Rate of Convergence of the Bundle Method
Not on the map. Why?
Abstract (arXiv)
We prove that the bundle method for nonsmooth optimization achieves solution accuracy varepsilon in at most Obig(ln(1/varepsilon)/varepsilonbig) iterations, if the function is strongly convex. The result is true for the versions of the method with multiple cuts and with cut aggregation.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2024Proximal Oracles for Optimization and Samplingcited by 5
- 2021Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimizationcited by 4
- 2023Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Setscited by 2
- 2025Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Piecescited by 0
- 2025Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Pieces25 Jul 2025
- 2024Proximal Oracles for Optimization and Sampling2 Apr 2024
- 2023Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets9 Mar 2023
- 2021Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization30 Nov 2021