1.2k citations · 1.6k across the 35 of their papers we have counts for
39 papers
The exact worst-case asymptotic rate of Nesterov acceleration for strongly convex optimization
Sebastian Pokutta, Bartolomeo Stellato
We study Nesterov's accelerated gradient method with its standard constant parameters for smooth strongly convex optimization. We characterize the exact worst-case asymptotic facto…
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
Anjian Li, Bartolomeo Stellato, Ryne Beeson
We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems. The goal is for these initia…
Disjunctive Sum of Squares
Amir Ali Ahmadi, Sanjeeb Dash, Yixuan Hua +1
We introduce the concept of disjunctive sum of squares for certifying nonnegativity of polynomials. Unlike the popular sum of squares approach where nonnegativity is certified by a…
Lower Bounds for Frank-Wolfe on Strongly Convex Sets
Jannis Halbey, Daniel Deza, Max Zimmer +3
We present a constructive lower bound of for Frank-Wolfe (FW) when both the objective and the constraint set are smooth and strongly convex, showing that…
Conformal Prediction for Early Stopping in Mixed Integer Optimization
Stefan Clarke, Bartolomeo Stellato
Mixed-integer optimization solvers often find optimal solutions early in the search, yet spend the majority of computation time proving optimality. We exploit this by learning when…
Batched First-Order Methods for Parallel LP Solving in MIP
Nicolas Blin, Stefano Gualandi, Christopher Maes +2
We present a batched first-order method for solving multiple linear programs in parallel on GPUs. Our approach extends the primal-dual hybrid gradient algorithm to efficiently solv…
Learning-Based Hierarchical Approach for Fast Mixed-Integer Optimization
Stefan Clarke, Bartolomeo Stellato
We propose a hierarchical architecture for efficiently computing high-quality solutions to structured mixed-integer programs (MIPs). To reduce computational effort, our approach de…
Data-Driven Analysis of First-Order Methods via Distributionally Robust Optimization
Jisun Park, Vinit Ranjan, Bartolomeo Stellato
We consider the problem of analyzing the probabilistic performance of first-order methods when solving convex optimization problems drawn from an unknown distribution only accessib…
AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Ori Press, Brandon Amos, Haoyu Zhao +21
Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming…
Exact Verification of First-Order Methods via Mixed-Integer Linear Programming
Vinit Ranjan, Jisun Park, Stefano Gualandi +2
We present exact mixed-integer linear programming formulations for verifying the performance of first-order methods for parametric quadratic optimization. We formulate the verifica…
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
Rajiv Sambharya, Bartolomeo Stellato
We introduce a machine-learning framework to learn the hyperparameter sequence of first-order methods (e.g., the step sizes in gradient descent) to quickly solve parametric convex…
Learning Hierarchical Control For Multi-Agent Capacity-Constrained Systems
Charlott Vallon, Alessandro Pinto, Bartolomeo Stellato +1
This paper introduces a novel data-driven hierarchical control scheme for managing a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. We prop…
Data-Driven Performance Guarantees for Classical and Learned Optimizers
Rajiv Sambharya, Bartolomeo Stellato
We introduce a data-driven approach to analyze the performance of continuous optimization algorithms using generalization guarantees from statistical learning theory. We study clas…
A neural network-based approach to hybrid systems identification for control
Filippo Fabiani, Bartolomeo Stellato, Daniele Masti +1
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the…
Verification of First-Order Methods for Parametric Quadratic Optimization
Vinit Ranjan, Bartolomeo Stellato
We introduce a numerical framework to verify the finite step convergence of first-order methods for parametric convex quadratic optimization. We formulate the verification problem…
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
Haimin Hu, Gabriele Dragotto, Zixu Zhang +3
We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its as…
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…