Square distance functions are Polyak-Lojasiewicz and vice-versa
Not on the map. Why?
Abstract (arXiv)
This short note gathers known results to state that the squared distance function to a (nonconvex) closed set of an Euclidean space is Polyak-Lojasiewicz. As a fuzzy reciprocate, we also recall that every Polyak-Lojasiewicz function can be bounded from below by the squared distance function to its set of minimizers.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jan 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2023Distributionally Time-Varying Online Stochastic Optimization under Polyak-Lojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learningcited by 3
- 2024From Inverse Optimization to Feasibility to ERMcited by 3
- 2025Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopescited by 0
- 2025Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes16 May 2025
- 2024From Inverse Optimization to Feasibility to ERM27 Feb 2024
- 2023Distributionally Time-Varying Online Stochastic Optimization under Polyak-Lojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning18 Sep 2023