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Efficiency of coordinate descent methods on huge-scale optimization problems

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Cited by:

  1. Sarah Perrin & Thierry Roncalli, 2019. "Machine Learning Optimization Algorithms & Portfolio Allocation," Papers 1909.10233, arXiv.org.
  2. Sjur Didrik Flåm, 2024. "Via Order Markets Towards Price-Taking Equilibrium," Journal of Optimization Theory and Applications, Springer, vol. 201(3), pages 977-994, June.
  3. Reza Eghbali & Maryam Fazel, 2017. "Decomposable norm minimization with proximal-gradient homotopy algorithm," Computational Optimization and Applications, Springer, vol. 66(2), pages 345-381, March.
  4. Malka N. Halgamuge & Eshan Daminda & Ampalavanapillai Nirmalathas, 2020. "Best optimizer selection for predicting bushfire occurrences using deep learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 845-860, August.
  5. Lee, Dongkyu & Song, Junho, 2023. "Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
  6. Aviad Aberdam & Amir Beck, 2022. "An Accelerated Coordinate Gradient Descent Algorithm for Non-separable Composite Optimization," Journal of Optimization Theory and Applications, Springer, vol. 193(1), pages 219-246, June.
  7. Abbaszadehpeivasti, Hadi & de Klerk, Etienne & Zamani, Moslem, 2023. "Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming," Other publications TiSEM 88512ac0-c26a-4a99-b840-3, Tilburg University, School of Economics and Management.
  8. R. Lopes & S. A. Santos & P. J. S. Silva, 2019. "Accelerating block coordinate descent methods with identification strategies," Computational Optimization and Applications, Springer, vol. 72(3), pages 609-640, April.
  9. Yangyang Xu, 2019. "Asynchronous parallel primal–dual block coordinate update methods for affinely constrained convex programs," Computational Optimization and Applications, Springer, vol. 72(1), pages 87-113, January.
  10. Flavia Chorobura & Ion Necoara, 2024. "Coordinate descent methods beyond smoothness and separability," Computational Optimization and Applications, Springer, vol. 88(1), pages 107-149, May.
  11. Masoud Ahookhosh & Le Thi Khanh Hien & Nicolas Gillis & Panagiotis Patrinos, 2021. "Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 79(3), pages 681-715, July.
  12. Ion Necoara & Andrei Patrascu, 2014. "A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints," Computational Optimization and Applications, Springer, vol. 57(2), pages 307-337, March.
  13. Filip Hanzely & Peter Richtárik, 2021. "Fastest rates for stochastic mirror descent methods," Computational Optimization and Applications, Springer, vol. 79(3), pages 717-766, July.
  14. Mingyi Hong & Tsung-Hui Chang & Xiangfeng Wang & Meisam Razaviyayn & Shiqian Ma & Zhi-Quan Luo, 2020. "A Block Successive Upper-Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 45(3), pages 833-861, August.
  15. Nicolas Loizou & Peter Richtárik, 2020. "Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods," Computational Optimization and Applications, Springer, vol. 77(3), pages 653-710, December.
  16. Hu, Chaoming & Wan, Zhao Man & Zhu, Saihua & Wan, Zhong, 2022. "An integrated stochastic model and algorithm for constrained multi-item newsvendor problems by two-stage decision-making approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 280-300.
  17. Olivier Fercoq & Zheng Qu, 2020. "Restarting the accelerated coordinate descent method with a rough strong convexity estimate," Computational Optimization and Applications, Springer, vol. 75(1), pages 63-91, January.
  18. Dvurechensky, Pavel & Gorbunov, Eduard & Gasnikov, Alexander, 2021. "An accelerated directional derivative method for smooth stochastic convex optimization," European Journal of Operational Research, Elsevier, vol. 290(2), pages 601-621.
  19. Yen, Yu-Min & Yen, Tso-Jung, 2014. "Solving norm constrained portfolio optimization via coordinate-wise descent algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 737-759.
  20. Tao Sun & Yuejiao Sun & Yangyang Xu & Wotao Yin, 2020. "Markov chain block coordinate descent," Computational Optimization and Applications, Springer, vol. 75(1), pages 35-61, January.
  21. Adrien B. Taylor & Julien M. Hendrickx & François Glineur, 2018. "Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization," Journal of Optimization Theory and Applications, Springer, vol. 178(2), pages 455-476, August.
  22. A. Ghaffari-Hadigheh & L. Sinjorgo & R. Sotirov, 2024. "On convergence of a q-random coordinate constrained algorithm for non-convex problems," Journal of Global Optimization, Springer, vol. 90(4), pages 843-868, December.
  23. Abbaszadehpeivasti, Hadi, 2024. "Performance analysis of optimization methods for machine learning," Other publications TiSEM 3050a62d-1a1f-494e-99ef-7, Tilburg University, School of Economics and Management.
  24. Du, Kui, 2024. "Regularized randomized iterative algorithms for factorized linear systems," Applied Mathematics and Computation, Elsevier, vol. 466(C).
  25. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
  26. Isaac M. Ross, 2023. "Derivation of Coordinate Descent Algorithms from Optimal Control Theory," SN Operations Research Forum, Springer, vol. 4(2), pages 1-11, June.
  27. V. S. Amaral & R. Andreani & E. G. Birgin & D. S. Marcondes & J. M. Martínez, 2022. "On complexity and convergence of high-order coordinate descent algorithms for smooth nonconvex box-constrained minimization," Journal of Global Optimization, Springer, vol. 84(3), pages 527-561, November.
  28. Sjur Didrik Flåm, 2019. "Blocks of coordinates, stochastic programming, and markets," Computational Management Science, Springer, vol. 16(1), pages 3-16, February.
  29. Yangyang Xu & Shuzhong Zhang, 2018. "Accelerated primal–dual proximal block coordinate updating methods for constrained convex optimization," Computational Optimization and Applications, Springer, vol. 70(1), pages 91-128, May.
  30. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
  31. Cassioli, A. & Di Lorenzo, D. & Sciandrone, M., 2013. "On the convergence of inexact block coordinate descent methods for constrained optimization," European Journal of Operational Research, Elsevier, vol. 231(2), pages 274-281.
  32. David Kozak & Stephen Becker & Alireza Doostan & Luis Tenorio, 2021. "A stochastic subspace approach to gradient-free optimization in high dimensions," Computational Optimization and Applications, Springer, vol. 79(2), pages 339-368, June.
  33. David Degras, 2021. "Sparse group fused lasso for model segmentation: a hybrid approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 625-671, September.
  34. Masoud Ahookhosh & Le Thi Khanh Hien & Nicolas Gillis & Panagiotis Patrinos, 2021. "A Block Inertial Bregman Proximal Algorithm for Nonsmooth Nonconvex Problems with Application to Symmetric Nonnegative Matrix Tri-Factorization," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 234-258, July.
  35. Aharon Ben-Tal & Arkadi Nemirovski, 2015. "On Solving Large-Scale Polynomial Convex Problems by Randomized First-Order Algorithms," Mathematics of Operations Research, INFORMS, vol. 40(2), pages 474-494, February.
  36. Quoc Tran-Dinh, 2019. "Proximal alternating penalty algorithms for nonsmooth constrained convex optimization," Computational Optimization and Applications, Springer, vol. 72(1), pages 1-43, January.
  37. Ion Necoara & Yurii Nesterov & François Glineur, 2017. "Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks," Journal of Optimization Theory and Applications, Springer, vol. 173(1), pages 227-254, April.
  38. Ron Shefi & Marc Teboulle, 2016. "On the rate of convergence of the proximal alternating linearized minimization algorithm for convex problems," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 4(1), pages 27-46, February.
  39. Edit Csizmás & Rajmund Drenyovszki & Tamás Szántai & Csaba I. Fábián, 2025. "Random Descent Steps in a Probability Maximization Scheme," Journal of Optimization Theory and Applications, Springer, vol. 205(1), pages 1-26, April.
  40. Jinlong Lei & Uday V. Shanbhag, 2020. "Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization," Operations Research, INFORMS, vol. 68(6), pages 1742-1766, November.
  41. Majid Jahani & Naga Venkata C. Gudapati & Chenxin Ma & Rachael Tappenden & Martin Takáč, 2021. "Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences," Computational Optimization and Applications, Springer, vol. 79(2), pages 369-404, June.
  42. Lebeau, Alexis & Petitet, Marie & Quemin, Simon & Saguan, Marcelo, 2024. "Long-term issues with the Energy-Only Market design in the context of deep decarbonization," Energy Economics, Elsevier, vol. 132(C).
  43. Anastasiya Ivanova & Pavel Dvurechensky & Evgeniya Vorontsova & Dmitry Pasechnyuk & Alexander Gasnikov & Darina Dvinskikh & Alexander Tyurin, 2022. "Oracle Complexity Separation in Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 193(1), pages 462-490, June.
  44. Aleksandr Lobanov & Nail Bashirov & Alexander Gasnikov, 2024. "The “Black-Box” Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation," Journal of Optimization Theory and Applications, Springer, vol. 203(3), pages 2451-2486, December.
  45. Sjur Didrik Flåm, 2020. "Emergence of price-taking Behavior," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 70(3), pages 847-870, October.
  46. Ruoyu Sun & Zhi-Quan Luo & Yinyu Ye, 2020. "On the Efficiency of Random Permutation for ADMM and Coordinate Descent," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 233-271, February.
  47. Stephan Eckstein & Michael Kupper & Mathias Pohl, 2020. "Robust risk aggregation with neural networks," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1229-1272, October.
  48. Jin Zhang & Xide Zhu, 2022. "Linear Convergence of Prox-SVRG Method for Separable Non-smooth Convex Optimization Problems under Bounded Metric Subregularity," Journal of Optimization Theory and Applications, Springer, vol. 192(2), pages 564-597, February.
  49. TAYLOR, Adrien B. & HENDRICKX, Julien M. & François GLINEUR, 2016. "Exact worst-case performance of first-order methods for composite convex optimization," LIDAM Discussion Papers CORE 2016052, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  50. Leandro Farias Maia & David Huckleberry Gutman & Ryan Christopher Hughes, 2024. "The Inexact Cyclic Block Proximal Gradient Method and Properties of Inexact Proximal Maps," Journal of Optimization Theory and Applications, Springer, vol. 201(2), pages 668-698, May.
  51. Jinlong Lei & Uday V. Shanbhag & Jong-Shi Pang & Suvrajeet Sen, 2020. "On Synchronous, Asynchronous, and Randomized Best-Response Schemes for Stochastic Nash Games," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 157-190, February.
  52. Guillaume Olikier & André Uschmajew & Bart Vandereycken, 2025. "Gauss–Southwell Type Descent Methods for Low-Rank Matrix Optimization," Journal of Optimization Theory and Applications, Springer, vol. 206(1), pages 1-32, July.
  53. ARAVENA, Ignacio & PAPAVASILIOU, Anthony, 2016. "An Asynchronous Distributed Algorithm for solving Stochastic Unit Commitment," LIDAM Discussion Papers CORE 2016038, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  54. Khan, Mohd Shariq & I.A. Karimi, & Bahadori, Alireza & Lee, Moonyong, 2015. "Sequential coordinate random search for optimal operation of LNG (liquefied natural gas) plant," Energy, Elsevier, vol. 89(C), pages 757-767.
  55. Elson Tomás & Susana Vinga & Alexandra M. Carvalho, 2017. "Unsupervised learning of pharmacokinetic responses," Computational Statistics, Springer, vol. 32(2), pages 409-428, June.
  56. Varron, Davit, 2016. "Empirical likelihood confidence tubes for functional parameters in plug-in estimation," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 100-118.
  57. Fu, Sheng & Zhang, Sanguo & Liu, Yufeng, 2018. "Adaptively weighted large-margin angle-based classifiers," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 282-299.
  58. Rachael Tappenden & Peter Richtárik & Jacek Gondzio, 2016. "Inexact Coordinate Descent: Complexity and Preconditioning," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 144-176, July.
  59. Andrei Patrascu & Ion Necoara, 2015. "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization," Journal of Global Optimization, Springer, vol. 61(1), pages 19-46, January.
  60. Qin Wang & Weiguo Li & Wendi Bao & Feiyu Zhang, 2022. "Accelerated Randomized Coordinate Descent for Solving Linear Systems," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
  61. Immanuel Bomze & Francesco Rinaldi & Damiano Zeffiro, 2025. "Projection free methods on product domains," Computational Optimization and Applications, Springer, vol. 91(2), pages 511-540, June.
  62. Kimon Fountoulakis & Rachael Tappenden, 2018. "A flexible coordinate descent method," Computational Optimization and Applications, Springer, vol. 70(2), pages 351-394, June.
  63. Mareček, Jakub & Richtárik, Peter & Takáč, Martin, 2017. "Matrix completion under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 256(1), pages 35-43.
  64. Martin Morin & Pontus Giselsson, 2025. "Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods," Journal of Optimization Theory and Applications, Springer, vol. 205(3), pages 1-23, June.
  65. Chenxi Chen & Yunmei Chen & Yuyuan Ouyang & Eduardo Pasiliao, 2018. "Stochastic Accelerated Alternating Direction Method of Multipliers with Importance Sampling," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 676-695, November.
  66. Zhigang Li & Mingchuan Zhang & Junlong Zhu & Ruijuan Zheng & Qikun Zhang & Qingtao Wu, 2018. "Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization," Complexity, Hindawi, vol. 2018, pages 1-11, December.
  67. Laura Palagi & Ruggiero Seccia, 2020. "Block layer decomposition schemes for training deep neural networks," Journal of Global Optimization, Springer, vol. 77(1), pages 97-124, May.
  68. Md Sarowar Morshed & Md Saiful Islam & Md. Noor-E-Alam, 2020. "Accelerated sampling Kaczmarz Motzkin algorithm for the linear feasibility problem," Journal of Global Optimization, Springer, vol. 77(2), pages 361-382, June.
  69. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
  70. Veprikov, Andrey & Bogdanov, Alexander & Minashkin, Vladislav & Beznosikov, Aleksandr, 2024. "New aspects of black box conditional gradient: Variance reduction and one point feedback," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
  71. Jean-Charles Richard & Thierry Roncalli, 2019. "Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles," Papers 1902.05710, arXiv.org.
  72. Ching-pei Lee & Stephen J. Wright, 2020. "Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 151-187, April.
  73. Duy Khuong Nguyen & Tu Bao Ho, 2017. "Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization," Journal of Global Optimization, Springer, vol. 68(2), pages 307-328, June.
  74. Ahmed Khaled & Othmane Sebbouh & Nicolas Loizou & Robert M. Gower & Peter Richtárik, 2023. "Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization," Journal of Optimization Theory and Applications, Springer, vol. 199(2), pages 499-540, November.
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