Strongly convex functions, Moreau envelopes and the generic nature of convex functions with strong minimizers
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Abstract (arXiv)
In this work, using Moreau envelopes, we define a complete metric for the set of proper lower semicontinuous convex functions. Under this metric, the convergence of each sequence of convex functions is epi-convergence. We show that the set of strongly convex functions is dense but it is only of the first category. On the other hand, it is shown that the set of convex functions with strong minima is of the second category.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2015.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2023Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopescited by 8
- 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraintscited by 5
- 2024On the Convergence of FedProx with Extrapolation and Inexact Proxcited by 3
- 2025The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applicationscited by 3
- 2026LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimizationcited by 1
- 2026LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization8 Jun 2026
- 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints10 Apr 2025
- 2025The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications4 Feb 2025
- 2024On the Convergence of FedProx with Extrapolation and Inexact Prox2 Oct 2024
- 2023Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes17 Jan 2023