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math.OC2026
Peppy: An AI-Assisted Workflow for Tight Convergence Analysis of Optimization Algorithms
Jaewook J. Suh, TaeHo Yoon, Edward Duc Hien Nguyen +2
This paper presents Peppy, an AI-assisted workflow for discovering tight, analytic convergence proofs for first-order optimization algorithms. Generic approaches to using LLMs to c…
math.OC2026
Toward a Systematic Understanding and Interactive Search of Lyapunov-Style Proofs in Optimization
TaeHo Yoon, Jaewook J. Suh, Edward Duc Hien Nguyen +2
Lyapunov-style convergence proofs, which establish a nonincreasing sequence to provide a quantitative convergence rate for an algorithm, are popular and often considered desirable…
math.OC2026
A Theory of Composition and Duality of Extremal Optimal Fixed-Point Algorithms
TaeHo Yoon, Benjamin Grimmer
In this work, we reveal a rich combinatorial structure underlying exact minimax optimal algorithms for classical nonexpansive fixed-point problems. This viewpoint unifies all extre…