Operations Research Transactions ›› 2025, Vol. 29 ›› Issue (4): 121-140.doi: 10.15960/j.cnki.issn.1007-6093.2025.04.011
• Research Article • Previous Articles Next Articles
Received:2022-11-16
Online:2025-12-15
Published:2025-12-11
Contact:
Honglin LUO
E-mail:luohonglin@cqnu.edu.cn
CLC Number:
Zilin TAN, Honglin LUO. A second-order splitting method with its application[J]. Operations Research Transactions, 2025, 29(4): 121-140.
"
| 1 | GlowinskiR,MarrocoA.Sur l'approximation, paréléments finis d'ordre un, et la résolution, par pénalisation-dualitéd'une classe de problèmes de Dirichlet non linéaires[J].Journal of Equine Veterinary Science,1975,2(R2):41-76. |
| 2 |
GabayD,MercierB.A dual algorithm for the solution of nonlinear variational problems via finite element approximation[J].Computers Mathematics with Applications,1976,2(1):17-40.
doi: 10.1016/0898-1221(76)90003-1 |
| 3 | Dhar S, Yi C, Ramakrishnan N, et al. ADMM based scalable machine learning on Spark[C]//IEEE International Conference on Big Data, 2015: 1174-1182. |
| 4 |
YangQ,ChenG,WangT.ADMM-based distributed algorithm for economic dispatch in power systems with both packet drops and communication delays[J].IEEE/CAA Journal of Automatica Sinica,2020,7(3):842-852.
doi: 10.1109/JAS.2020.1003156 |
| 5 |
SchizasD,RibeiroA,GiannakisgB.Consensus in ad hoc WSNs with noisy links-part Ⅰ: Distributed estimation of deterministic signals[J].IEEE Transactions on Signal Processing,2008,56(1):350-364.
doi: 10.1109/TSP.2007.906734 |
| 6 | Huebner N, Rink Y, Suriyah M, et al. Distributed AC-DC optimal power flow in the European transmission grid with ADMM[C]//55th International Universities Power Engineering Conference, 2020: 1-6. |
| 7 | Xu P, Roosta-Khorasani F, Mahoney M W. Second-order optimization for non-convex machine learning: An empirical study[C]//The 2020 SIAM International Conference on Data Mining, 2020: 199-207. |
| 8 |
KanK,FungS W,RuthottoL.PNKH-B: A projected newton–-Krylov method for large-scale bound-constrained optimization[J].SIAM Journal on Scientific Computing,2021,43(5):704-726.
doi: 10.1137/20M1341428 |
| 9 | WangF H,CaoW F,XuZ B.Convergence of multi-block Bregman ADMM for nonconvex composite problems[J].Science China (Information Sciences),2018,61(12):1-12. |
| 10 |
CurtisF E,RobinsonD P,RoyerC W,et al.Trust-region Newton-CG with strong second-order complexity guarantees for nonconvex optimization[J].SIAM Journal on Optimization,2021,31(1):518-544.
doi: 10.1137/19M130563X |
| 11 | GouldN I,RobinsonD P,ThorneH S.On solving trust-region and other regularised subproblems in optimization[J].Mathematical Programming,2010,2(1):21-57. |
| 12 |
HazanE,KorenT.A linear-time algorithm for trust region problems[J].Mathematical Programming,2016,158(1-2):363-381.
doi: 10.1007/s10107-015-0933-y |
| 13 |
NesterovY,PolyakB T.Cubic regularization of Newton method and its global performance[J].Mathematical Programming,2006,108(1):177-205.
doi: 10.1007/s10107-006-0706-8 |
| 14 |
CartisC,GouldN,TointP L.Adaptive cubic regularisation methods for unconstrained optimization, Part Ⅰ: Motivation, convergence and numerical results[J].Mathematical Programming,2011,127(2):245-295.
doi: 10.1007/s10107-009-0286-5 |
| 15 |
JiangB,LinT,MaS,et al.Structured nonconvex and nonsmooth optimization: Algorithms and iteration complexity analysis[J].Computational Optimization and Applications,2019,72,115-157.
doi: 10.1007/s10589-018-0034-y |
| 16 |
AravkinA Y,BaraldiR,OrbanD.A proximal quasi-Newton trust-region method for nonsmooth regularized optimization[J].SIAM Journal on Optimization,2022,32(2):900-929.
doi: 10.1137/21M1409536 |
| 17 | XuP,RoostaF,MahoneyM W.Newton-type methods for non-convex optimization under inexact Hessian information[J].Mathematical Programming,2020,25(3):78-92. |
| 18 |
ZhangY,ZhangN,SunD,et al.An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems[J].Mathematical Programming,2020,179,223-263.
doi: 10.1007/s10107-018-1329-6 |
| 19 | WangF,CaoW,XuZ.Convergence of multi-block Bregman ADMM for nonconvex composite problems[J].Science China Information Sciences,2018,61(12):122-148. |
| 20 |
BaiJ,HagerW W,ZhangH.An inexact accelerated stochastic ADMM for separable convex optimization[J].Computational Optimization and Applications,2022,81,479-518.
doi: 10.1007/s10589-021-00338-8 |
| 21 | BaiJ,ChangX,LiJ,et al.Convergence revisit on generalized symmetric ADMM[J].SIAM Journal on Optimization,2021,70,149-168. |
| 22 |
BaiJ,LiJ,XuF,et al.Generalized symmetric ADMM for separable convex optimization[J].Computational Optimization and Applications,2018,70,129-170.
doi: 10.1007/s10589-017-9971-0 |
| 23 | CartisC,GouldN,TointP L.Adaptive cubic regularisation methods for unconstrained optimization. Part Ⅱ: Worst-case function-and derivative-evaluation complexity[J].Mathematical Programming,2011,127(2):295-319. |
| 24 |
CartisC,GouldN,TointP L.Complexity bounds for second-order optimality in unconstrained optimization[J].Journal of Complexity,2012,28(1):93-108.
doi: 10.1016/j.jco.2011.06.001 |
| 25 |
MoréJ J,SorensenD C.Computing a trust region step[J].SIAM Journal on Scientific Computing,1983,4(3):553-572.
doi: 10.1137/0904038 |
| 26 |
SteihaugT.The conjugate gradient method and trust regions in large scale optimization[J].SIAM Journal on Numerical Analysis,1983,20(3):626-637.
doi: 10.1137/0720042 |
| 27 |
GoldsteinT,O'DonoghueB,SetzerS,et al.Fast alternating direction optimization methods[J].SIAM Journal on Imaging Sciences,2014,7(3):1588-1623.
doi: 10.1137/120896219 |
| 28 |
CartisC,GouldN,TointP L.On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming[J].SIAM Journal on Optimization,2011,21(4):1721-1739.
doi: 10.1137/11082381X |
| [1] | Maoran WANG, Xingju CAI, Zhongming WU, Deren HAN. First-order splitting algorithm for multi-model traffic equilibrium problems [J]. Operations Research Transactions, 2023, 27(2): 63-78. |
| [2] | LIU Pengjie, JIANG Xianzhen, SONG Dan. A class of spectral conjugate gradient method with sufficient descent property [J]. Operations Research Transactions, 2022, 26(4): 87-97. |
| [3] | Huiling ZHANG, Naoerzai SAI, Xiaoyun WU. Modified PRP conjugate gradient method for unconstrained optimization [J]. Operations Research Transactions, 2022, 26(2): 64-72. |
| [4] | Xiquan SHAN, Meixia LI, Jinyu LIU. Smoothing Newton method for the tensor stochastic complementarity problem [J]. Operations Research Transactions, 2022, 26(2): 128-136. |
| [5] | Liyuan CUI, Shouqiang DU. Projected Levenberg-Marquardt method for stochastic R0 tensor complementarity problems [J]. Operations Research Transactions, 2021, 25(4): 69-79. |
| [6] | XU Zi, ZHANG Huiling. Optimization algorithms and their complexity analysis for non-convex minimax problems [J]. Operations Research Transactions, 2021, 25(3): 74-86. |
| [7] |
LI Jianling, ZHANG Hui, YANG Zhenping, JIAN Jinbao.
A globally convergent SSDP algorithm without a penalty function or a filter for nonlinear semidefinite programming
[J]. Operations Research Transactions, 2018, 22(4): 1-16.
|
| [8] |
SHAO Shuting, DU Shouqiang.
Smoothing cautious DPRP conjugate gradient method for solving a kind of special nonsmooth equations with max-type function
[J]. Operations Research Transactions, 2018, 22(3): 69-78.
|
| [9] |
.
A sufficient descent conjugate gradient method for nonlinear unconstrained optimization problems
[J]. Operations Research Transactions, 2018, 22(3): 59-68.
|
| [10] | CHEN Yuanyuan, GAO Yan, LIU Zhimin, DU Shouqiang. The smoothing gradient method for a kind of special optimization problem [J]. Operations Research Transactions, 2017, 21(2): 119-125. |
| [11] | CHEN Zhongwen, ZHAO Qi, BIAN Kai. A successive linearization method with flexible penalty for nonlinear semidefinite programming [J]. Operations Research Transactions, 2017, 21(2): 84-100. |
| [12] | TIAN Zhaowei, ZHANG Liwei. A look at the convergence of the augmented Lagrange method for nondifferentiable convex programming from the view of a gradient method with constant stepsize [J]. Operations Research Transactions, 2017, 21(1): 111-117. |
| [13] | HANG Dan, YAN Shijian. Convergence of nonmonotonic Perry-Shanno's memoryless quasi-Newton method with parameters [J]. Operations Research Transactions, 2016, 20(4): 85-92. |
| [14] | MA Guodong, JIAN Jinbao. A feasible sequential systems of linear equations algorithm for inequality constrained optimization [J]. Operations Research Transactions, 2015, 19(4): 48-58. |
| [15] | WAN Rui, XU Zi. On the linear convergence of the general alternating proximal gradient method for convex minimization [J]. Operations Research Transactions, 2014, 18(3): 1-12. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
