18 citations · 45 across the 11 of their papers we have counts for
13 papers
A characterization of maximal inhomogeneous-quadratic-free sets
Gonzalo Muñoz, Joseph Paat, Felipe Serrano
The intersection cut framework is a versatile tool for generating valid inequalities in optimization. Its main ingredients are so-called -free sets: convex sets whose interiors…
Chvátal-Gomory Rounding of Eigenvector Inequalities for QCQPs
Santanu S. Dey, Nan Jiang, Aleksandr Kazachkov +2
We introduce and analyze a class of valid inequalities for nonconvex quadratically constrained optimization problems (QCQPs) which we call Eigen-CG inequalities. These inequalities…
A characterization of maximal homogeneous-quadratic-free sets
Gonzalo Muñoz, Joseph Paat, Felipe Serrano
The intersection cut framework was introduced by Balas in 1971 as a method for generating cutting planes in integer optimization. In this framework, one uses a full-dimensional con…
Compressing Branch-and-Bound Trees
Gonzalo Muñoz, Joseph Paat, Álinson S. Xavier
A branch-and-bound (BB) tree certifies a dual bound on the value of an integer program. In this work, we introduce the tree compression problem (TCP): Given a BB tree T that certif…
On obtaining the convex hull of quadratic inequalities via aggregations
Santanu S. Dey, Gonzalo Munoz, Felipe Serrano
A classical approach for obtaining valid inequalities for a set involves weighted aggregations of the inequalities that describe such set. When the set is described by linear inequ…
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse +3
The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bound is a tight, computationally tractable…
Maximal quadratic-free sets
Gonzalo Muñoz, Felipe Serrano
The intersection cut paradigm is a powerful framework that facilitates the generation of valid linear inequalities, or cutting planes, for a potentially complex set S. The key ingr…
Principled Deep Neural Network Training through Linear Programming
Daniel Bienstock, Gonzalo Muñoz, Sebastian Pokutta
Deep learning has received much attention lately due to the impressive empirical performance achieved by training algorithms. Consequently, a need for a better theoretical understa…
New Limits of Treewidth-based tractability in Optimization
Yuri Faenza, Gonzalo Muñoz, Sebastian Pokutta
Sparse structures are frequently sought when pursuing tractability in optimization problems. They are exploited from both theoretical and computational perspectives to handle compl…
Robust linear control of nonconvex battery operation in transmission systems
Daniel Bienstock, Carsten Matke, Gonzalo Munoz +1
We describe a robust multiperiod transmission plan- ning model including renewables and batteries, where battery output is used to partly offset renewable output deviations from fo…
Outer-Product-Free Sets for Polynomial Optimization and Oracle-Based Cuts
Daniel Bienstock, Chen Chen, Gonzalo Muñoz
This paper introduces cutting planes that involve minimal structural assumptions, enabling the generation of strong polyhedral relaxations for a broad class of problems. We conside…
LP approximations to mixed-integer polynomial optimization problems
Daniel Bienstock, Gonzalo Munoz
We present a class of linear programming approximations for constrained optimization problems. In the case of mixed-integer polynomial optimization problems, if the intersection gr…
On linear relaxations of OPF problems
Daniel Bienstock, Gonzalo Munoz
We present lifted linear relaxations of the OPF problem.