No Universal Remainder Rate for Chambolle-Dossal Acceleration

Chambolle-Dossal acceleration guarantees F(x_n) - F* = o(n^(-2)) for every fixed smooth convex loss with a minimizer. We show that this qualitative improvement admits no universal quantitative rate. For every damping parameter alpha > 3 and positive nondecreasing gain G(n) tending to infinity, we construct a fixed one-dimensional smooth convex loss whose exact CD orbit satisfies sup_{n >= 1} n^2 G(n) (F(x_n) - F*) = infinity. Thus no divergent gain improves the n^(-2) scale for all fixed losses, even with instance-dependent constants. The construction prescribes queried gradients and realizes infinitely many slow blocks within one smooth convex objective. Under local p-power growth with p > 2 and sufficiently strong damping, we also construct a fixed loss whose exact CD orbit satisfies F(x_n) - F* ~ D n^(-2p/(p-2)), D > 0, establishing the sharpness of the known convergence rate. Both main results are formally verified in Lean 4. This record contains the signed preprint and accompanying research materials: LaTeX sources, Lean 4 formalization sources, simulation code and saved results, figure-generation scripts, and reproduction instructions. Appendix G describes the scope of the formal verification and the supplementary materials. The archive README provides build and reproduction commands. Yuchen Yang and Xinan Dai contributed equally. Corresponding author: Tailin Wu ([email protected]). Xinan Dai and Wenhao Deng contributed to this research during their internships at Westlake University.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-25
DOI
https://doi.org/10.5281/zenodo.22963216
Primary Topic
Stochastic processes and financial applications
Type
preprint
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preprint

No Universal Remainder Rate for Chambolle-Dossal Acceleration

Tailin Wu, Xinan Dai, Yuchen Yang, Deng Wenhao et al.
Zenodo (CERN European Organization for Nuclear Research)
Stochastic processes and financial applications
preprint

No Universal Remainder Rate for Chambolle-Dossal Acceleration

Tailin Wu, Xinan Dai, Yuchen Yang, Deng Wenhao, Yingdong Shi, Feng Xu
preprint en

Abstract

Chambolle-Dossal acceleration guarantees F(x_n) - F* = o(n^(-2)) for every fixed smooth convex loss with a minimizer. We show that this qualitative improvement admits no universal quantitative rate. For every damping parameter alpha > 3 and positive nondecreasing gain G(n) tending to infinity, we construct a fixed one-dimensional smooth convex loss whose exact CD orbit satisfies sup_{n >= 1} n^2 G(n) (F(x_n) - F*) = infinity. Thus no divergent gain improves the n^(-2) scale for all fixed losses, even with instance-dependent constants. The construction prescribes queried gradients and realizes infinitely many slow blocks within one smooth convex objective. Under local p-power growth with p > 2 and sufficiently strong damping, we also construct a fixed loss whose exact CD orbit satisfies F(x_n) - F* ~ D n^(-2p/(p-2)), D > 0, establishing the sharpness of the known convergence rate. Both main results are formally verified in Lean 4. This record contains the signed preprint and accompanying research materials: LaTeX sources, Lean 4 formalization sources, simulation code and saved results, figure-generation scripts, and reproduction instructions. Appendix G describes the scope of the formal verification and the supplementary materials. The archive README provides build and reproduction commands. Yuchen Yang and Xinan Dai contributed equally. Corresponding author: Tailin Wu ([email protected]). Xinan Dai and Wenhao Deng contributed to this research during their internships at Westlake University.

Zenodo (CERN European Organization for Nuclear Research)
Fudan University (CN), ShanghaiTech University (CN), Westlake University (CN), University of Glasgow (GB)
Stochastic processes and financial applications
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No Universal Remainder Rate for Chambolle-Dossal Acceleration — Tailin Wu, Xinan Dai, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS