趋同(经济学)
下降(航空)
数学
订单(交换)
梯度下降
收敛速度
数学优化
应用数学
计算机科学
电信
物理
经济
机器学习
频道(广播)
人工神经网络
气象学
经济增长
财务
作者
Tianyu Wang,Yasong Feng
出处
期刊:Informs Journal on Computing
[Institute for Operations Research and the Management Sciences]
日期:2024-03-13
卷期号:36 (6): 1611-1633
被引量:2
标识
DOI:10.1287/ijoc.2023.0247
摘要
We prove convergence rates of Zeroth-order Gradient Descent (ZGD) algorithms for Łojasiewicz functions. Our results show that for smooth Łojasiewicz functions with Łojasiewicz exponent larger than 0.5 and smaller than 1, the functions values can converge much faster than the (zeroth-order) gradient descent trajectory. Similar results hold for convex nonsmooth Łojasiewicz functions. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0247 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0247 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
科研通智能强力驱动
Strongly Powered by AbleSci AI