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Smooth minimization of non-smooth functions
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Cited by:
- Raphael Hauser & Sergey Shahverdyan & Paul Embrechts, 2014. "A General Duality Relation with Applications in Quantitative Risk Management," Papers 1410.0852, arXiv.org.
- David Müller & Vladimir Shikhman, 2022. "Network manipulation algorithm based on inexact alternating minimization," Computational Management Science, Springer, vol. 19(4), pages 627-664, October.
- Bjarne Grimstad & Brage R. Knudsen, 2020. "Mathematical programming formulations for piecewise polynomial functions," Journal of Global Optimization, Springer, vol. 77(3), pages 455-486, July.
- Hanif D. Sherali & Churlzu Lim, 2007. "Enhancing Lagrangian Dual Optimization for Linear Programs by Obviating Nondifferentiability," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 3-13, February.
- Xiangfeng Wang & Junping Zhang & Wenxing Zhang, 2020. "The distance between convex sets with Minkowski sum structure: application to collision detection," Computational Optimization and Applications, Springer, vol. 77(2), pages 465-490, November.
- DEVOLDER, Olivier, 2011. "Stochastic first order methods in smooth convex optimization," LIDAM Discussion Papers CORE 2011070, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Dimitris Bertsimas & Nishanth Mundru, 2021. "Sparse Convex Regression," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 262-279, January.
- Vincenzo Bonifaci, 2021. "A Laplacian approach to $$\ell _1$$ ℓ 1 -norm minimization," Computational Optimization and Applications, Springer, vol. 79(2), pages 441-469, June.
- Radu Boţ & Christopher Hendrich, 2015. "A variable smoothing algorithm for solving convex optimization problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 124-150, April.
- Guanghui Lan & Yuyuan Ouyang, 2022. "Accelerated gradient sliding for structured convex optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 361-394, June.
- Chengbo Li & Wotao Yin & Hong Jiang & Yin Zhang, 2013. "An efficient augmented Lagrangian method with applications to total variation minimization," Computational Optimization and Applications, Springer, vol. 56(3), pages 507-530, December.
- Chunming Tang & Bo He & Zhenzhen Wang, 2020. "Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming," Mathematics, MDPI, vol. 8(2), pages 1-26, February.
- Vishal Gupta & Paat Rusmevichientong, 2021. "Small-Data, Large-Scale Linear Optimization with Uncertain Objectives," Management Science, INFORMS, vol. 67(1), pages 220-241, January.
- Guoqiang Wang & Bo Yu, 2019. "PAL-Hom method for QP and an application to LP," Computational Optimization and Applications, Springer, vol. 73(1), pages 311-352, May.
- Chao, Shih-Kang & Härdle, Wolfgang K. & Yuan, Ming, 2021.
"Factorisable Multitask Quantile Regression,"
Econometric Theory, Cambridge University Press, vol. 37(4), pages 794-816, August.
- Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2016. "Factorisable multi-task quantile regression," SFB 649 Discussion Papers 2016-057, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Xiubo Liang & Guoqiang Wang & Bo Yu, 2022. "A reduced proximal-point homotopy method for large-scale non-convex BQP," Computational Optimization and Applications, Springer, vol. 81(2), pages 539-567, March.
- Daskalakis, Constantinos & Deckelbaum, Alan & Kim, Anthony, 2015. "Near-optimal no-regret algorithms for zero-sum games," Games and Economic Behavior, Elsevier, vol. 92(C), pages 327-348.
- Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2022. "Essentials of numerical nonsmooth optimization," Annals of Operations Research, Springer, vol. 314(1), pages 213-253, July.
- Axel Dreves, 2018. "How to Select a Solution in Generalized Nash Equilibrium Problems," Journal of Optimization Theory and Applications, Springer, vol. 178(3), pages 973-997, September.
- Hiva Ghanbari & Katya Scheinberg, 2018. "Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates," Computational Optimization and Applications, Springer, vol. 69(3), pages 597-627, April.
- Radu Boţ & Christopher Hendrich, 2013. "A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems," Computational Optimization and Applications, Springer, vol. 54(2), pages 239-262, March.
- Pi, J. & Wang, Honggang & Pardalos, Panos M., 2021. "A dual reformulation and solution framework for regularized convex clustering problems," European Journal of Operational Research, Elsevier, vol. 290(3), pages 844-856.
- Boriss Siliverstovs, 2017.
"Short-term forecasting with mixed-frequency data: a MIDASSO approach,"
Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
- Boriss Siliverstovs, 2015. "Short-term forecasting with mixed-frequency data: A MIDASSO approach," KOF Working papers 15-375, KOF Swiss Economic Institute, ETH Zurich.
- Gaudioso, Manlio & Giallombardo, Giovanni & Mukhametzhanov, Marat, 2018. "Numerical infinitesimals in a variable metric method for convex nonsmooth optimization," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 312-320.
- Chen, Le-Yu & Lee, Sokbae, 2023.
"Sparse quantile regression,"
Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
- Le-Yu Chen & Sokbae (Simon) Lee, 2020. "Sparse Quantile Regression," CeMMAP working papers CWP30/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Le-Yu Chen & Sokbae Lee, 2020. "Sparse Quantile Regression," Papers 2006.11201, arXiv.org, revised Mar 2023.
- Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2015. "Factorisable sparse tail event curves," SFB 649 Discussion Papers 2015-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013.
"Pivotal estimation via square-root lasso in nonparametric regression,"
CeMMAP working papers
62/13, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers CWP62/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Gondzio, Jacek, 2012. "Interior point methods 25 years later," European Journal of Operational Research, Elsevier, vol. 218(3), pages 587-601.
- Chang, Kuo-Hao & Cuckler, Robert & Lee, Song-Lin & Lee, Loo Hay, 2022. "Discrete conditional-expectation-based simulation optimization: Methodology and applications," European Journal of Operational Research, Elsevier, vol. 298(1), pages 213-228.
- Daniel Dadush & László A. Végh & Giacomo Zambelli, 2020. "Rescaling Algorithms for Linear Conic Feasibility," Mathematics of Operations Research, INFORMS, vol. 45(2), pages 732-754, May.
- Yurii Nesterov, 2009. "Unconstrained Convex Minimization in Relative Scale," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 180-193, February.
- Jiaming Liang & Renato D. C. Monteiro & Chee-Khian Sim, 2021. "A FISTA-type accelerated gradient algorithm for solving smooth nonconvex composite optimization problems," Computational Optimization and Applications, Springer, vol. 79(3), pages 649-679, July.
- DEVOLDER, Olivier & GLINEUR, François & NESTEROV, Yurii, 2013. "First-order methods with inexact oracle: the strongly convex case," LIDAM Discussion Papers CORE 2013016, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Maurya, Ashwini, 2014. "A joint convex penalty for inverse covariance matrix estimation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 15-27.
- Franck Iutzeler & Jérôme Malick, 2018. "On the Proximal Gradient Algorithm with Alternated Inertia," Journal of Optimization Theory and Applications, Springer, vol. 176(3), pages 688-710, March.
- 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.
- Yakui Huang & Hongwei Liu, 2016. "Smoothing projected Barzilai–Borwein method for constrained non-Lipschitz optimization," Computational Optimization and Applications, Springer, vol. 65(3), pages 671-698, December.
- Masoud Ahookhosh & Arnold Neumaier, 2017. "An optimal subgradient algorithm for large-scale bound-constrained convex optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(1), pages 123-147, August.
- Xiaoqi Yang & Chenchen Zu, 2022. "Convergence of Inexact Quasisubgradient Methods with Extrapolation," Journal of Optimization Theory and Applications, Springer, vol. 193(1), pages 676-703, June.
- Zheng, Yuchen & Xie, Yujia & Lee, Ilbin & Dehghanian, Amin & Serban, Nicoleta, 2022. "Parallel subgradient algorithm with block dual decomposition for large-scale optimization," European Journal of Operational Research, Elsevier, vol. 299(1), pages 60-74.
- Cui, Ying & Leng, Chenlei & Sun, Defeng, 2016. "Sparse estimation of high-dimensional correlation matrices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 390-403.
- repec:hum:wpaper:sfb649dp2015-034 is not listed on IDEAS
- Amir Beck & Shoham Sabach, 2015. "Weiszfeld’s Method: Old and New Results," Journal of Optimization Theory and Applications, Springer, vol. 164(1), pages 1-40, January.
- Bot, Radu Ioan & Lorenz, Nicole, 2011. "Optimization problems in statistical learning: Duality and optimality conditions," European Journal of Operational Research, Elsevier, vol. 213(2), pages 395-404, September.
- Simon Setzer & Gabriele Steidl & Jan Morgenthaler, 2013. "A cyclic projected gradient method," Computational Optimization and Applications, Springer, vol. 54(2), pages 417-440, March.
- repec:hum:wpaper:sfb649dp2016-057 is not listed on IDEAS
- Donghwan Kim & Jeffrey A. Fessler, 2021. "Optimizing the Efficiency of First-Order Methods for Decreasing the Gradient of Smooth Convex Functions," Journal of Optimization Theory and Applications, Springer, vol. 188(1), pages 192-219, January.
- Nishanth Dikkala & Greg Lewis & Lester Mackey & Vasilis Syrgkanis, 2020. "Minimax Estimation of Conditional Moment Models," Papers 2006.07201, arXiv.org.
- Eric P Xing & Ross E Curtis & Georg Schoenherr & Seunghak Lee & Junming Yin & Kriti Puniyani & Wei Wu & Peter Kinnaird, 2014. "GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-19, June.
- Guido Consonni & Luca La Rocca & Stefano Peluso, 2017. "Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 741-764, September.
- Lingxue Zhang & Seyoung Kim, 2014. "Learning Gene Networks under SNP Perturbations Using eQTL Datasets," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-20, February.
- Ya-Feng Liu & Xin Liu & Shiqian Ma, 2019. "On the Nonergodic Convergence Rate of an Inexact Augmented Lagrangian Framework for Composite Convex Programming," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 632-650, May.
- Huynh Ngai & Ta Anh Son, 2022. "Generalized Nesterov’s accelerated proximal gradient algorithms with convergence rate of order o(1/k2)," Computational Optimization and Applications, Springer, vol. 83(2), pages 615-649, November.
- Qian Liu & Yuqing Xu & Yang Zhou, 2020. "A class of exact penalty functions and penalty algorithms for nonsmooth constrained optimization problems," Journal of Global Optimization, Springer, vol. 76(4), pages 745-768, April.
- Hanif Sherali & J. Smith, 2012. "Dynamic Lagrangian dual and reduced RLT constructs for solving 0–1 mixed-integer programs," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 173-189, April.
- Silvia Villa & Lorenzo Rosasco & Sofia Mosci & Alessandro Verri, 2014. "Proximal methods for the latent group lasso penalty," Computational Optimization and Applications, Springer, vol. 58(2), pages 381-407, June.
- Susan Schommer, 2013. "Computing equilibria in economies with incomplete markets, collateral and default penalties," Annals of Operations Research, Springer, vol. 206(1), pages 367-383, July.
- Hiroaki Kaido & Jiaxuan Li & Marc Rysman, 2018.
"Moment inequalities in the context of simulated and predicted variables,"
CeMMAP working papers
CWP26/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Hiroaki Kaido & Jiaxuan Li & Marc Rysman, 2018. "Moment Inequalities in the Context of Simulated and Predicted Variables," Papers 1804.03674, arXiv.org.
- Xiaolong Qin & Nguyen Thai An, 2019. "Smoothing algorithms for computing the projection onto a Minkowski sum of convex sets," Computational Optimization and Applications, Springer, vol. 74(3), pages 821-850, December.
- Nguyen Thai An & Nguyen Mau Nam & Xiaolong Qin, 2020. "Solving k-center problems involving sets based on optimization techniques," Journal of Global Optimization, Springer, vol. 76(1), pages 189-209, January.
- Kimon Fountoulakis & Jacek Gondzio, 2016. "Performance of first- and second-order methods for $$\ell _1$$ ℓ 1 -regularized least squares problems," Computational Optimization and Applications, Springer, vol. 65(3), pages 605-635, December.
- Jianqing Fan & Han Liu & Yang Ning & Hui Zou, 2017. "High dimensional semiparametric latent graphical model for mixed data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 405-421, March.
- 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).
- Adrien B. TAYLOR & Julien M. HENDRICKX & François GLINEUR, 2017. "Exact worst-case performance of first-order methods for composite convex optimization," LIDAM Reprints CORE 2875, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2020. "Essentials of numerical nonsmooth optimization," 4OR, Springer, vol. 18(1), pages 1-47, March.
- Jiarui Chu & Ludovic Tangpi, 2021. "Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics," Papers 2111.12248, arXiv.org, revised Feb 2023.
- Martin Meinel & Michael Ulbrich & Sebastian Albrecht, 2014. "A class of distributed optimization methods with event-triggered communication," Computational Optimization and Applications, Springer, vol. 57(3), pages 517-553, April.
- Arkadi Nemirovski & Shmuel Onn & Uriel G. Rothblum, 2010. "Accuracy Certificates for Computational Problems with Convex Structure," Mathematics of Operations Research, INFORMS, vol. 35(1), pages 52-78, February.
- Shipra Agrawal & Nikhil R. Devanur, 2019. "Bandits with Global Convex Constraints and Objective," Operations Research, INFORMS, vol. 67(5), pages 1486-1502, September.
- Masoud Ahookhosh & Arnold Neumaier, 2018. "Solving structured nonsmooth convex optimization with complexity $$\mathcal {O}(\varepsilon ^{-1/2})$$ O ( ε - 1 / 2 )," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 110-145, April.
- NESTEROV, Yu., 2012. "Subgradient methods for huge-scale optimization problems," LIDAM Discussion Papers CORE 2012002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- 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.
- Renato D. C. Monteiro & Camilo Ortiz & Benar F. Svaiter, 2016. "An adaptive accelerated first-order method for convex optimization," Computational Optimization and Applications, Springer, vol. 64(1), pages 31-73, May.
- Prater, Ashley & Shen, Lixin & Suter, Bruce W., 2015. "Finding Dantzig selectors with a proximity operator based fixed-point algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 36-46.
- Niao He & Anatoli Juditsky & Arkadi Nemirovski, 2015. "Mirror Prox algorithm for multi-term composite minimization and semi-separable problems," Computational Optimization and Applications, Springer, vol. 61(2), pages 275-319, June.
- Jueyou Li & Zhiyou Wu & Changzhi Wu & Qiang Long & Xiangyu Wang, 2016. "An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 153-171, January.
- Nguyen Thai An & Daniel Giles & Nguyen Mau Nam & R. Blake Rector, 2016. "The Log-Exponential Smoothing Technique and Nesterov’s Accelerated Gradient Method for Generalized Sylvester Problems," Journal of Optimization Theory and Applications, Springer, vol. 168(2), pages 559-583, February.
- Guoyin Li & Alfred Ma & Ting Pong, 2014. "Robust least square semidefinite programming with applications," Computational Optimization and Applications, Springer, vol. 58(2), pages 347-379, June.
- Masaru Ito, 2016. "New results on subgradient methods for strongly convex optimization problems with a unified analysis," Computational Optimization and Applications, Springer, vol. 65(1), pages 127-172, September.
- Quoc Tran-Dinh, 2017. "Adaptive smoothing algorithms for nonsmooth composite convex minimization," Computational Optimization and Applications, Springer, vol. 66(3), pages 425-451, April.
- Mikhail A. Bragin & Peter B. Luh & Joseph H. Yan & Nanpeng Yu & Gary A. Stern, 2015. "Convergence of the Surrogate Lagrangian Relaxation Method," Journal of Optimization Theory and Applications, Springer, vol. 164(1), pages 173-201, January.
- Masoud Ahookhosh, 2019. "Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(3), pages 319-353, June.
- Dirk Lorenz & Marc Pfetsch & Andreas Tillmann, 2014. "An infeasible-point subgradient method using adaptive approximate projections," Computational Optimization and Applications, Springer, vol. 57(2), pages 271-306, March.
- Jueyou Li & Guo Chen & Zhaoyang Dong & Zhiyou Wu, 2016. "A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints," Computational Optimization and Applications, Springer, vol. 64(3), pages 671-697, July.
- Donghwan Kim & Jeffrey A. Fessler, 2017. "On the Convergence Analysis of the Optimized Gradient Method," Journal of Optimization Theory and Applications, Springer, vol. 172(1), pages 187-205, January.
- C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
- 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.
- W. Geremew & N. M. Nam & A. Semenov & V. Boginski & E. Pasiliao, 2018. "A DC programming approach for solving multicast network design problems via the Nesterov smoothing technique," Journal of Global Optimization, Springer, vol. 72(4), pages 705-729, December.
- Samid Hoda & Andrew Gilpin & Javier Peña & Tuomas Sandholm, 2010. "Smoothing Techniques for Computing Nash Equilibria of Sequential Games," Mathematics of Operations Research, INFORMS, vol. 35(2), pages 494-512, May.
- Raphael Hauser & Sergey Shahverdyan, 2015. "A New Approach to Model Free Option Pricing," Papers 1501.03701, arXiv.org.
- Eduardo Conde, 2014. "A Minmax Regret Linear Regression Model Under Uncertainty in the Dependent Variable," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 573-596, February.
- Quoc Tran Dinh & Carlo Savorgnan & Moritz Diehl, 2013. "Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems," Computational Optimization and Applications, Springer, vol. 55(1), pages 75-111, May.
- 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).
- Yunmei Chen & Xiaojing Ye & Wei Zhang, 2020. "Acceleration techniques for level bundle methods in weakly smooth convex constrained optimization," Computational Optimization and Applications, Springer, vol. 77(2), pages 411-432, November.
- A. Chambolle & Ch. Dossal, 2015. "On the Convergence of the Iterates of the “Fast Iterative Shrinkage/Thresholding Algorithm”," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 968-982, September.
- Bo Wen & Xiaoping Xue, 2019. "On the convergence of the iterates of proximal gradient algorithm with extrapolation for convex nonsmooth minimization problems," Journal of Global Optimization, Springer, vol. 75(3), pages 767-787, November.
- Nima Rabiei & Jose Muñoz, 2015. "AAR-based decomposition algorithm for non-linear convex optimisation," Computational Optimization and Applications, Springer, vol. 62(3), pages 761-786, December.
- Qihang Lin & Xi Chen & Javier Peña, 2014. "A sparsity preserving stochastic gradient methods for sparse regression," Computational Optimization and Applications, Springer, vol. 58(2), pages 455-482, June.
- Boţ, Radu Ioan & Csetnek, Ernö Robert & Hendrich, Christopher, 2015. "Inertial Douglas–Rachford splitting for monotone inclusion problems," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 472-487.
- Necdet Aybat & Donald Goldfarb & Shiqian Ma, 2014. "Efficient algorithms for robust and stable principal component pursuit problems," Computational Optimization and Applications, Springer, vol. 58(1), pages 1-29, May.
- Frank E. Curtis & Arvind U. Raghunathan, 2017. "Solving nearly-separable quadratic optimization problems as nonsmooth equations," Computational Optimization and Applications, Springer, vol. 67(2), pages 317-360, June.
- Wenjie Huang & Xun Zhang, 2021. "Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization," SN Operations Research Forum, Springer, vol. 2(2), pages 1-28, June.
- Masaru Ito & Mituhiro Fukuda, 2021. "Nearly Optimal First-Order Methods for Convex Optimization under Gradient Norm Measure: an Adaptive Regularization Approach," Journal of Optimization Theory and Applications, Springer, vol. 188(3), pages 770-804, March.
- Li, Peili & Xiao, Yunhai, 2018. "An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 292-307.
- Yi Zhou & Yingbin Liang & Lixin Shen, 2019. "A simple convergence analysis of Bregman proximal gradient algorithm," Computational Optimization and Applications, Springer, vol. 73(3), pages 903-912, July.
- Chen, Xuerong & Li, Haoqi & Liang, Hua & Lin, Huazhen, 2019. "Functional response regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 218-233.
- Le Thi Khanh Hien & Cuong V. Nguyen & Huan Xu & Canyi Lu & Jiashi Feng, 2019. "Accelerated Randomized Mirror Descent Algorithms for Composite Non-strongly Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 181(2), pages 541-566, May.
- Nguyen Mau Nam & R. Blake Rector & Daniel Giles, 2017. "Minimizing Differences of Convex Functions with Applications to Facility Location and Clustering," Journal of Optimization Theory and Applications, Springer, vol. 173(1), pages 255-278, April.
- Silvia Bonettini & Valeria Ruggiero, 2014. "An alternating extragradient method with non euclidean projections for saddle point problems," Computational Optimization and Applications, Springer, vol. 59(3), pages 511-540, December.
- Garud Iyengar & Alfred Ma, 2013. "Fast gradient descent method for Mean-CVaR optimization," Annals of Operations Research, Springer, vol. 205(1), pages 203-212, May.
- Yuan Shen & Hongyong Wang, 2016. "New Augmented Lagrangian-Based Proximal Point Algorithm for Convex Optimization with Equality Constraints," Journal of Optimization Theory and Applications, Springer, vol. 171(1), pages 251-261, October.