Conditional Accelerated Lazy Stochastic Gradient Descent
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Abstract (arXiv)
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate Oleft(frac1varepsilon2right) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate Oleft(frac1varepsilon4right).
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021Efficient Projection-Free Online Convex Optimization with Membership Oraclecited by 30
- 2021Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimizationcited by 6
- 2022Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Controlcited by 2
- 2025Scalable DC Optimization via Adaptive Frank-Wolfe Algorithmscited by 1
- 2025Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms23 Jul 2025
- 2022Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control11 Oct 2022
- 2021Efficient Projection-Free Online Convex Optimization with Membership Oracle10 Nov 2021
- 2021Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimization14 Jul 2021