4 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…
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…
Distributionally-Robust Learning to Optimize
Vinit Ranjan, Jisun Park, Bartolomeo Stellato
We propose a distributionally robust approach to learning hyperparameters for first-order methods in convex optimization. Given a dataset of problem instances, we minimize a Wasser…
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…