New Lower Bounds for Stochastic Non-Convex Optimization through Divergence Composition

• URL: http://arxiv.org/abs/2502.14060v1
• Date: Wed, 19 Feb 2025 19:21:00 GMT
• Title: New Lower Bounds for Stochastic Non-Convex Optimization through Divergence Composition
• Authors: El Mehdi Saad, Weicheng Lee, Francesco Orabona,
• Abstract summary: We present fundamental limits of first-order optimization in a range of non-dimensional settings, including L-Convexity (QC), Quadratic Growth (smoothQG), and Restricted Inequalities (RSI)
• Score: 11.530542389959347
• License:
• Abstract: We study fundamental limits of first-order stochastic optimization in a range of nonconvex settings, including L-smooth functions satisfying Quasar-Convexity (QC), Quadratic Growth (QG), and Restricted Secant Inequalities (RSI). While the convergence properties of standard algorithms are well-understood in deterministic regimes, significantly fewer results address the stochastic case, where only unbiased and noisy gradients are available. We establish new lower bounds on the number of noisy gradient queries to minimize these classes of functions, also showing that they are tight (up to a logarithmic factor) in all the relevant quantities characterizing each class. Our approach reformulates the optimization task as a function identification problem, leveraging divergence composition arguments to construct a challenging subclass that leads to sharp lower bounds. Furthermore, we present a specialized algorithm in the one-dimensional setting that achieves faster rates, suggesting that certain dimensional thresholds are intrinsic to the complexity of non-convex stochastic optimization.

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