Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
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Abstract (arXiv)
We consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for first-order methods. We present the first known results for the non-smooth case, which requires different analysis and a different algorithm.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jan 2019.
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Citing papers per year: 2021: 4.
4 papers.
- 2021Stochastic Gradient Langevin Dynamics with Variance Reductioncited by 7
- 2021Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyondcited by 5
- 2021Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithmscited by 4
- 2021Single-pass Object-adaptive Data Undersampling and Reconstruction for MRIcited by 2
- 2021Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI17 Nov 2021
- 2021Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond26 Mar 2021
- 2021Stochastic Gradient Langevin Dynamics with Variance Reduction12 Feb 2021
- 2021Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms4 Feb 2021