1 citations · 1 across the 3 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
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…