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通过强化学习发现更快的矩阵乘法算法
Alhussein Fawzi1, Matej Balog2, Aja Huang2
1DeepMind, London, UK. afawzi@deepmind.com.
Nature
|October 5, 2022
概括
通过AlphaTensor,深度强化学习发现了新的矩阵乘法算法. 这种人工智能方法显著提高了计算效率,超过了关键矩阵大小的人为设计的方法.
科学领域:
- 计算机科学
- 人工智能
- 计算数学
背景情况:
- 矩阵乘法是影响神经网络和科学计算等不同领域的基本计算.
- 由于广的搜索空间,发现新的矩阵乘法算法具有挑战性.
- 现有的算法虽然有效,但可能不是最佳解决方案.
研究的目的:
- 开发一种人工智能驱动的方法来发现高效且可证明正确的矩阵乘法算法.
- 探索深度强化学习在算法发现自动化方面的潜力.
- 在矩阵复杂度方面取得突破,
主要方法:
- 使用深度强化学习剂AlphaTensor,
- 训练AlphaTensor玩一个专注于在有限的因子空间内找到张量分解的游戏.
- 应用该代理来发现任意和结构化矩阵乘法算法.
主要成果:
- AlphaTensor发现了超越各种矩阵维度的最先进复杂度的算法.
- 对于有限场中的4x4矩阵的新算法改进了斯特拉森50年前的方法.
- 对特定硬件运行时间和结构化矩阵乘法进行了优化.
结论:
- 深度强化学习可以加速算法发现.
- 这种方法为基本的计算任务提供了超越人类设计的算法的途径.
- AlphaTensor提供了一个灵活的框架,可以根据不同的标准优化算法,包括计算复杂性和实际效率.
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