290 citations · 492 across the 36 of their papers we have counts for
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Min-Max Optimization Is Strictly Easier Than Variational Inequalities
Henry Shugart, Jason M. Altschuler
Classically, a mainstream approach for solving a convex-concave min-max problem is to instead solve the variational inequality problem arising from its first-order optimality condi…
Optimized methods for composite optimization: a reduction perspective
Jinho Bok, Jason M. Altschuler
Recent advances in convex optimization have leveraged computer-assisted proofs to develop optimized first-order methods that improve over classical algorithms. However, each optimi…
Shifted Composition IV: Toward Ballistic Acceleration for Log-Concave Sampling
Jason M. Altschuler, Sinho Chewi, Matthew S. Zhang
Acceleration is a celebrated cornerstone of convex optimization, enabling gradient-based algorithms to converge sublinearly in the condition number. A major open question is whethe…
Negative Stepsizes Make Gradient-Descent-Ascent Converge
Henry Shugart, Jason M. Altschuler
Efficient computation of min-max problems is a central question in optimization, learning, games, and control. Arguably the most natural algorithm is gradient-descent-ascent (GDA).…
Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm
Xufeng Cai, Jason M. Altschuler, Jelena Diakonikolas
In 1960, Osborne proposed a simple iterative algorithm for matrix balancing with outstanding numerical performance. Today, it is the default preconditioning procedure before eigenv…