数学
可微函数
单调多边形
单调函数
数学优化
路径(计算)
正多边形
杠杆(统计)
参数统计
凸优化
应用数学
纯数学
数学分析
计算机科学
统计
程序设计语言
几何学
作者
Jérôme Bolte,Edouard Pauwels,Antonio José Silveti-Falls
出处
期刊:Siam Journal on Optimization
[Society for Industrial and Applied Mathematics]
日期:2024-01-04
卷期号:34 (1): 71-97
摘要
We leverage path differentiability and a recent result on nonsmooth implicit differentiation calculus to give sufficient conditions ensuring that the solution to a monotone inclusion problem will be path differentiable, with formulas for computing its generalized gradient. A direct consequence of our result is that these solutions happen to be differentiable almost everywhere. Our approach is fully compatible with automatic differentiation and comes with the following assumptions which are easy to check (roughly speaking): semialgebraicity and strong monotonicity. We illustrate the scope of our results by considering the following three fundamental composite problem settings: strongly convex problems, dual solutions to convex minimization problems, and primal-dual solutions to min-max problems.
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