Path differentiability of ODE flows
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Abstract (arXiv)
We consider flows of ordinary differential equations (ODEs) driven by path differentiable vector fields. Path differentiable functions constitute a proper subclass of Lipschitz functions which admit conservative gradients, a notion of generalized derivative compatible with basic calculus rules. Our main result states that such flows inherit the path differentiability property of the driving vector field. We show indeed that forward propagation of derivatives given by the sensitivity differential inclusions provide a conservative Jacobian for the flow. This allows to propose a nonsmooth version of the adjoint method, which can be applied to integral costs under an ODE constraint. This result constitutes a theoretical ground to the application of small step first order methods to solve a broad class of nonsmooth optimization problems with parametrized ODE constraints. This is illustrated with the convergence of small step first order methods based on the proposed nonsmooth adjoint.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 11 Jan 2022.
Builds on
8 of 39 references.
References · 8
- 2018Neural Ordinary Differential Equationscited by 3,779
- 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
- 2019Augmented Neural ODEscited by 397
- 2019Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learningcited by 72
- 2020A mathematical model for automatic differentiation in machine learningcited by 45
- 2020Differential Inclusions in Wasserstein Spaces: The Cauchy-Lipschitz Frameworkcited by 4
- 2021Conservative and semismooth derivatives are equivalent for semialgebraic mapscited by 4
- 2020Examples of pathological dynamics of the subgradient method for Lipschitz path-differentiable functionscited by 1
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Citing papers per year: 2022: 1.
1 paper.
- 2022Automatic differentiation of nonsmooth iterative algorithmscited by 25
- 2022Automatic differentiation of nonsmooth iterative algorithms31 May 2022