Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
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Abstract (arXiv)
We propose an accelerated version of stochastic variance reduced coordinate descent -- ASVRCD. As other variance reduced coordinate descent methods such as SEGA or SVRCD, our method can deal with problems that include a non-separable and non-smooth regularizer, while accessing a random block of partial derivatives in each iteration only. However, ASVRCD incorporates Nesterov's momentum, which offers favorable iteration complexity guarantees over both SEGA and SVRCD. As a by-product of our theory, we show that a variant of Allen-Zhu (2017) is a specific case of ASVRCD, recovering the optimal oracle complexity for the finite sum objective.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 1.
8 papers.
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
- 2021Differentially Private Coordinate Descent for Composite Empirical Risk Minimizationcited by 10
- 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 6
- 2023Towards a Better Theoretical Understanding of Independent Subnetwork Trainingcited by 6
- 2022Personalized Federated Learning with Multiple Known Clusterscited by 3
- 2024Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reductioncited by 1
- 2024Differentially Private Random Block Coordinate Descentcited by 0
- 2025An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimizationcited by 0
- 2025An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization13 Jan 2025
- 2024Differentially Private Random Block Coordinate Descent22 Dec 2024
- 2024Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction11 Mar 2024
- 2023Towards a Better Theoretical Understanding of Independent Subnetwork Training28 Jun 2023
- 2022Personalized Federated Learning with Multiple Known Clusters28 Apr 2022
- 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback28 Dec 2021
- 2021Differentially Private Coordinate Descent for Composite Empirical Risk Minimization22 Oct 2021
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques19 Feb 2021