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 assumptions which are easy to check, roughly speaking: semialgebraicity and strong monotonicity. We illustrate the scope of our results by considering three fundamental composite problem settings: strongly convex problems, dual solutions to convex minimization problems and primal-dual solutions to min-max problems.
翻译:我们利用路径差异和最近对非移动的隐含分化计算结果,以提供充分的条件,确保单调包容问题的解决办法是不同的,以公式计算其普遍梯度。我们结果的直接后果是,这些解决办法几乎在所有地方都是不同的。我们的方法与自动区分完全一致,并附有容易核对的假设,大致说来是:半位数和强烈的单一性。我们通过考虑三种基本的综合问题设置来说明我们的结果的范围:强烈的交错问题、对二次曲线最小化问题的双重解决办法和对小轴问题的原始双向解决办法。