1.2k citations · 1.6k across the 35 of their papers we have counts for
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Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization
Gabriele Dragotto, Stefan Clarke, Jaime Fernández Fisac +1
We consider the problem of solving a family of parametric mixed-integer linear optimization problems where some entries in the input data change. We introduce the concept of cuttin…
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization
Theo Diamandis, Zachary Frangella, Shipu Zhao +2
We introduce the GEneralized Newton Inexact Operator Splitting solver (GeNIOS) for large-scale convex optimization. GeNIOS speeds up ADMM by approximately solving approximate subpr…
Learning to Warm-Start Fixed-Point Optimization Algorithms
Rajiv Sambharya, Georgina Hall, Brandon Amos +1
We introduce a machine-learning framework to warm-start fixed-point optimization algorithms. Our architecture consists of a neural network mapping problem parameters to warm starts…
Learning Decision-Focused Uncertainty Sets in Robust Optimization
Irina Wang, Bart Van Parys, Bartolomeo Stellato
We propose a data-driven technique to automatically learn contextual uncertainty sets in robust optimization, resulting in excellent worst-case and average-case performance while a…
Learning Rationality in Potential Games
Stefan Clarke, Gabriele Dragotto, Jaime Fernández Fisac +1
We propose a stochastic first-order algorithm to learn the rationality parameters of simultaneous and non-cooperative potential games, i.e., the parameters of the agents' optimizat…
The Benefit of Uncertainty Coupling in Robust and Adaptive Robust Optimization
Dimitris Bertsimas, Liangyuan Na, Bartolomeo Stellato +1
Despite the modeling power for problems under uncertainty, robust optimization (RO) and adaptive robust optimization (ARO) can exhibit too conservative solutions in terms of object…
On the (linear) convergence of Generalized Newton Inexact ADMM
Zachary Frangella, Theo Diamandis, Bartolomeo Stellato +1
This paper presents GeNI-ADMM, a framework for large-scale composite convex optimization that facilitates theoretical analysis of both existing and new approximate ADMM schemes. Ge…
Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic
Joyce Luo, Bartolomeo Stellato
The opioid epidemic is a crisis that has plagued the United States (US) for decades. One central issue is inequitable access to treatment for opioid use disorder (OUD), which puts…