Skip to yearly menu bar Skip to main content


RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

Yair Carmon · Arun Jambulapati · Yujia Jin · Aaron Sidford

Hall E #714

Keywords: [ OPT: Convex ] [ T: Optimization ] [ OPT: Stochastic ] [ OPT: First-order ]


The accelerated proximal point method (APPA), also known as "Catalyst", is a well-established reduction from convex optimization to approximate proximal point computation (i.e., regularized minimization). This reduction is conceptually elegant and yields strong convergence rate guarantees. However, these rates feature an extraneous logarithmic term arising from the need to compute each proximal point to high accuracy. In this work, we propose a novel Relaxed Error Criterion for Accelerated Proximal Point (RECAPP) that eliminates the need for high accuracy subproblem solutions. We apply RECAPP to two canonical problems: finite-sum and max-structured minimization. For finite-sum problems, we match the best known complexity, previously obtained by carefully-designed problem-specific algorithms. For minimizing max_y f(x,y) where f is convex in x and strongly-concave in y, we improve on the best known (Catalyst-based) bound by a logarithmic factor.

Chat is not available.