All papers are listed below in reverse chronological order in which they appeared online.

Prepared in 2019

[114] Ahmed Khaled, Konstantin Mishchenko and Peter Richtárik
Better communication complexity for local SGD
Federated Learning Paper
[arXiv] [code: local SGD]

[113] Ahmed Khaled and Peter Richtárik
Gradient descent with compressed iterates
Federated Learning Paper
[arXiv] [code: GDCI]

[112] Ahmed Khaled, Konstantin Mishchenko and Peter Richtárik
First analysis of local GD on heterogeneous data
Federated Learning Paper
[arXiv] [code: local GD]

[111] Jinhui Xiong, Peter Richtárik and Wolfgang Heidrich
Stochastic convolutional sparse coding
International Symposium on Vision, Modeling and Visualization 2019
VMV Best Paper Award, 2019
[arXiv] [code: SBCSC, SOCSC]

[110] Xun Qian, Zheng Qu and Peter Richtárik
L-SVRG and L-Katyusha with arbitrary sampling
[arXiv] [code: L-SVRG, L-Katyusha]

[109] Xun Qian, Alibek Sailanbayev, Konstantin Mishchenko and Peter Richtárik
MISO is making a comeback with better proofs and rates
[arXiv] [code: MISO]

[108] Eduard Gorbunov, Adel Bibi, Ozan Sezer, El Houcine Bergou and Peter Richtárik
A stochastic derivative free optimization method with momentum
[arXiv] [code: SMTP]

[107] Mher Safaryan and Peter Richtárik
On stochastic sign descent methods
[arXiv] [code: signSGD, signSGDmaj]

[106] Adil Salim, Dmitry Kovalev and Peter Richtárik
Stochastic proximal Langevin algorithm: potential splitting and nonasymptotic rates
to appear in NeurIPS 2019
[arXiv] [code: SPLA]

[105] Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini and Peter Richtárik
Direct nonlinear acceleration
[arXiv] [code: DNA]

[104] Konstantin Mishchenko and Peter Richtárik
A stochastic decoupling method for minimizing the sum of smooth and non-smooth functions
[arXiv] [code: SDM]

[103] Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik and Yura Malitsky
Revisiting stochastic extragradient
[arXiv]

[102] Filip Hanzely and Peter Richtárik
One method to rule them all: variance reduction for data, parameters and many new methods
[arXiv] [code: GJS + 17 algorithms]

[101] Eduard Gorbunov, Filip Hanzely and Peter Richtárik
A unified theory of SGD: variance reduction, sampling, quantization and coordinate descent
[arXiv]

[100] Samuel Horváth, Chen-Yu Ho, Ľudovít Horváth, Atal Narayan Sahu, Marco Canini and Peter Richtárik
Natural compression for distributed deep learning
[arXiv]

[99] Robert M. Gower, Dmitry Kovalev, Felix Lieder and Peter Richtárik
RSN: Randomized Subspace Newton
to appear in NeurIPS 2019
[arXiv]

[98] Aritra Dutta, Filip Hanzely, Jingwei Liang and Peter Richtárik
Best pair formulation & accelerated scheme for non-convex principal component pursuit
[arXiv]

[97] Nicolas Loizou and Peter Richtárik
Revisiting randomized gossip algorithms: general framework, convergence rates and novel block and accelerated protocols
[arXiv]

[96] Nicolas Loizou and Peter Richtárik
Convergence analysis of inexact randomized iterative methods
[arXiv] [code: iBasic, iSDSA, iSGD, iSPM, iRBK, iRBCD]

[95] Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports and Peter Richtárik
Scaling distributed machine learning with in-network aggregation
[arXiv] [code: SwitchML]

[94] Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik and Sebastian Stich
Stochastic distributed learning with gradient quantization and variance reduction
[arXiv] [code: DIANA, VR-DIANA, SVRG-DIANA]

[93] El Houcine Bergou, Eduard Gorbunov and Peter Richtárik
Stochastic three points method for unconstrained smooth minimization
[arXiv] [code: STP]

[92] Adel Bibi, El Houcine Bergou, Ozan Sener, Bernard Ghanem and Peter Richtárik
A stochastic derivative-free optimization method with importance sampling
To appear in AAAI-20
[arXiv] [code: STP_IS]

[91] Konstantin Mishchenko, Filip Hanzely and Peter Richtárik
99% of distributed optimization is a waste of time: the issue and how to fix it
[arXiv] [code: IBCD, ISAGA, ISGD, IASGD, ISEGA]

[90] Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč and Peter Richtárik
Distributed learning with compressed gradient differences
[arXiv] [code: DIANA]

[89] Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin and Peter Richtárik
SGD: general analysis and improved rates
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5200-5209, 2019
[arXiv] [poster] [code: SGD-AS]

[88] Dmitry Kovalev, Samuel Horváth and Peter Richtárik
Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop
[arXiv] [code: L-SVRG, L-Katyusha]

[87] Xun Qian, Zheng Qu and Peter Richtárik
SAGA with arbitrary sampling
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5190-5199, 2019
[arXiv] [poster] [code: SAGA-AS]

Prepared in 2018

[86] Lam M. Nguyen, Phuong Ha Nguyen, P. Richtárik, Katya Scheinberg, Martin Takáč and Marten van Dijk
New convergence aspects of stochastic gradient algorithms
To appear in Journal of Machine Learning Research
[arXiv]

[85] Filip Hanzely, Jakub Konečný, Nicolas Loizou, Peter Richtárik and Dmitry Grishchenko
A privacy preserving randomized gossip algorithm via controlled noise insertion
NeurIPS Privacy Preserving Machine Learning Workshop, 2018
[arXiv] [poster]

[84] Konstantin Mishchenko and Peter Richtárik
A stochastic penalty model for convex and nonconvex optimization with big constraints
[arXiv]

[83] Nicolas Loizou, Michael G. Rabbat and Peter Richtárik
Provably accelerated randomized gossip algorithms
[arXiv] [code: AccGossip]

[82] Filip Hanzely and Peter Richtárik
Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
Proceedings of the 22nd Int. Conf. on Artificial Intelligence and Statistics, PMLR 89:304-312, 2019
[arXiv] [poster] [code: ACD]

[81] Samuel Horváth and Peter Richtárik
Nonconvex variance reduced optimization with arbitrary sampling
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2781-2789, 2019
Horváth: Best DS3 Poster Award, Paris, 2018
(link)
[arXiv] [poster] [code: SVRG, SAGA, SARAH]

[80] Filip Hanzely, Konstantin Mishchenko and Peter Richtárik
SEGA: Variance reduction via gradient sketching
Advances in Neural Information Processing Systems 31:2082-2093, 2018
[arXiv] [poster] [slides] [code: SEGA] [video: YouTube]

[79] Filip Hanzely, Peter Richtárik and Lin Xiao
Accelerated Bregman proximal gradient methods for relatively smooth convex optimization

[arXiv] [code: ABPG, ABDA]

[78] Jakub Mareček, Peter Richtárik and Martin Takáč
Matrix completion under interval uncertainty: highlights
Lecture Notes in Computer Science, ECML-PKDD 2018
[pdf]

[77] Nicolas Loizou and Peter Richtárik
Accelerated gossip via stochastic heavy ball method
56th Annual Allerton Conference on Communication, Control, and Computing, 927-934, 2018
Press coverage [KAUST Discovery]
[arXiv] [poster]

[76] Adel Bibi, Alibek Sailanbayev, Bernard Ghanem, Robert Mansel Gower and Peter Richtárik
Improving SAGA via a probabilistic interpolation with gradient descent
[arXiv] [code: SAGD]

[75] Aritra Dutta, Filip Hanzely and Peter Richtárik
A nonconvex projection method for robust PCA
The Thirty-Third AAAI Conference on Artificial Intelligence, 2019 (AAAI-19)
[arXiv]

[74] Robert M. Gower, Peter Richtárik and Francis Bach
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching
[arXiv] [slides] [code: JacSketch] [video: YouTube]

[73] Aritra Dutta, Xin Li and Peter Richtárik
Weighted low-rank approximation of matrices and background modeling
[arXiv]

[72] Filip Hanzely and Peter Richtárik
Fastest rates for stochastic mirror descent methods

[arXiv]

[71] Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, P. Richtárik, Katya Scheinberg and Martin Takáč
SGD and Hogwild! convergence without the bounded gradients assumption
Proceedings of The 35th International Conference on Machine Learning, PMLR 80:3750-3758, 2018
[arXiv]

[70] Robert M. Gower, Filip Hanzely, Peter Richtárik and Sebastian Stich
Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization

Advances in Neural Information Processing Systems 31:1619-1629, 2018
[arXiv] [poster] [code: ABFGS]

[69] Nikita Doikov and Peter Richtárik
Randomized block cubic Newton method
Proceedings of The 35th International Conference on Machine Learning, PMLR 80:1290-1298, 2018
Doikov: Best Talk Award, "Control, Information and Optimization", Voronovo, Russia, 2018
[arXiv] [bib] [code: RBCN]

[68] Dmitry Kovalev, Eduard Gorbunov, Elnur Gasanov and Peter Richtárik
Stochastic spectral and conjugate descent methods

Advances in Neural Information Processing Systems 31:3358-3367, 2018
[arXiv] [poster] [code: SSD, SconD, SSCD, mSSCD, iSconD, iSSD]

[67] Radoslav Harman, Lenka Filová and Peter Richtárik
A randomized exchange algorithm for computing optimal approximate designs of experiments

Journal of the American Statistical Association
[arXiv] [code: REX, OD_REX, MVEE_REX]

[66] Ion Necoara, Andrei Patrascu and Peter Richtárik
Randomized projection methods for convex feasibility problems: conditioning and convergence rates
To appear in SIAM Journal on Optimization
[arXiv] [slides]

Prepared in 2017

[65] Nicolas Loizou and Peter Richtárik
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods
[arXiv]

[64] Aritra Dutta and Peter Richtárik
Online and batch supervised background estimation via L1 regression
IEEE Winter Conference on Applications in Computer Vision, 2019
[arXiv]

[63] Nicolas Loizou and Peter Richtárik
Linearly convergent stochastic heavy ball method for minimizing generalization error
NIPS Workshop on Optimization for Machine Learning, 2017
[arXiv] [poster]

[62] Dominik Csiba and Peter Richtárik
Global convergence of arbitrary-block gradient methods for generalized Polyak-Łojasiewicz functions
[arXiv]

[61] Ademir Alves Ribeiro and Peter Richtárik
The complexity of primal-dual fixed point methods for ridge regression
Linear Algebra and its Applications 556:342-372, 2018
[arXiv]

[60] Matthias J. Ehrhardt, Pawel Markiewicz, Antonin Chambolle, Peter Richtárik, Jonathan Schott and Carola-Bibiane Schoenlieb
Faster PET reconstruction with a stochastic primal-dual hybrid gradient method
Proceedings of SPIE, Wavelets and Sparsity XVII, Volume 10394, pages 1039410-1 - 1039410-11, 2017
[pdf] [poster] [code: SPDHG] [video: YouTube]

[59] Aritra Dutta, Xin Li and Peter Richtárik
A batch-incremental video background estimation model using weighted low-rank approximation of matrices
IEEE International Conference on Computer Vision (ICCV) Workshops, 2017
[arXiv] [code: inWLR]

[58] Filip Hanzely, Jakub Konečný, Nicolas Loizou, Peter Richtárik and Dmitry Grishchenko
Privacy preserving randomized gossip algorithms
[arXiv] [slides]

[57] Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik and Carola-Bibiane Schoenlieb
Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications
SIAM Journal on Optimization 28(4):2783-2808, 2018
[arXiv] [slides] [poster] [code: SPDHG] [video: YouTube]

[56] Peter Richtárik and Martin Takáč
Stochastic reformulations of linear systems: algorithms and convergence theory
[arXiv] [slides] [code: basic, parallel and accelerated methods]

[55] Mojmír Mutný and Peter Richtárik
Parallel stochastic Newton method
Journal of Computational Mathematics 36(3):404-425, 2018
[arXiv] [code: PSNM]


Prepared in 2016

[54] Robert M. Gower and Peter Richtárik
Linearly convergent randomized iterative methods for computing the pseudoinverse
[arXiv] [bib]

[53] Jakub Konečný and Peter Richtárik
Randomized distributed mean estimation: accuracy vs communication
Frontiers in Applied Mathematics and Statistics 2018
Federated Learning Paper
[arXiv] [bib]

[52] Jakub Konečný, H. Brendan McMahan, Felix Yu, P. Richtárik, Ananda Theertha Suresh and Dave Bacon
Federated learning: strategies for improving communication efficiency
NIPS Private Multi-Party Machine Learning Workshop, 2016
Federated Learning Paper link [selected press coverage: The Verge - Quartz - Vice CBR - Android Authority]
[arXiv] [bib] [poster]

[51] Jakub Konečný, H. Brendan McMahan, Daniel Ramage and Peter Richtárik
Federated optimization: distributed machine learning for on-device intelligence
Federated Learning Paper link [selected press coverage: The Verge - Quartz - Vice CBR - Android Authority]
[arXiv] [bib]

[50] Nicolas Loizou and Peter Richtárik
A new perspective on randomized gossip algorithms
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 440-444, 2016
[arXiv] [bib]

[49] Sashank J. Reddi, Jakub Konečný, Peter Richtárik, Barnabás Póczos, Alex Smola
AIDE: fast and communication efficient distributed optimization
[arXiv] [poster]

[48] Dominik Csiba and Peter Richtárik
Coordinate descent face-off: primal or dual?
Proceedings of Algorithmic Learning Theory, PMLR 83:246-267, 2018
[arXiv] [bib]

[47] Olivier Fercoq and Peter Richtárik
Optimization in high dimensions via accelerated, parallel and proximal coordinate descent
SIAM Review 58(4):739-771, 2016
SIAM SIGEST Award
[arXiv] [bib]

[46] Robert M. Gower, Donald Goldfarb and Peter Richtárik
Stochastic block BFGS: squeezing more curvature out of data
Proceedings of the 33rd International Conference on Machine Learning, PMLR 48:1869-1878, 2016
[arXiv] [bib] [poster]

[45] Dominik Csiba and Peter Richtárik
Importance sampling for minibatches
Journal of Machine Learning Research 19(27):1-21, 2018
[arXiv] [bib]

[44] Robert M. Gower and Peter Richtárik
Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms
SIAM Journal on Matrix Analysis and Applications 38(4):1380-1409, 2017
Most Downloaded SIMAX Paper (6th place: 2018)
[arXiv] [bib] [code: SIMI, RBFGS, AdaRBFGS, ...]


Prepared in 2015

[43] Zeyuan Allen-Zhu, Zheng Qu, Peter Richtárik and Yang Yuan
Even faster accelerated coordinate descent using non-uniform sampling
Proceedings of the 33rd International Conference on Machine Learning, PMLR 48:1110-1119, 2016
[arXiv] [bib] [code: NU_ACDM]

[42] Robert M. Gower and Peter Richtárik
Stochastic dual ascent for solving linear systems
[arXiv] [code: SDA] [video: YouTube]

[41] Chenxin Ma, Jakub Konečný, Martin Jaggi, Virginia Smith, Michael I Jordan, P. Richtárik and Martin Takáč Distributed optimization with arbitrary local solvers
Optimization Methods and Software 32(4):813-848, 2017
Most-Read Paper, Optimization Methods and Software, 2017
[arXiv] [code: CoCoA+]

[40] Martin Takáč, Peter Richtárik and Nathan Srebro
Distributed mini-batch SDCA
to appear in: Journal of Machine Learning Research, 2018
[arXiv]

[39] Robert M. Gower and Peter Richtárik
Randomized iterative methods for linear systems
SIAM Journal on Matrix Analysis and Applications 36(4):1660-1690, 2015
Most Downloaded SIMAX Paper (1st place: 2017; 2nd place: 2018)

Gower: 18th IMA Leslie Fox Prize (2nd Prize), 2017
link
[arXiv] [slides]

[38] Dominik Csiba and Peter Richtárik
Primal method for ERM with flexible mini-batching schemes and non-convex losses
[arXiv] [code: dfSDCA]

[37] Jakub Konečný, Jie Liu, Peter Richtárik and Martin Takáč
Mini-batch semi-stochastic gradient descent in the proximal setting
IEEE Journal of Selected Topics in Signal Processing 10(2): 242-255, 2016
[arXiv] [code: mS2GD]

[36] Rachael Tappenden, Martin Takáč and Peter Richtárik
On the complexity of parallel coordinate descent
Optimization Methods and Software 33(2):372-395, 2018
[arXiv]

[35] Dominik Csiba, Zheng Qu and Peter Richtárik
Stochastic dual coordinate ascent with adaptive probabilities
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:674-683, 2015
Csiba: Best Contribution Award (2nd Place), Optimization and Big Data 2015
Implemented in Tensor Flow
[arXiv] [bib] [poster] [code: AdaSDCA and AdaSDCA+]

[34] Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik and Martin Takáč
Adding vs. averaging in distributed primal-dual optimization
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1973-1982, 2015
Smith: 2015 MLconf Industry Impact Student Research Award link
CoCoA+ is now the default linear optimizer in Tensor Flow link
[arXiv] [bib] [poster] [code: CoCoA+]

[33] Zheng Qu, Peter Richtárik, Martin Takáč and Olivier Fercoq
SDNA: Stochastic dual Newton ascent for empirical risk minimization
Proceedings of the 33rd International Conference on Machine Learning, PMLR 48:1823-1832, 2016
[arXiv] [bib] [slides] [poster] [code: SDNA]


Prepared in 2014

[32] Zheng Qu and Peter Richtárik
Coordinate descent with arbitrary sampling II: expected separable overapproximation
Optimization Methods and Software 31(5):858-884, 2016
[arXiv]

[31] Zheng Qu and Peter Richtárik
Coordinate descent with arbitrary sampling I: algorithms and complexity
Optimization Methods and Software 31(5):829-857, 2016
[arXiv] [code: ALPHA]

[30] Jakub Konečný, Zheng Qu and Peter Richtárik
Semi-stochastic coordinate descent
Optimization Methods and Software 32(5):993-1005, 2017
[arXiv] [code: S2CD]

[29] Zheng Qu, Peter Richtárik and Tong Zhang
Quartz: Randomized dual coordinate ascent with arbitrary sampling
Advances in Neural Information Processing Systems 28:865-873, 2015
[arXiv] [slides] [code: QUARTZ] [video: YouTube]

[28] Jakub Konečný, Jie Liu, Peter Richtárik and Martin Takáč
mS2GD: Mini-batch semi-stochastic gradient descent in the proximal setting
NIPS Workshop on Optimization for Machine Learning, 2014
[arXiv] [poster] [code: mS2GD]

[27] Jakub Konečný, Zheng Qu and Peter Richtárik
S2CD: Semi-stochastic coordinate descent
NIPS Workshop on Optimization for Machine Learning, 2014
[pdf] [poster] [code: S2CD]

[26] Jakub Konečný and Peter Richtárik
Simple complexity analysis of simplified direct search
[arXiv] [slides in Slovak] [code: SDS]

[25] Jakub Mareček, Peter Richtárik and Martin Takáč
Distributed block coordinate descent for minimizing partially separable functions
Numerical Analysis and Optimization, Springer Proceedings in Math. and Statistics 134:261-288, 2015
[arXiv]

[24] Olivier Fercoq, Zheng Qu, Peter Richtárik and Martin Takáč
Fast distributed coordinate descent for minimizing non-strongly convex losses
2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2014
[arXiv] [poster] [code: Hydra^2]

[23] Duncan Forgan and Peter Richtárik
On optimal solutions to planetesimal growth models
Technical Report ERGO 14-002, 2014
[pdf]

[22] Jakub Mareček, Peter Richtárik and Martin Takáč
Matrix completion under interval uncertainty
European Journal of Operational Research 256(1):35-42, 2017
[arXiv] [code: MACO]


Prepared in 2013

[21] Olivier Fercoq and Peter Richtárik
Accelerated, Parallel and PROXimal coordinate descent
SIAM Journal on Optimization 25(4):1997-2023, 2015
Fercoq: 17th IMA Leslie Fox Prize (Second Prize), 2015
2nd Most Downloaded SIOPT Paper (Aug 2016 - now)
[arXiv] [poster] [code: APPROX] [video: YouTube]

[20] Jakub Konečný and Peter Richtárik
Semi-stochastic gradient descent methods
Frontiers in Applied Mathematics and Statistics 3:9, 2017
[arXiv] [poster] [slides] [code: S2GD and S2GD+]

[19] Peter Richtárik and Martin Takáč
On optimal probabilities in stochastic coordinate descent methods
Optimization Letters 10(6):1233-1243, 2016
[arXiv] [poster] [code: NSync]

[18] Peter Richtárik and Martin Takáč
Distributed coordinate descent method for learning with big data
Journal of Machine Learning Research 17(75):1-25, 2016
[arXiv] [poster] [code: Hydra]

[17] Olivier Fercoq and Peter Richtárik
Smooth minimization of nonsmooth functions with parallel coordinate descent methods
Springer Proceedings in Mathematics and Statistics 279:57-96, 2019
[arXiv] [code: SPCDM]

[16] Rachael Tappenden, Peter Richtárik and Burak Buke
Separable approximations and decomposition methods for the augmented Lagrangian
Optimization Methods and Software 30(3):643-668, 2015
[arXiv]

[15] Rachael Tappenden, Peter Richtárik and Jacek Gondzio
Inexact coordinate descent: complexity and preconditioning
Journal of Optimization Theory and Applications 170(1):144-176, 2016
[arXiv] [poster] [code: ICD]

[14] Martin Takáč, Selin Damla Ahipasaoglu, Ngai-Man Cheung and Peter Richtárik
TOP-SPIN: TOPic discovery via Sparse Principal component INterference
Springer Proceedings in Mathematics and Statistics 279:157-180, 2019
[arXiv] [poster] [code: TOP-SPIN]

[13] Martin Takáč, Avleen Bijral, Peter Richtárik and Nathan Srebro
Mini-batch primal and dual methods for SVMs
Proceedings of the 30th International Conference on Machine Learning, 2013
[arXiv] [poster] [code: minibatch SDCA and minibatch Pegasos]


Prepared in 2012 or earlier

[12] Peter Richtárik, Martin Takáč and Selin Damla Ahipasaoglu
Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes
[arXiv] [code: 24am]

[11] William Hulme, Peter Richtárik, Lynne McGuire and Alison Green
Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on SVM classification of RT-QuIC data
Technical Report, 2012
[arXiv]

[10] Peter Richtárik and Martin Takáč
Parallel coordinate descent methods for big data optimization
Mathematical Programming 156(1):433-484, 2016
Takáč: 16th IMA Leslie Fox Prize (2nd Prize), 2013 link
#1 Top Trending Article in Mathematical Programming Ser A and B (2017) link
[arXiv] [slides] [code: PCDM, AC/DC] [video: YouTube]

[9] Peter Richtárik and Martin Takáč
Efficient serial and parallel coordinate descent methods for huge-scale truss topology design
Operations Research Proceedings 2011:27-32, Springer-Verlag, 2012
[Optimization Online] [poster]

[8] Peter Richtárik and Martin Takáč
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Mathematical Programming 144(2):1-38, 2014
Best Student Paper (runner-up), INFORMS Computing Society, 2012
[arXiv] [slides]

[7] Peter Richtárik and Martin Takáč
Efficiency of randomized coordinate descent methods on minimization problems with a composite objective function
Proceedings of Signal Processing with Adaptive Sparse Structured Representations, 2011
[pdf]

[6] Peter Richtárik
Finding sparse approximations to extreme eigenvectors: generalized power method for sparse PCA and extensions
Proceedings of Signal Processing with Adaptive Sparse Structured Representations, 2011
[pdf]

[5] Peter Richtárik
Approximate level method for nonsmooth convex minimization
Journal of Optimization Theory and Applications 152(2):334–350, 2012
[Optimization Online]

[4] Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre
Generalized power method for sparse principal component analysis
Journal of Machine Learning Research 11:517–553, 2010
[arXiv] [slides] [poster] [code: GPower]

[3] Peter Richtárik
Improved algorithms for convex minimization in relative scale
SIAM Journal on Optimization 21(3):1141–1167, 2011
[pdf] [slides]

[2] Peter Richtárik
Simultaneously solving seven optimization problems in relative scale
Technical Report, 2009
[Optimization Online]

[1] Peter Richtárik
Some algorithms for large-scale convex and linear minimization in relative scale
PhD Dissertation, School of Operations Research and Information Engineering, Cornell University, 2007
[pdf]