A family of three-term conjugate gradient projection methods with a restart procedure and their relaxed-inertial extensions for the constrained nonlinear pseudo-monotone equations with applications
Al-Baali et al. ( Comput. Optim. Appl. 60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and global convergence. In this paper, we extend this method to a family of three-term conjugate gradient projection methods with a restart proc...
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| Vydané v: | Numerical algorithms Ročník 94; číslo 3; s. 1055 - 1083 |
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| Jazyk: | English |
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01.11.2023
Springer Nature B.V |
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| ISSN: | 1017-1398, 1572-9265 |
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| Abstract | Al-Baali et al. (
Comput. Optim. Appl.
60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and global convergence. In this paper, we extend this method to a family of three-term conjugate gradient projection methods with a restart procedure and their relaxed-inertial versions for constrained nonlinear pseudo-monotone equations. The accelerated gradient-descent method MSM and the relaxed-inertial strategy are incorporated into the proposed methods to obtain better computational performance. The global convergence of the extended methods are established theoretically without the Lipschitz continuity of the underlying mapping. Numerical results on constrained nonlinear equations show that the extended methods with different settings are efficient. The applicability and efficiency of the extended methods in the regularized decentralized logistic regression and sparse signal restoration problems are also presented and verified. |
|---|---|
| AbstractList | Al-Baali et al. (
Comput. Optim. Appl.
60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and global convergence. In this paper, we extend this method to a family of three-term conjugate gradient projection methods with a restart procedure and their relaxed-inertial versions for constrained nonlinear pseudo-monotone equations. The accelerated gradient-descent method MSM and the relaxed-inertial strategy are incorporated into the proposed methods to obtain better computational performance. The global convergence of the extended methods are established theoretically without the Lipschitz continuity of the underlying mapping. Numerical results on constrained nonlinear equations show that the extended methods with different settings are efficient. The applicability and efficiency of the extended methods in the regularized decentralized logistic regression and sparse signal restoration problems are also presented and verified. Al-Baali et al. (Comput. Optim. Appl. 60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and global convergence. In this paper, we extend this method to a family of three-term conjugate gradient projection methods with a restart procedure and their relaxed-inertial versions for constrained nonlinear pseudo-monotone equations. The accelerated gradient-descent method MSM and the relaxed-inertial strategy are incorporated into the proposed methods to obtain better computational performance. The global convergence of the extended methods are established theoretically without the Lipschitz continuity of the underlying mapping. Numerical results on constrained nonlinear equations show that the extended methods with different settings are efficient. The applicability and efficiency of the extended methods in the regularized decentralized logistic regression and sparse signal restoration problems are also presented and verified. |
| Author | Liu, Pengjie Wu, Xiaoyu Shao, Hu Zheng, Tianlei Yuan, Zihang |
| Author_xml | – sequence: 1 givenname: Pengjie surname: Liu fullname: Liu, Pengjie organization: School of Mathematics, China University of Mining and Technology, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University – sequence: 2 givenname: Hu surname: Shao fullname: Shao, Hu email: shaohu@cumt.edu.cn organization: School of Mathematics, China University of Mining and Technology – sequence: 3 givenname: Zihang surname: Yuan fullname: Yuan, Zihang organization: School of Mathematics, China University of Mining and Technology – sequence: 4 givenname: Xiaoyu surname: Wu fullname: Wu, Xiaoyu organization: School of Mathematics, China University of Mining and Technology – sequence: 5 givenname: Tianlei surname: Zheng fullname: Zheng, Tianlei organization: Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, School of Information and Control Engineering, China University of Mining and Technology |
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| Cites_doi | 10.1137/100813026 10.1007/s10589-019-00165-y 10.3934/jimo.2021125 10.1137/15100463X 10.1007/s12190-014-0774-5 10.1016/0041-5553(64)90137-5 10.1007/s11075-021-01157-y 10.1051/cocv/2022032 10.1016/j.jmaa.2013.04.017 10.1007/s002450010019 10.1007/s40314-022-02019-6 10.1007/s10898-019-00819-5 10.1007/s10589-014-9662-z 10.1109/ACCESS.2021.3091906 10.1016/j.apnum.2022.02.001 10.1080/00207160.2018.1494825 10.1016/j.na.2011.02.040 10.1109/JSTSP.2007.910281 10.1007/s11075-022-01356-1 10.1007/BF02591989 10.1145/78928.78930 10.1360/N012016-00134 10.3934/jimo.2021173 10.1016/j.cam.2022.115020 10.1007/s11075-018-0603-2 10.1016/j.apnum.2022.06.015 10.1080/10556788.2013.833199 10.1007/s11071-022-08013-1 10.1007/s11075-018-0541-z 10.1080/10556780310001610493 10.1007/s11075-009-9350-8 10.1080/10556788.2019.1653868 10.1145/1961189.1961199 10.1080/10556789508805619 10.1051/ro/2022213 10.1007/978-1-4757-6388-1_18 10.1051/ro/2019045 10.1007/s10898-022-01213-4 10.1007/s10092-015-0154-z 10.1007/s101070100263 10.1016/j.apm.2023.01.018 10.1016/j.apnum.2019.08.022 10.1016/j.ejor.2013.11.012 10.1007/s11075-022-01483-9 10.1007/s11075-020-01043-z 10.3934/jimo.2013.9.117 10.1007/s10092-018-0291-2 |
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| Keywords | Logistic regression Global convergence 65K05 90C30 90C56 Nonlinear pseudo-monotone equations Relaxed-inertial strategy Conjugate gradient projection method Compressed sensing |
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| References | StanimirovićPSMiladinovićMBAccelerated gradient descent methods with line searchNumer. Algorithms20105450352026706661198.65104 AbubakarABKumanPA descent Dai-Liao conjugate gradient method for nonlinear equationsNumer. Algorithms201981119721039436301412.65042 GaoXCaiXJHanDRA Gauss-Seidel type inertial proximal alternating linearized minimization for a class of nonconvex optimization problemsJ. Glob. Optim.202076486388740783821476.90256 CruzWLRaydanMNonmonotone spectral methods for large-scale nonlinear systemsOptim. Methods Softw.200318558359920153991069.65056 IbrahimAHKumamPRapajićSPappZAbubakarABApproximation methods with inertial term for large-scale nonlinear monotone equationsAppl. Numer. Math.202218141743544513861502.65026 FigueiredoMATNowakRDWrightSJGradient projection for sparse reconstruction, application to compressed sensing and other inverse problemsIEEE J. Sel. Top. Sign. Proces.200714586597 OuYGXuWJA unified derivative-free projection method model for large-scale nonlinear equations with convex constraintsJ. Ind. Manag. Optim.20221853539356044704951513.90131 LiuPJWuXYShaoHZhangYCaoSHThree adaptive hybrid derivative-free projection methods for constrained monotone nonlinear equations and their applicationsNumer. Linear Algebra Appl.2023302456021707675620 Al-BaaliMNarushimaYYabeHA family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimizationComput. Optim. Appl.2015608911032978901315.90051 YuanGLLiTTHuWJA conjugate gradient algorithm for large-scale nonlinear equations and image restoration problemsAppl. Numer. Math.202014712914140153051433.90165 YuanGLWangBPShengZThe Hager-Zhang conjugate gradient algorithm for large-scale nonlinear equationsInter. J. Comput. Math.20199681533154739474101499.90233 FacchineiFPangJSFinite-dimensional Variational Inequalities and Complementarity Problems2003BerlinVol-I. Springer1062.90001 PappZRapajićSFR type methods for systems of large-scale nonlinear monotone equationsAppl. Math. Comput.201526981682333968241410.65196 LiuJKFengYMA derivative-free iterative method for nonlinear monotone equations with convex constraintsNumer. Algorithms20198224526239960161431.65073 IbrahimAHKumamPSunMChaipunyaPProjection method with inertial step for nonlinear equations: application to signal recoveryJ. Ind. Manag. Optim.2023191305544896801513.65073 SunMLiuJNew hybrid conjugate gradient projection method for the convex constrained equationsCalcolo20185339941135411611357.65078 SolodovMVSvaiterBFFukushimaMQiLA globally convergent inexact Newton method for systems of monotone equationsReformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods1998DordrechtKluwer355369 LiuJKSunYZhaoYXA derivative-free projection algorithm for solving pseudo-monotone equations with convex constraints (in Chinese)Math. Numer. Sin.202143338840044844491513.65150 YinJHJianJBJiangXZLiuMXWangLZA hybrid three-term conjugate gradient projection method for constrained nonlinear monotone equations with applicationsNumer. Algorithms20218838941842979241484.65131 AminifardZBabaie-KafakiSDai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processingNumer. Algorithms2022891369138743766901484.65132 Ma, G.D., Jin, J.C., Jian, J.B., Yin, J.H., Han, D.L.: A modified inertial three-term conjugate gradient projection method for constrained nonlinear equations with applications in compressed sensing. Numer. Algorithms 2023, 92(3), 1621-1653 (2023) YuGHNiuSZMaJHMultivariate spectral gradient projection method for nonlinear monotone equations with convex constraintsJ. Ind. Manag. Optim.20139111712930030191264.49037 Babaie-KafakiSA survey on the Dai-Liao family of nonlinear conjugate gradient methodsRAIRO-Oper. Res.2023571435845327871514.90251 SunMLiuJThree derivative-free projection methods for nonlinear equations with convex constraintsJ. Appl. Math. Comput.201547126527633040951348.90630 YinJHJianJBJiangXZA generalized hybrid CGPM-based algorithm for solving large-scale convex constrained equations with applications to image restorationJ. Comput. Appl. Math.202139142097311464.65069 PangJSInexact Newton methods for the nonlinear complementary problemMath. Program.198636154718656060613.90097 ChenCHChanRHMaSQYangJFInertial proximal ADMM for linearly constrained separable convex optimizationSIAM J. Imaging Sci.2015842239226734046821328.65134 ZhouWJLiDHLimited memory BFGS method for nonlinear monotone equationsJ. Comput. Math.20072589962292430 IvanovBMilovanovićGVStanimirovićPSAccelerated Dai-Liao projection method for solving systems of monotone nonlinear equations with application to image deblurringJ. Glob. Optim.20238537742045437861515.65153 JiangXZZhuYHJianJBTwo efficient nonlinear conjugate gradient methods with restart procedures and their applications in image restorationNonlinear Dyn.202311154695498 IbrahimAHKumamPAbubakarABAdamuAAccelerated derivative-free method for nonlinear monotone equations with an applicationNumer. Linear Algebra Appl.2022293446255507511605 DouMYLiHYLiuXWAn inertial proximal Peaceman-Rachford splitting method (in Chinese)Sci. Sin. Math.20174723333481499.90157 JianJBYinJHTangCMHanDLA family of inertial derivative-free projection methods for constrained nonlinear pseudo-monotone equations with applicationsComput. Appl. Math.20224130944813151513.65149 WangSGuanHBA scaled conjugate gradient method for solving monotone nonlinear equations with convex constraintsJ. Appl. Math.2013201331389341397.90365 AbubakarABKumamPA descent Dai-Liao conjugate gradient method for nonlinear equationsNumer. Algorithms20198119721039436301412.65042 DaiYHKouCXA nonlinear conjugate gradient algorithm with an optimal property and an improved Wolfe line searchSIAM J. Optim.201323129632030331091266.49065 HoyerPONon-negative matrix factorization with sparseness constraintsJ. Mach. Learn. Res.200451457146922480241222.68218 GaoPTHeCJAn efficient three-term conjugate gradient method for nonlinear monotone equations with convex constraintsCalcolo2018555338788021405.65043 Babaie-KafakiSGhanbariRThe Dai-Liao nonlinear conjugate gradient method with optimal parameter choicesEur. J. Oper. Res.2014234362563031513231304.90216 XiaoYHWangQYHuQJNon-smooth equations based method for l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_1$$\end{document}-norm problems with applications to compressed sensingNonlinear Anal. Theor.20117411357035771217.65069 Babaie-KafakiSGambariRA descent family of Dai-Liao conjugate gradient methodsOptim. Meth. Softw.201429358359131755041285.90063 ChangCCLinCJLIBSVM: a library for support vector machinesACM Trans. Intell. Syst. Technol.201123127 ShaoHGuoHWuXYLiuPJTwo families of self-adjusting spectral hybrid DL conjugate gradient methods and applications in image denoisingAppl. Math. Model.202311839341145451271510.90215 IvanovBStanimirovićPSMilovanovićGVDjordjevićSBrajevićIAccelerated multiple step-size methods for solving unconstrained optimization problemsOptim. Methods Softw.202136998102944329291489.65085 WuXYShaoHLiuPJZhangYZhuoYAn efficient conjugate gradient-based algorithm for unconstrained optimization and its projection extension to large-scale constrained nonlinear equations with application in signal recovery and image denoising problemsJ. Comput. Appl. Math.2023422450546507630774 DolanEDMoréJJBenchmarking optimization software with performance profilesMath. Program.200291220121318755151049.90004 LiuJKLuZLXuJLWuSTuZWAn efficient projection-based algorithm without Lipschitz continuity for large-scale nonlinear pseudo-monotone equationsJ. Comput. Appl. Math.202240343216261484.65125 AlvesMMEcksteinJGeremiaMMeloJGRelative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithmsComput. Optim. Appl.202075238942240645951432.90107 PolyakBTSome methods of speeding up the convergence of iteration methodsUSSR Comput. Math. Math. Phys.196445117 XiaoYHZhuHA conjugate gradient method to sovle convex constrained monotone equations with applications in compressive sensingJ. Math. Anal. Appl.2013405131031930535101316.90050 OuYGLiLA unified convergence analysis of the derivative-free projection-based method for constrained nonlinear monotone equationsNumer. Algorithms202210.1007/s11075-022-01483-907720460 AbubakarABKumamPIbrahimAHInertial derivative-free projection method for nonlinear monotone operator equations with convex constraintsIEEE Access202199215792167 JiangXZYangHHYinJHLiaoWA three-term conjugate gradient algorithm with restart procedure to solve image restoration problemsJ. Comput. Appl. Math.2023424452653807697429 MeintjesKMorganAPChemical equilibrium systems as numerical test problemsACM Trans. Math. Software19901621431510900.65153 Luo, H.: Accelerated primal-dual methods for linearly constrained convex optimization problems. arXiv: 2109.12604 (2022) Babaie-KafakiSAminifardZImproving the Dai-Liao parameter choices using a fixed point equationJ. Math. Model.2022101112043768551499.90215 YinJHJianJBJiangXZWuXDA family of inertial-relaxed DFPM-based algorithms for solving large-scale monotone nonlinear equations with application to sparse signal restorationJ. Comput. Appl. Math.202341944748341505.65199 DaiYHLiaoLZNew conjugacy conditions and related nonlinear conjugate gradient methodsAppl. Math. Optim.2001438710118043960973.65050 WangXQShaoHLiuPJWuTAn inertial proximal partially symmetric ADMM-based algorithm for linearly constrained multi-block nonconvex optimization problems with applicationsJ. Comput. Appl. Math.202342044833361504.90103 YinJHJianJBJiangXZA spectral gradient projection algorithm for convex constrained nonsmooth equations based on an adaptive line search (in Chinese)Math. Numer. Sin.202042445747144693521474.90454 DirkseSPFerrisMCMCPLIB: A collection of nonlinear mixed complementarity problemsOptim. Methods Softw.199554319345 Po 1527_CR55 YG Ou (1527_CR25) 2022; 18 SP Dirkse (1527_CR3) 1995; 5 MAT Figueiredo (1527_CR62) 2007; 1 1527_CR59 JS Pang (1527_CR63) 1986; 36 B Ivanov (1527_CR19) 2021; 36 YH Dai (1527_CR9) 2013; 23 ED Dolan (1527_CR56) 2002; 91 M Sun (1527_CR28) 2018; 53 MM Alves (1527_CR54) 2020; 75 XQ Wang (1527_CR38) 2023; 420 YH Xiao (1527_CR4) 2013; 405 1527_CR43 WL Cruz (1527_CR58) 2003; 18 JH Yin (1527_CR5) 2021; 88 S Babaie-Kafaki (1527_CR16) 2023; 57 M Sun (1527_CR29) 2015; 47 JH Yin (1527_CR53) 2020; 42 YH Xiao (1527_CR61) 2011; 74 CH Chen (1527_CR39) 2015; 8 Z Aminifard (1527_CR13) 2022; 89 GL Yuan (1527_CR22) 2020; 147 MV Solodov (1527_CR17) 1998 GH Yu (1527_CR31) 2013; 9 PJ Liu (1527_CR7) 2022; 175 XY Wu (1527_CR27) 2023; 422 K Meintjes (1527_CR2) 1990; 16 F Facchinei (1527_CR1) 2003 JK Liu (1527_CR21) 2022; 403 JB Jian (1527_CR47) 2022; 41 XZ Jiang (1527_CR51) 2023; 111 JK Liu (1527_CR34) 2019; 82 PJ Liu (1527_CR26) 2023; 30 PT Gao (1527_CR33) 2018; 55 S Babaie-Kafaki (1527_CR15) 2014; 29 AB Abubakar (1527_CR32) 2019; 81 YG Ou (1527_CR24) 2022 CC Chang (1527_CR60) 2011; 2 S Babaie-Kafaki (1527_CR14) 2022; 10 XZ Jiang (1527_CR52) 2023; 424 MY Dou (1527_CR40) 2017; 47 BT Polyak (1527_CR37) 1964; 4 Z Aminifard (1527_CR12) 2020; 54 JH Yin (1527_CR48) 2023; 419 Z Papp (1527_CR36) 2015; 269 H Shao (1527_CR10) 2023; 118 YH Dai (1527_CR8) 2001; 43 B Ivanov (1527_CR20) 2023; 85 GL Yuan (1527_CR23) 2019; 96 X Gao (1527_CR41) 2020; 76 JH Yin (1527_CR6) 2021; 391 S Wang (1527_CR30) 2013; 2013 M Al-Baali (1527_CR49) 2015; 60 AH Ibrahim (1527_CR46) 2022; 181 AB Abubakar (1527_CR18) 2019; 81 AH Ibrahim (1527_CR45) 2023; 19 JK Liu (1527_CR35) 2021; 43 S Babaie-Kafaki (1527_CR11) 2014; 234 PO Hoyer (1527_CR64) 2004; 5 AH Ibrahim (1527_CR44) 2022; 29 AB Abubakar (1527_CR42) 2021; 9 WJ Zhou (1527_CR57) 2007; 25 PS Stanimirović (1527_CR50) 2010; 54 |
| References_xml | – reference: YinJHJianJBJiangXZA generalized hybrid CGPM-based algorithm for solving large-scale convex constrained equations with applications to image restorationJ. Comput. Appl. Math.202139142097311464.65069 – reference: YuanGLWangBPShengZThe Hager-Zhang conjugate gradient algorithm for large-scale nonlinear equationsInter. J. Comput. Math.20199681533154739474101499.90233 – reference: WangSGuanHBA scaled conjugate gradient method for solving monotone nonlinear equations with convex constraintsJ. Appl. Math.2013201331389341397.90365 – reference: PappZRapajićSFR type methods for systems of large-scale nonlinear monotone equationsAppl. Math. Comput.201526981682333968241410.65196 – reference: Ma, G.D., Jin, J.C., Jian, J.B., Yin, J.H., Han, D.L.: A modified inertial three-term conjugate gradient projection method for constrained nonlinear equations with applications in compressed sensing. Numer. Algorithms 2023, 92(3), 1621-1653 (2023) – reference: Babaie-KafakiSGhanbariRThe Dai-Liao nonlinear conjugate gradient method with optimal parameter choicesEur. J. Oper. Res.2014234362563031513231304.90216 – reference: JiangXZZhuYHJianJBTwo efficient nonlinear conjugate gradient methods with restart procedures and their applications in image restorationNonlinear Dyn.202311154695498 – reference: GaoPTHeCJAn efficient three-term conjugate gradient method for nonlinear monotone equations with convex constraintsCalcolo2018555338788021405.65043 – reference: IbrahimAHKumamPAbubakarABAdamuAAccelerated derivative-free method for nonlinear monotone equations with an applicationNumer. Linear Algebra Appl.2022293446255507511605 – reference: Babaie-KafakiSA survey on the Dai-Liao family of nonlinear conjugate gradient methodsRAIRO-Oper. Res.2023571435845327871514.90251 – reference: SolodovMVSvaiterBFFukushimaMQiLA globally convergent inexact Newton method for systems of monotone equationsReformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods1998DordrechtKluwer355369 – reference: DirkseSPFerrisMCMCPLIB: A collection of nonlinear mixed complementarity problemsOptim. Methods Softw.199554319345 – reference: LiuPJWuXYShaoHZhangYCaoSHThree adaptive hybrid derivative-free projection methods for constrained monotone nonlinear equations and their applicationsNumer. Linear Algebra Appl.2023302456021707675620 – reference: ShaoHGuoHWuXYLiuPJTwo families of self-adjusting spectral hybrid DL conjugate gradient methods and applications in image denoisingAppl. Math. Model.202311839341145451271510.90215 – reference: Babaie-KafakiSAminifardZImproving the Dai-Liao parameter choices using a fixed point equationJ. Math. Model.2022101112043768551499.90215 – reference: MeintjesKMorganAPChemical equilibrium systems as numerical test problemsACM Trans. Math. Software19901621431510900.65153 – reference: DolanEDMoréJJBenchmarking optimization software with performance profilesMath. Program.200291220121318755151049.90004 – reference: FigueiredoMATNowakRDWrightSJGradient projection for sparse reconstruction, application to compressed sensing and other inverse problemsIEEE J. Sel. Top. Sign. Proces.200714586597 – reference: WangXQShaoHLiuPJWuTAn inertial proximal partially symmetric ADMM-based algorithm for linearly constrained multi-block nonconvex optimization problems with applicationsJ. Comput. Appl. Math.202342044833361504.90103 – reference: PangJSInexact Newton methods for the nonlinear complementary problemMath. Program.198636154718656060613.90097 – reference: AminifardZBabaie-KafakiSA restart scheme for the Dai-Liao conjugate gradient method by ignoring a direction of maximum magnification by the search direction matrixRAIRO-Oper. Res.202054498199140918561443.90330 – reference: AbubakarABKumanPA descent Dai-Liao conjugate gradient method for nonlinear equationsNumer. Algorithms201981119721039436301412.65042 – reference: IbrahimAHKumamPRapajićSPappZAbubakarABApproximation methods with inertial term for large-scale nonlinear monotone equationsAppl. Numer. Math.202218141743544513861502.65026 – reference: WuXYShaoHLiuPJZhangYZhuoYAn efficient conjugate gradient-based algorithm for unconstrained optimization and its projection extension to large-scale constrained nonlinear equations with application in signal recovery and image denoising problemsJ. Comput. Appl. Math.2023422450546507630774 – reference: ZhouWJLiDHLimited memory BFGS method for nonlinear monotone equationsJ. Comput. Math.20072589962292430 – reference: Luo, H.: Accelerated primal-dual methods for linearly constrained convex optimization problems. arXiv: 2109.12604 (2022) – reference: DouMYLiHYLiuXWAn inertial proximal Peaceman-Rachford splitting method (in Chinese)Sci. Sin. Math.20174723333481499.90157 – reference: SunMLiuJThree derivative-free projection methods for nonlinear equations with convex constraintsJ. Appl. Math. Comput.201547126527633040951348.90630 – reference: YinJHJianJBJiangXZWuXDA family of inertial-relaxed DFPM-based algorithms for solving large-scale monotone nonlinear equations with application to sparse signal restorationJ. Comput. Appl. Math.202341944748341505.65199 – reference: HoyerPONon-negative matrix factorization with sparseness constraintsJ. Mach. Learn. Res.200451457146922480241222.68218 – reference: IvanovBStanimirovićPSMilovanovićGVDjordjevićSBrajevićIAccelerated multiple step-size methods for solving unconstrained optimization problemsOptim. Methods Softw.202136998102944329291489.65085 – reference: XiaoYHWangQYHuQJNon-smooth equations based method for l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_1$$\end{document}-norm problems with applications to compressed sensingNonlinear Anal. Theor.20117411357035771217.65069 – reference: SunMLiuJNew hybrid conjugate gradient projection method for the convex constrained equationsCalcolo20185339941135411611357.65078 – reference: PolyakBTSome methods of speeding up the convergence of iteration methodsUSSR Comput. Math. Math. Phys.196445117 – reference: IvanovBMilovanovićGVStanimirovićPSAccelerated Dai-Liao projection method for solving systems of monotone nonlinear equations with application to image deblurringJ. Glob. Optim.20238537742045437861515.65153 – reference: CruzWLRaydanMNonmonotone spectral methods for large-scale nonlinear systemsOptim. Methods Softw.200318558359920153991069.65056 – reference: YuGHNiuSZMaJHMultivariate spectral gradient projection method for nonlinear monotone equations with convex constraintsJ. Ind. Manag. Optim.20139111712930030191264.49037 – reference: IbrahimAHKumamPSunMChaipunyaPProjection method with inertial step for nonlinear equations: application to signal recoveryJ. Ind. Manag. Optim.2023191305544896801513.65073 – reference: LiuPJShaoHWangYWuXYA three-term CGPM-based algorithm without Lipschitz continuity for constrained nonlinear monotone equations with applicationsAppl. Numer. Math.20221759810743797641484.65109 – reference: GaoXCaiXJHanDRA Gauss-Seidel type inertial proximal alternating linearized minimization for a class of nonconvex optimization problemsJ. Glob. Optim.202076486388740783821476.90256 – reference: Polyak, B.T.: Introduction to Optimization, Optimization Software, pp. 49. Inc. Publications Division, New York (1987) – reference: YuanGLLiTTHuWJA conjugate gradient algorithm for large-scale nonlinear equations and image restoration problemsAppl. Numer. Math.202014712914140153051433.90165 – reference: LiuJKLuZLXuJLWuSTuZWAn efficient projection-based algorithm without Lipschitz continuity for large-scale nonlinear pseudo-monotone equationsJ. Comput. Appl. Math.202240343216261484.65125 – reference: XiaoYHZhuHA conjugate gradient method to sovle convex constrained monotone equations with applications in compressive sensingJ. Math. Anal. Appl.2013405131031930535101316.90050 – reference: LiuJKSunYZhaoYXA derivative-free projection algorithm for solving pseudo-monotone equations with convex constraints (in Chinese)Math. Numer. Sin.202143338840044844491513.65150 – reference: AbubakarABKumamPIbrahimAHInertial derivative-free projection method for nonlinear monotone operator equations with convex constraintsIEEE Access202199215792167 – reference: OuYGLiLA unified convergence analysis of the derivative-free projection-based method for constrained nonlinear monotone equationsNumer. Algorithms202210.1007/s11075-022-01483-907720460 – reference: YinJHJianJBJiangXZA spectral gradient projection algorithm for convex constrained nonsmooth equations based on an adaptive line search (in Chinese)Math. Numer. Sin.202042445747144693521474.90454 – reference: JianJBYinJHTangCMHanDLA family of inertial derivative-free projection methods for constrained nonlinear pseudo-monotone equations with applicationsComput. Appl. Math.20224130944813151513.65149 – reference: DaiYHLiaoLZNew conjugacy conditions and related nonlinear conjugate gradient methodsAppl. Math. Optim.2001438710118043960973.65050 – reference: ChangCCLinCJLIBSVM: a library for support vector machinesACM Trans. Intell. Syst. Technol.201123127 – reference: DaiYHKouCXA nonlinear conjugate gradient algorithm with an optimal property and an improved Wolfe line searchSIAM J. Optim.201323129632030331091266.49065 – reference: Babaie-KafakiSGambariRA descent family of Dai-Liao conjugate gradient methodsOptim. Meth. Softw.201429358359131755041285.90063 – reference: StanimirovićPSMiladinovićMBAccelerated gradient descent methods with line searchNumer. Algorithms20105450352026706661198.65104 – reference: AbubakarABKumamPA descent Dai-Liao conjugate gradient method for nonlinear equationsNumer. Algorithms20198119721039436301412.65042 – reference: AlvesMMEcksteinJGeremiaMMeloJGRelative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithmsComput. Optim. Appl.202075238942240645951432.90107 – reference: LiuJKFengYMA derivative-free iterative method for nonlinear monotone equations with convex constraintsNumer. Algorithms20198224526239960161431.65073 – reference: ChenCHChanRHMaSQYangJFInertial proximal ADMM for linearly constrained separable convex optimizationSIAM J. Imaging Sci.2015842239226734046821328.65134 – reference: FacchineiFPangJSFinite-dimensional Variational Inequalities and Complementarity Problems2003BerlinVol-I. Springer1062.90001 – reference: JiangXZYangHHYinJHLiaoWA three-term conjugate gradient algorithm with restart procedure to solve image restoration problemsJ. Comput. Appl. Math.2023424452653807697429 – reference: YinJHJianJBJiangXZLiuMXWangLZA hybrid three-term conjugate gradient projection method for constrained nonlinear monotone equations with applicationsNumer. Algorithms20218838941842979241484.65131 – reference: Al-BaaliMNarushimaYYabeHA family of three-term conjugate gradient methods with sufficient descent property for unconstrained optimizationComput. Optim. Appl.2015608911032978901315.90051 – reference: OuYGXuWJA unified derivative-free projection method model for large-scale nonlinear equations with convex constraintsJ. Ind. Manag. Optim.20221853539356044704951513.90131 – reference: AminifardZBabaie-KafakiSDai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processingNumer. Algorithms2022891369138743766901484.65132 – volume: 23 start-page: 296 issue: 1 year: 2013 ident: 1527_CR9 publication-title: SIAM J. Optim. doi: 10.1137/100813026 – volume: 422 year: 2023 ident: 1527_CR27 publication-title: J. Comput. Appl. Math. – volume: 75 start-page: 389 issue: 2 year: 2020 ident: 1527_CR54 publication-title: Comput. Optim. Appl. doi: 10.1007/s10589-019-00165-y – volume: 18 start-page: 3539 issue: 5 year: 2022 ident: 1527_CR25 publication-title: J. Ind. Manag. Optim. doi: 10.3934/jimo.2021125 – volume: 8 start-page: 2239 issue: 4 year: 2015 ident: 1527_CR39 publication-title: SIAM J. Imaging Sci. doi: 10.1137/15100463X – volume: 47 start-page: 265 issue: 1 year: 2015 ident: 1527_CR29 publication-title: J. Appl. Math. Comput. doi: 10.1007/s12190-014-0774-5 – volume: 4 start-page: 1 issue: 5 year: 1964 ident: 1527_CR37 publication-title: USSR Comput. Math. Math. Phys. doi: 10.1016/0041-5553(64)90137-5 – volume: 89 start-page: 1369 year: 2022 ident: 1527_CR13 publication-title: Numer. Algorithms doi: 10.1007/s11075-021-01157-y – ident: 1527_CR59 doi: 10.1051/cocv/2022032 – volume: 405 start-page: 310 issue: 1 year: 2013 ident: 1527_CR4 publication-title: J. Math. Anal. Appl. doi: 10.1016/j.jmaa.2013.04.017 – volume: 43 start-page: 87 year: 2001 ident: 1527_CR8 publication-title: Appl. Math. Optim. doi: 10.1007/s002450010019 – volume: 41 start-page: 309 year: 2022 ident: 1527_CR47 publication-title: Comput. Appl. Math. doi: 10.1007/s40314-022-02019-6 – volume: 76 start-page: 863 issue: 4 year: 2020 ident: 1527_CR41 publication-title: J. Glob. Optim. doi: 10.1007/s10898-019-00819-5 – volume: 60 start-page: 89 year: 2015 ident: 1527_CR49 publication-title: Comput. Optim. Appl. doi: 10.1007/s10589-014-9662-z – volume: 9 start-page: 92157 year: 2021 ident: 1527_CR42 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3091906 – volume: 10 start-page: 11 issue: 1 year: 2022 ident: 1527_CR14 publication-title: J. Math. Model. – volume: 175 start-page: 98 year: 2022 ident: 1527_CR7 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2022.02.001 – volume: 269 start-page: 816 year: 2015 ident: 1527_CR36 publication-title: Appl. Math. Comput. – volume: 25 start-page: 89 year: 2007 ident: 1527_CR57 publication-title: J. Comput. Math. – volume: 96 start-page: 1533 issue: 8 year: 2019 ident: 1527_CR23 publication-title: Inter. J. Comput. Math. doi: 10.1080/00207160.2018.1494825 – volume: 74 start-page: 3570 issue: 11 year: 2011 ident: 1527_CR61 publication-title: Nonlinear Anal. Theor. doi: 10.1016/j.na.2011.02.040 – volume-title: Finite-dimensional Variational Inequalities and Complementarity Problems year: 2003 ident: 1527_CR1 – volume: 1 start-page: 586 issue: 4 year: 2007 ident: 1527_CR62 publication-title: IEEE J. Sel. Top. Sign. Proces. doi: 10.1109/JSTSP.2007.910281 – ident: 1527_CR43 doi: 10.1007/s11075-022-01356-1 – volume: 36 start-page: 54 issue: 1 year: 1986 ident: 1527_CR63 publication-title: Math. Program. doi: 10.1007/BF02591989 – volume: 403 year: 2022 ident: 1527_CR21 publication-title: J. Comput. Appl. Math. – volume: 16 start-page: 143 issue: 2 year: 1990 ident: 1527_CR2 publication-title: ACM Trans. Math. Software doi: 10.1145/78928.78930 – volume: 420 year: 2023 ident: 1527_CR38 publication-title: J. Comput. Appl. Math. – volume: 47 start-page: 333 issue: 2 year: 2017 ident: 1527_CR40 publication-title: Sci. Sin. Math. doi: 10.1360/N012016-00134 – volume: 29 issue: 3 year: 2022 ident: 1527_CR44 publication-title: Numer. Linear Algebra Appl. – volume: 19 start-page: 30 issue: 1 year: 2023 ident: 1527_CR45 publication-title: J. Ind. Manag. Optim. doi: 10.3934/jimo.2021173 – volume: 424 year: 2023 ident: 1527_CR52 publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2022.115020 – volume: 82 start-page: 245 year: 2019 ident: 1527_CR34 publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0603-2 – volume: 181 start-page: 417 year: 2022 ident: 1527_CR46 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2022.06.015 – volume: 29 start-page: 583 issue: 3 year: 2014 ident: 1527_CR15 publication-title: Optim. Meth. Softw. doi: 10.1080/10556788.2013.833199 – volume: 111 start-page: 5469 year: 2023 ident: 1527_CR51 publication-title: Nonlinear Dyn. doi: 10.1007/s11071-022-08013-1 – volume: 81 start-page: 197 year: 2019 ident: 1527_CR18 publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0541-z – volume: 18 start-page: 583 issue: 5 year: 2003 ident: 1527_CR58 publication-title: Optim. Methods Softw. doi: 10.1080/10556780310001610493 – volume: 5 start-page: 1457 year: 2004 ident: 1527_CR64 publication-title: J. Mach. Learn. Res. – volume: 54 start-page: 503 year: 2010 ident: 1527_CR50 publication-title: Numer. Algorithms doi: 10.1007/s11075-009-9350-8 – volume: 36 start-page: 998 year: 2021 ident: 1527_CR19 publication-title: Optim. Methods Softw. doi: 10.1080/10556788.2019.1653868 – volume: 419 year: 2023 ident: 1527_CR48 publication-title: J. Comput. Appl. Math. – volume: 81 start-page: 197 issue: 1 year: 2019 ident: 1527_CR32 publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0541-z – ident: 1527_CR55 – volume: 2 start-page: 1 issue: 3 year: 2011 ident: 1527_CR60 publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – volume: 5 start-page: 319 issue: 4 year: 1995 ident: 1527_CR3 publication-title: Optim. Methods Softw. doi: 10.1080/10556789508805619 – volume: 30 issue: 2 year: 2023 ident: 1527_CR26 publication-title: Numer. Linear Algebra Appl. – volume: 57 start-page: 43 issue: 1 year: 2023 ident: 1527_CR16 publication-title: RAIRO-Oper. Res. doi: 10.1051/ro/2022213 – start-page: 355 volume-title: Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods year: 1998 ident: 1527_CR17 doi: 10.1007/978-1-4757-6388-1_18 – volume: 2013 year: 2013 ident: 1527_CR30 publication-title: J. Appl. Math. – volume: 54 start-page: 981 issue: 4 year: 2020 ident: 1527_CR12 publication-title: RAIRO-Oper. Res. doi: 10.1051/ro/2019045 – volume: 85 start-page: 377 year: 2023 ident: 1527_CR20 publication-title: J. Glob. Optim. doi: 10.1007/s10898-022-01213-4 – volume: 43 start-page: 388 issue: 3 year: 2021 ident: 1527_CR35 publication-title: Math. Numer. Sin. – volume: 53 start-page: 399 year: 2018 ident: 1527_CR28 publication-title: Calcolo doi: 10.1007/s10092-015-0154-z – volume: 91 start-page: 201 issue: 2 year: 2002 ident: 1527_CR56 publication-title: Math. Program. doi: 10.1007/s101070100263 – volume: 118 start-page: 393 year: 2023 ident: 1527_CR10 publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2023.01.018 – volume: 147 start-page: 129 year: 2020 ident: 1527_CR22 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2019.08.022 – volume: 234 start-page: 625 issue: 3 year: 2014 ident: 1527_CR11 publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2013.11.012 – year: 2022 ident: 1527_CR24 publication-title: Numer. Algorithms doi: 10.1007/s11075-022-01483-9 – volume: 88 start-page: 389 year: 2021 ident: 1527_CR5 publication-title: Numer. Algorithms doi: 10.1007/s11075-020-01043-z – volume: 391 year: 2021 ident: 1527_CR6 publication-title: J. Comput. Appl. Math. – volume: 42 start-page: 457 issue: 4 year: 2020 ident: 1527_CR53 publication-title: Math. Numer. Sin. – volume: 9 start-page: 117 issue: 1 year: 2013 ident: 1527_CR31 publication-title: J. Ind. Manag. Optim. doi: 10.3934/jimo.2013.9.117 – volume: 55 start-page: 53 year: 2018 ident: 1527_CR33 publication-title: Calcolo doi: 10.1007/s10092-018-0291-2 |
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60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and... Al-Baali et al. (Comput. Optim. Appl. 60:89–110, 2015) have proposed a three-term conjugate gradient method which satisfies a sufficient descent condition and... |
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| SubjectTerms | Algebra Algorithms Computer Science Conjugate gradient method Continuity (mathematics) Convergence Iterative methods Mathematical analysis Methods Nonlinear equations Numeric Computing Numerical Analysis Optimization Original Paper Regression analysis Theory of Computation |
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