Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps
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Abstract (arXiv)
We study the oracle complexity of computing a point with small fixed-point residual |T(x)-x| ≤ ε, for a general norm |·| and a self-map T of a compact convex set. We study this problem in the setting where T is nonexpansive with respect to the same norm |·| and accessed via an unbiased stochastic oracle with bounded variance σ2. We provide an algorithm that solves such instances for any norm with a weak Rademacher type q > 1, with high probability. The algorithm is based on a recursive anchoring technique. For type-2 spaces, such as ℓp-spaces for p ∈ [2, ∞], our algorithm attains stochastic oracle complexity tilde O(σ2 ε-3 + ε-1). We further prove a near-matching lower bound (i.e., matching up to poly-log factors) for such ℓ∞-norm instances in high dimensions. Our lower bound holds against any randomized algorithm that succeeds with constant probability. It further extends to settings with ``sparse'' noise, where variance measured with respect to any ℓp norm is of the same order, ruling out the possibility of improving oracle complexity as a function of varepsilon by measuring variance in a non-matching ℓp norm.
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References · 18
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- 2021Optimal error bounds for nonexpansive fixed-point iterations in normed spacescited by 12
6 more
- 2024Stochastic Halpern iteration in normed spaces and applications to reinforcement learningcited by 12
- 2024Mean Estimation in Banach Spaces Under Infinite Variance and Martingale Dependencecited by 6
- 2025Pushing the Complexity Boundaries of Fixed-Point Equations: Adaptation to Contraction and Controlled Expansioncited by 5
- 2026Stochastic Krasnoselskii-Mann Iterations: Convergence without Uniformly Bounded Variancecited by 5
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- 2026A Banach-Space Theory of Markovian Halpern Iteration for Non-Expansive Mapscited by 1
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