Unifying mirror descent and dual averaging

[1]  András György,et al.  A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds , 2017, ALT.

[2]  Alexander V. Nazin,et al.  Algorithms of Inertial Mirror Descent in Convex Problems of Stochastic Optimization , 2017, Automation and Remote Control.

[3]  Tamir Hazan,et al.  Tight Bounds for Bandit Combinatorial Optimization , 2017, COLT.

[4]  Francis R. Bach,et al.  Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ , 2017, COLT.

[5]  John C. Duchi,et al.  Asymptotic optimality in stochastic optimization , 2016, The Annals of Statistics.

[6]  Elad Hazan,et al.  Introduction to Online Convex Optimization , 2016, Found. Trends Optim..

[7]  Alexandre M. Bayen,et al.  Accelerated Mirror Descent in Continuous and Discrete Time , 2015, NIPS.

[8]  Yu. Nesterov,et al.  Quasi-monotone Subgradient Methods for Nonsmooth Convex Minimization , 2015, J. Optim. Theory Appl..

[9]  Zeyuan Allen Zhu,et al.  Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent , 2014, ITCS.

[10]  Sébastien Bubeck,et al.  Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..

[11]  H. Brendan McMahan,et al.  A survey of Algorithms and Analysis for Adaptive Online Learning , 2014, J. Mach. Learn. Res..

[12]  Panayotis Mertikopoulos,et al.  A continuous-time approach to online optimization , 2014, Journal of Dynamics & Games.

[13]  Koby Crammer,et al.  A generalized online mirror descent with applications to classification and regression , 2013, Machine Learning.

[14]  Arkadi Nemirovski,et al.  Dual subgradient algorithms for large-scale nonsmooth learning problems , 2013, Math. Program..

[15]  Sanjoy Dasgupta,et al.  Agglomerative Bregman Clustering , 2012, ICML.

[16]  Guanghui Lan,et al.  An optimal method for stochastic composite optimization , 2011, Mathematical Programming.

[17]  Sébastien Bubeck,et al.  Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems , 2012, Found. Trends Mach. Learn..

[18]  Gábor Lugosi,et al.  Regret in Online Combinatorial Optimization , 2012, Math. Oper. Res..

[19]  Shai Shalev-Shwartz,et al.  Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..

[20]  Stephen J. Wright,et al.  Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning , 2012, J. Mach. Learn. Res..

[21]  Sham M. Kakade,et al.  Towards Minimax Policies for Online Linear Optimization with Bandit Feedback , 2012, COLT.

[22]  H. Brendan McMahan,et al.  Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization , 2011, AISTATS.

[23]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[24]  Ohad Shamir,et al.  Optimal Distributed Online Prediction Using Mini-Batches , 2010, J. Mach. Learn. Res..

[25]  Martin J. Wainwright,et al.  Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling , 2010, IEEE Transactions on Automatic Control.

[26]  Jean-Yves Audibert,et al.  Regret Bounds and Minimax Policies under Partial Monitoring , 2010, J. Mach. Learn. Res..

[27]  Matthew J. Streeter,et al.  Adaptive Bound Optimization for Online Convex Optimization , 2010, COLT 2010.

[28]  Lin Xiao,et al.  Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..

[29]  Jean-Yves Audibert,et al.  Minimax Policies for Adversarial and Stochastic Bandits. , 2009, COLT 2009.

[30]  Elad Hazan,et al.  Extracting certainty from uncertainty: regret bounded by variation in costs , 2008, Machine Learning.

[31]  Alexander Shapiro,et al.  Stochastic Approximation approach to Stochastic Programming , 2013 .

[32]  A. Juditsky,et al.  Solving variational inequalities with Stochastic Mirror-Prox algorithm , 2008, 0809.0815.

[33]  Peter L. Bartlett,et al.  Adaptive Online Gradient Descent , 2007, NIPS.

[34]  Yurii Nesterov,et al.  Dual extrapolation and its applications to solving variational inequalities and related problems , 2003, Math. Program..

[35]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[36]  A. Juditsky,et al.  Learning by mirror averaging , 2005, math/0511468.

[37]  Yurii Nesterov,et al.  Primal-dual subgradient methods for convex problems , 2005, Math. Program..

[38]  Y. Mansour,et al.  Improved second-order bounds for prediction with expert advice , 2005, Machine Learning.

[39]  Alexander V. Nazin,et al.  Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging , 2005, Probl. Inf. Transm..

[40]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[41]  Martin Zinkevich,et al.  Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.

[42]  Marc Teboulle,et al.  Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..

[43]  Marc Teboulle,et al.  Convergence Analysis of a Proximal-Like Minimization Algorithm Using Bregman Functions , 1993, SIAM J. Optim..

[44]  A. Goldstein Convex programming in Hilbert space , 1964 .

[45]  Elad Hazan The convex optimization approach to regret minimization , 2011 .

[46]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[47]  Shai Shalev-Shwartz,et al.  Online learning: theory, algorithms and applications (למידה מקוונת.) , 2007 .

[48]  Arkadi Nemirovski,et al.  Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems , 2004, SIAM J. Optim..

[49]  John Darzentas,et al.  Problem Complexity and Method Efficiency in Optimization , 1983 .

[50]  L. Bregman The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .

[51]  Boris Polyak,et al.  Constrained minimization methods , 1966 .