Competitively Chasing Convex Bodies
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Abstract (arXiv)
Let F be a family of sets in some metric space. In the F-chasing problem, an online algorithm observes a request sequence of sets in F and responds (online) by giving a sequence of points in these sets. The movement cost is the distance between consecutive such points. The competitive ratio is the worst case ratio (over request sequences) between the total movement of the online algorithm and the smallest movement one could have achieved by knowing in advance the request sequence. The family F is said to be chaseable if there exists an online algorithm with finite competitive ratio. In 1991, Linial and Friedman conjectured that the family of convex sets in Euclidean space is chaseable. We prove this conjecture.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 1.
7 papers.
- 2021Revisiting Smoothed Online Learningcited by 29
- 2022Optimal Comparator Adaptive Online Learning with Switching Costcited by 9
- 2023Sequential Strategic Screeningcited by 8
- 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
- 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictorcited by 6
- 2021Online Optimization with Feedback Delay and Nonlinear Switching Costcited by 5
- 2022Smoothed Online Combinatorial Optimization Using Imperfect Predictionscited by 0
- 2023Sequential Strategic Screening31 Jan 2023
- 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systems14 Oct 2022
- 2022Optimal Comparator Adaptive Online Learning with Switching Cost13 May 2022
- 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor2 May 2022
- 2022Smoothed Online Combinatorial Optimization Using Imperfect Predictions23 Apr 2022
- 2021Online Optimization with Feedback Delay and Nonlinear Switching Cost29 Oct 2021
- 2021Revisiting Smoothed Online Learning13 Feb 2021