Highly Smooth Zeroth-Order Methods for Solving Optimization Problems under the PL Condition

IF 0.7 4区 数学 Q3 MATHEMATICS, APPLIED Computational Mathematics and Mathematical Physics Pub Date : 2024-06-07 DOI:10.1134/s0965542524700118
A. V. Gasnikov, A. V. Lobanov, F. S. Stonyakin
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Abstract

In this paper, we study the black box optimization problem under the Polyak–Lojasiewicz (PL) condition, assuming that the objective function is not just smooth, but has higher smoothness. By using “kernel-based” approximations instead of the exact gradient in the Stochastic Gradient Descent method, we improve the best-known results of convergence in the class of gradient-free algorithms solving problems under the PL condition. We generalize our results to the case where a zeroth-order oracle returns a function value at a point with some adversarial noise. We verify our theoretical results on the example of solving a system of nonlinear equations.

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解决 PL 条件下优化问题的高平滑零阶方法
摘要 本文研究了 Polyak-Lojasiewicz(PL)条件下的黑箱优化问题,假设目标函数不仅是平滑的,而且具有更高的平滑性。通过在随机梯度下降法中使用 "基于核 "的近似值而不是精确梯度,我们改进了解决 PL 条件下问题的无梯度算法中最著名的收敛结果。我们将结果推广到了零阶神谕在某点返回函数值并带有一些对抗噪声的情况。我们以求解非线性方程组为例,验证了我们的理论结果。
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来源期刊
Computational Mathematics and Mathematical Physics
Computational Mathematics and Mathematical Physics MATHEMATICS, APPLIED-PHYSICS, MATHEMATICAL
CiteScore
1.50
自引率
14.30%
发文量
125
审稿时长
4-8 weeks
期刊介绍: Computational Mathematics and Mathematical Physics is a monthly journal published in collaboration with the Russian Academy of Sciences. The journal includes reviews and original papers on computational mathematics, computational methods of mathematical physics, informatics, and other mathematical sciences. The journal welcomes reviews and original articles from all countries in the English or Russian language.
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