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[2601.00728] Precision Autotuning for Linear Solvers via Reinforcement Learning
Summary
This paper presents a reinforcement learning framework for adaptive precision tuning of linear solvers, enhancing computational efficiency while maintaining accuracy.
Why It Matters
The research addresses the growing need for efficient numerical methods in scientific computing. By utilizing reinforcement learning for precision autotuning, it offers a novel approach that can significantly reduce computational costs without sacrificing accuracy, which is vital for various applications in machine learning and numerical analysis.
Key Takeaways
- Introduces a reinforcement learning framework for precision tuning in linear solvers.
- Balances computational efficiency and accuracy through dynamic precision selection.
- Demonstrates effectiveness in reducing costs while maintaining accuracy comparable to double precision.
- Generalizes to various numerical algorithms beyond linear solvers.
- First study on precision autotuning using reinforcement learning verified on unseen datasets.
Computer Science > Machine Learning arXiv:2601.00728 (cs) [Submitted on 2 Jan 2026 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Precision Autotuning for Linear Solvers via Reinforcement Learning Authors:Erin Carson, Xinye Chen View a PDF of the paper titled Precision Autotuning for Linear Solvers via Reinforcement Learning, by Erin Carson and 1 other authors View PDF HTML (experimental) Abstract:We propose a reinforcement learning (RL) framework for adaptive precision tuning of linear solvers, and can be extended to general algorithms. The framework is formulated as a contextual bandit problem and solved using incremental action-value estimation with a discretized state space to select optimal precision configurations for computational steps, balancing precision and computational efficiency. To verify its effectiveness, we apply the framework to iterative refinement for solving linear systems $Ax = b$. In this application, our approach dynamically chooses precisions based on calculated features from the system. In detail, a Q-table maps discretized features (e.g., approximate condition number and matrix norm)to actions (chosen precision configurations for specific steps), optimized via an epsilon-greedy strategy to maximize a multi-objective reward balancing accuracy and computational cost. Empirical results demonstrate effective precision selection, reducing computational cost while maintaining accuracy comparable to double-precision baselines. The framework g...
