On the Complexity of a Practical Primal-Dual Coordinate Method

We prove complexity bounds for the primal-dual algorithm with random extrapolation and coordinate descent (PURE-CD), which has been shown to obtain good practical performance for solving convexconcave min-max problems with bilinear coupling. Our complexity bounds either match or improve the best-known results in the literature for both dense and sparse (strongly)-convex-(strongly)-concave problems.

[1]  Lin Xiao,et al.  A Proximal Stochastic Gradient Method with Progressive Variance Reduction , 2014, SIAM J. Optim..

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

[3]  Shai Shalev-Shwartz,et al.  Stochastic dual coordinate ascent methods for regularized loss , 2012, J. Mach. Learn. Res..

[4]  Yuchen Zhang,et al.  Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization , 2014, ICML.

[5]  Zeyuan Allen Zhu,et al.  Katyusha: the first direct acceleration of stochastic gradient methods , 2017, STOC.

[6]  Heinz H. Bauschke,et al.  Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.

[7]  Stephen J. Wright,et al.  Coordinate Linear Variance Reduction for Generalized Linear Programming , 2021, ArXiv.

[8]  Antonin Chambolle,et al.  Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications , 2017, SIAM J. Optim..

[9]  Stephen J. Wright Coordinate descent algorithms , 2015, Mathematical Programming.

[10]  Pascal Bianchi,et al.  A Coordinate-Descent Primal-Dual Algorithm with Large Step Size and Possibly Nonseparable Functions , 2015, SIAM J. Optim..

[11]  Stephen J. Wright,et al.  Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums , 2021, ICML.

[12]  Tong Zhang,et al.  Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization , 2013, Mathematical Programming.

[13]  Tong Zhang,et al.  Accelerated dual-averaging primal–dual method for composite convex minimization , 2020, Optim. Methods Softw..

[14]  On the convergence of stochastic primal-dual hybrid gradient , 2019, 1911.00799.

[15]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[16]  Yura Malitsky,et al.  Stochastic Variance Reduction for Variational Inequality Methods , 2021, ArXiv.

[17]  丸山 徹 Convex Analysisの二,三の進展について , 1977 .

[18]  Zeyuan Allen-Zhu,et al.  Katyusha: the first direct acceleration of stochastic gradient methods , 2016, J. Mach. Learn. Res..

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

[20]  Kevin Tian,et al.  Variance Reduction for Matrix Games , 2019, NeurIPS.

[21]  Guanghui Lan,et al.  First-order and Stochastic Optimization Methods for Machine Learning , 2020 .

[22]  Volkan Cevher,et al.  A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization , 2015, SIAM J. Optim..

[23]  Kevin Tian,et al.  Coordinate Methods for Matrix Games , 2020, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

[24]  Volkan Cevher,et al.  Random extrapolation for primal-dual coordinate descent , 2020, ICML.

[25]  Yurii Nesterov,et al.  Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..

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

[27]  Antonin Chambolle,et al.  On the ergodic convergence rates of a first-order primal–dual algorithm , 2016, Math. Program..