On the efficiency of a randomized mirror descent algorithm in online optimization problems

  • A. V. GasnikovEmail author
  • Yu. E. Nesterov
  • V. G. Spokoiny


A randomized online version of the mirror descent method is proposed. It differs from the existing versions by the randomization method. Randomization is performed at the stage of the projection of a subgradient of the function being optimized onto the unit simplex rather than at the stage of the computation of a subgradient, which is common practice. As a result, a componentwise subgradient descent with a randomly chosen component is obtained, which admits an online interpretation. This observation, for example, has made it possible to uniformly interpret results on weighting expert decisions and propose the most efficient method for searching for an equilibrium in a zero-sum two-person matrix game with sparse matrix.


mirror descent method dual averaging method online optimization exponential weighting multi-armed bandits weighting of experts stochastic optimization randomization 


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Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  • A. V. Gasnikov
    • 1
    • 2
    • 3
    Email author
  • Yu. E. Nesterov
    • 4
  • V. G. Spokoiny
    • 1
    • 2
    • 3
    • 5
  1. 1.Moscow Institute of Physics and TechnologyDolgoprudnyi, Moscow oblastRussia
  2. 2.Institute for Information Transmission ProblemsRussian Academy of SciencesMoscowRussia
  3. 3.National Research University Higher School of EconomicsMoscowRussia
  4. 4.Center of Operations Research and Econometrics (CORE, UCL)Louvain-la-NeuveBelgium
  5. 5.Weierstrass Institute for Applied Analysis and StochasticsBerlinGermany

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