Linear Coupling of Gradient and Mirror Descent: A Novel, Simple Interpretation of Nesterov's Accelerated Method

[1]  Yurii Nesterov,et al.  Universal gradient methods for convex optimization problems , 2015, Math. Program..

[2]  Zeyuan Allen Zhu,et al.  Using Optimization to Break the Epsilon Barrier: A Faster and Simpler Width-Independent Algorithm for Solving Positive Linear Programs in Parallel , 2014, SODA.

[3]  Aleksander Madry,et al.  Navigating Central Path with Electrical Flows: From Flows to Matchings, and Back , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.

[4]  Shai Shalev-Shwartz,et al.  Accelerated Mini-Batch Stochastic Dual Coordinate Ascent , 2013, NIPS.

[5]  Jonah Sherman,et al.  Nearly Maximum Flows in Nearly Linear Time , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.

[6]  Ohad Shamir,et al.  Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes , 2012, ICML.

[7]  Yurii Nesterov,et al.  Gradient methods for minimizing composite functions , 2012, Mathematical Programming.

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

[9]  Sanjeev Arora,et al.  The Multiplicative Weights Update Method: a Meta-Algorithm and Applications , 2012, Theory Comput..

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

[11]  Nisheeth K. Vishnoi,et al.  Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator , 2011, STOC '12.

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

[13]  Shang-Hua Teng,et al.  Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs , 2010, STOC '11.

[14]  Ambuj Tewari,et al.  Composite objective mirror descent , 2010, COLT 2010.

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

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

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

[18]  Yurii Nesterov,et al.  Accelerating the cubic regularization of Newton’s method on convex problems , 2005, Math. Program..

[19]  Elad Hazan,et al.  Logarithmic regret algorithms for online convex optimization , 2006, Machine Learning.

[20]  Inderjit S. Dhillon,et al.  Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..

[21]  Sanjeev Arora,et al.  Fast algorithms for approximate semidefinite programming using the multiplicative weights update method , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

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

[23]  Danny Raz,et al.  Fast, Distributed Approximation Algorithms for Positive Linear Programming with Applications to Flow Control , 2004, SIAM J. Comput..

[24]  Neal E. Young,et al.  Sequential and parallel algorithms for mixed packing and covering , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[25]  Danny Raz,et al.  Global optimization using local information with applications to flow control , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.

[26]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[27]  Noam Nisan,et al.  A parallel approximation algorithm for positive linear programming , 1993, STOC.

[28]  Éva Tardos,et al.  Fast approximation algorithms for fractional packing and covering problems , 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.