mlsanityData to 14 Oct 2026

Unifying mirror descent and dual averaging

arXiv 1910.13742 ↗First posted 30 Oct 2019math.OC cs.LGcited by 8

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Abstract (arXiv)

We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging. Within the framework of this family, we define new algorithms for constrained optimization that combines the advantages of mirror descent and dual averaging. Our preliminary simulation study shows that these new algorithms significantly outperform available methods in some situations.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 0; 2024: 0; 2025: 1.

8 papers.

  1. 2021Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibriumcited by 51
  2. 2021Parameter-free Stochastic Optimization of Variationally Coherent Functionscited by 27
  3. 2022Understanding the bias-variance tradeoff of Bregman divergencescited by 11
  4. 2022Learning Stationary Nash Equilibrium Policies in n-Player Stochastic Games with Independent Chainscited by 7
  5. 2022Ensembling over Classifiers: a Bias-Variance Perspectivecited by 6
  6. 2022Robust Methods for High-Dimensional Linear Learningcited by 5
  7. 2025Primal-dual algorithm for contextual stochastic combinatorial optimizationcited by 4
  8. 2021A Unified Analysis Method for Online Optimization in Normed Vector Spacecited by 1