李普希茨连续性
数学
可微函数
收敛速度
趋同(经济学)
应用数学
功能(生物学)
二次方程
财产(哲学)
数学优化
数学分析
计算机科学
几何学
计算机网络
频道(广播)
哲学
认识论
进化生物学
经济
生物
经济增长
作者
Xiaoxi Jia,Christian Kanzow,Patrick Mehlitz
出处
期刊:Siam Journal on Optimization
[Society for Industrial and Applied Mathematics]
日期:2023-11-09
卷期号:33 (4): 3038-3056
摘要
We consider a composite optimization problem where the sum of a continuously differentiable and a merely lower semicontinuous function has to be minimized. The proximal gradient algorithm is the classical method for solving such a problem numerically. The corresponding global convergence and local rate-of-convergence theory typically assumes, besides some technical conditions, that the smooth function has a globally Lipschitz continuous gradient and that the objective function satisfies the Kurdyka–Łojasiewicz property. Though this global Lipschitz assumption is satisfied in several applications where the objective function is, e.g., quadratic, this requirement is very restrictive in the nonquadratic case. Some recent contributions therefore try to overcome this global Lipschitz condition by replacing it with a local one, but, to the best of our knowledge, they still require some extra condition in order to obtain the desired global and rate-of-convergence results. The aim of this paper is to show that the local Lipschitz assumption together with the Kurdyka–Łojasiewicz property is sufficient to recover these convergence results.
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