Fetching the paper…
Reading the bibliography…
We develop stochastic first-order primal-dual algorithms to solve a class of convex-concave saddle-point problems.
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
1904
Earlier work this paper cites.
Davis D (2015) Convergence rate analysis of primal-dual splitting schemes. SIAM J. Optim. 25(3):1912–1943
1943
Earlier work this paper cites.
Robbins H, Monro S (1951) A stochastic approximation method. Ann. Math. Statist. 22(3):400–407
1951
Earlier work this paper cites.
Sion M (1958) On general minimax theorems. Pacific J. Math. 8(1):171–176
1958
Earlier work this paper cites.
Berge C (1963) Topological Spaces (Macmillan)
1963
Earlier work this paper cites.
Nemirovskii A, Yudin D (1979) Efficient methods for large-scale convex problems. Ekonomika i Matematicheskie Metody (in Russian) 15:135–152
1979
Earlier work this paper cites.
Bertsekas DP (1999) Nonlinear Programming (Athena Scitific)
1999
Earlier work this paper cites.
Ben-Tal A, Margalit T, Nemirovski A (2001) The ordered subsets mirror descent optimization method with applications to tomography. SIAM J. Optim. 12(1):79–108
2001
Earlier work this paper cites.
Beck A, Teboulle M (2003) Mirror descent and nonlinear projected subgradient methods for convex optimization. Oper. Res. Lett. 31(3):167 – 175
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
Boyd S, Vandenberghe L (2004) Convex Optimization (Cambridge University Press)
2004
Earlier work this paper cites.
Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5:27–72
2004
Earlier work this paper cites.
Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. Proc. ICML , 919–926
2004
Earlier work this paper cites.
Ben-Tal A, Nemirovski A (2005) Non-euclidean restricted memory level method for large-scale convex optimization. Math. Program. 102(3):407–456
2005
Earlier work this paper cites.
Nemirovski A (2005) Prox-method with rate of convergence O ( 1 / t ) O(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems. SIAM J. Optim. 15(1):229–251
2005
Earlier work this paper cites.
Cover TM, Thomas JA (2006) Elements of Information Theory (Wiley-Interscience)
2006
Cited alongside, same era.
Nesterov Y (2007) Smoothing technique and its applications in semidefinite optimization. Math. Program. 110(2):245–259
2007
Cited alongside, same era.
Ok EA (2007) Real Analysis with Economic Applications (Princeton University Press)
2007
Cited alongside, same era.
Kim S, Sohn KA, Xing EP (2009) A multivariate regression approach to association analysis of a quantitative trait network. Bioinform. 25(12):204–212
2009
Cited alongside, same era.
Nedić A, Ozdaglar A (2009) Subgradient methods for saddle-point problems. J. Optim. Theory Appl. 142(1):205–228
2009
Cited alongside, same era.
Juditsky A, Nesterov Y (2014) Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization. Stoch. Syst. 4(1):44–80
2014
Later among the works it cites.
Lin Q, Xiao L (2015) An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization. Comput. Optim. Appl. 60(3):73–81
2015
Later among the works it cites.
Peypouquet J (2015) Convex optimization in normed spaces : theory, methods and examples (Springer)
2015
Later among the works it cites.
Balamurugan P, Bach F (2016) Stochastic variance reduction methods for saddle-point problems. Proc. NIPS
2016
Later among the works it cites.
Chambolle A, Pock T (2016) On the ergodic convergence rates of a first-order primal–dual algorithm. Math. Program. 159(1):253–287
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nemirovski A, Juditsky A, Lan G, Shapiro A (2009) Robust stochastic approximation approach to stochastic programming. SIAM J. Optim. 19(4):1574–1609
2009
Cited alongside, same era.
Nesterov Y (2009) Primal-dual subgradient methods for convex problems. Math. Program. 120(1):221–259
2009
Cited alongside, same era.
Chambolle A, Pock T (2011) A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40(1):120–145
2011
Cited alongside, same era.
Juditsky A, Nemirovski A, Tauvel C (2011) Solving variational inequalities with stochastic mirror-prox algorithm. Stoch. Syst. 1(1):17–58
2011
Cited alongside, same era.
Raginsky M, Rakhlin A (2011) Information-based complexity, feedback and dynamics in convex programming. IEEE Trans. Inf. Theory 57(10):7036–7056
2011
Cited alongside, same era.
Banerjee M (2012) Simple random sampling. http://dept.stat.lsa.umich.edu/ moulib/sampling.pdf
2012
Cited alongside, same era.
Ghadimi S, Lan G (2012) Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization I: A generic algorithmic framework. SIAM J. Optim. 22(4):1469–1492
2012
Cited alongside, same era.
Diamond S, Boyd S (2016) CVXPY: A Python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17(83):1–5
2016
Later among the works it cites.
He Y, Monteiro RDC (2016) An accelerated HPE-type algorithm for a class of composite convex-concave saddle-point problems. SIAM J. Optim. 26(1):29–56
2016
Later among the works it cites.
Chen Y, Lan G, Ouyang Y (2017) Accelerated schemes for a class of variational inequalities. Math. Program. 165(1):113–149
2017
Later among the works it cites.
2017
Later among the works it cites.
Kolossoski O, Monteiro R (2017) An accelerated non-euclidean hybrid proximal extragradient-type algorithm for convex–concave saddle-point problems. Optim. Methods Softw. 32(6):1244–1272
2017
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Vershynin R (2018) High-Dimensional Probability: (Cambridge University Press)
2018
Later among the works it cites.
Zhao R, Cevher V (2018) Stochastic three-composite convex minimization with a linear operator. Proc. AISTATS (Lanzarote, Spain)
2018
Later among the works it cites.
Zhao R, Haskell WB, Tan VYF (2019) An optimal algorithm for stochastic three-composite optimization. Proc. AISTATS (Okinawa, Japan)
2019
Closest in time.
Ghadimi S, Lan G (2013) Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization II: Shrinking procedures and optimal algorithms. SIAM J. Optim. 23(4):2061–2089
2089
Closest in time.