal Disjunctive Inequalities for Chance-Constrained Stochastic Problems with Finite Support

We consider mixed-integer linear chance-constrained problems for which the random vector that parameterizes the feasible region has finite support. Our key objective is to improve branch-and-bound or -cut approaches by introducing new types of valid inequalities that improve the dual bounds and, by this, the overall performance of such methods. We introduce so-called primal-dual as … Read more

Mixed-Feature Logistic Regression Robust to Distribution Shifts

Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same time, such domains are permeated by distribution shifts, where the distribution generating the data changes between training and deployment. In this paper, we study a distributionally robust logistic regression … Read more

Inverse Optimization via Learning Feasible Regions

We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of the literature focuses on learning only the objective function, as learning the constraint function (i.e., feasible regions) leads to nonconvex training programs. Motivated by this, we focus on … Read more

A surplus-maximizing two-sided multi-period non-convex ISO auction market

Since the inception of ISOs, Locational Marginal Prices (LMPs) alone were not market clearing or incentive compatible because an auction winner who offered its avoidable costs could lose money at the LMPs. ISOs used make-whole payments to ensure that market participants did not lose money. Make-whole payments were not public, creating transparency issues. Over time, … Read more

Responsible Machine Learning via Mixed-Integer Optimization

In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice and finance. But its deployment in increasingly sophisticated, critical, and sensitive areas affecting individuals, the groups they belong to, and society as a whole raises critical concerns around fairness, transparency … Read more

Alternating Methods for Large-Scale AC Optimal Power Flow with Unit Commitment

Security-constrained unit commitment with alternating current optimal power flow (SCUC-ACOPF) is a central problem in power grid operations that optimizes commitment and dispatch of generators under a physically accurate power transmission model while encouraging robustness against component failures.  SCUC-ACOPF requires solving large-scale problems that involve multiple time periods and networks with thousands of buses within … Read more

A Graphical Global Optimization Framework for Parameter Estimation of Statistical Models with Nonconvex Regularization Functions

Optimization problems with norm-bounding constraints appear in various applications, from portfolio optimization to machine learning, feature selection, and beyond. A widely used variant of these problems relaxes the norm-bounding constraint through Lagrangian relaxation and moves it to the objective function as a form of penalty or regularization term. A challenging class of these models uses … Read more

Quadratic Convex Reformulations for MultiObjective Binary Quadratic Programming

Multiobjective binary quadratic programming refers to optimization problems involving multiple quadratic – potentially non-convex – objective functions and a feasible set that includes binary constraints on the variables. In this paper, we extend the well-established Quadratic Convex Reformulation technique, originally developed for single-objective binary quadratic programs, to the multiobjective setting. We propose a branch-and-bound algorithm … Read more

Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate

In constraint learning, we use a neural network as a surrogate for part of the constraints or of the objective function of an optimization model. However, the tractability of the resulting model is heavily influenced by the size of the neural network used as a surrogate. One way to obtain a more tractable surrogate is … Read more

The 1-persistency of the clique relaxation of the stable set polytope: a focus on some forbidden structures

A polytope $P\subseteq [0,1]^n$ is said to have the \emph{persistency} property if for every vector $c\in \R^{n}$ and every $c$-optimal point $x\in P$, there exists a $c$-optimal integer point $y\in P\cap \{0,1\}^n$ such that $x_i = y_i$ for each $i \in \{1,\dots,n\}$ with $x_i \in \{0,1\}$. In this paper, we consider a relaxation of the … Read more