A modified inertial three-term conjugate gradient projection method for constrained nonlinear equations with applications in compressed sensing
In this paper, based on the three-term conjugate gradient projection method and the inertial technique, we propose a modified inertial three-term conjugate gradient projection method for solving nonlinear monotone equations with convex constraints. Embedding the inertial extrapolation step in the de...
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| Vydané v: | Numerical algorithms Ročník 92; číslo 3; s. 1621 - 1653 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.03.2023
Springer Nature B.V |
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| ISSN: | 1017-1398, 1572-9265 |
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| Abstract | In this paper, based on the three-term conjugate gradient projection method and the inertial technique, we propose a modified inertial three-term conjugate gradient projection method for solving nonlinear monotone equations with convex constraints. Embedding the inertial extrapolation step in the design for the search direction, the resulting direction satisfies the sufficient descent property which is independent of any line search rules. The global convergence and Q-linear convergence rate of the proposed algorithm are established under standard conditions. Numerical comparisons with three existing methods demonstrate that the proposed algorithm possesses superior numerical performance and good robustness for solving large-scale equations. Finally, the proposed method is applied to solve the sparse signal problems and image restoration in compressed sensing. |
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| AbstractList | In this paper, based on the three-term conjugate gradient projection method and the inertial technique, we propose a modified inertial three-term conjugate gradient projection method for solving nonlinear monotone equations with convex constraints. Embedding the inertial extrapolation step in the design for the search direction, the resulting direction satisfies the sufficient descent property which is independent of any line search rules. The global convergence and Q-linear convergence rate of the proposed algorithm are established under standard conditions. Numerical comparisons with three existing methods demonstrate that the proposed algorithm possesses superior numerical performance and good robustness for solving large-scale equations. Finally, the proposed method is applied to solve the sparse signal problems and image restoration in compressed sensing. |
| Author | Jin, Jiachen Yin, Jianghua Han, Daolan Ma, Guodong Jian, Jinbao |
| Author_xml | – sequence: 1 givenname: Guodong surname: Ma fullname: Ma, Guodong organization: College of Mathematics and Physics, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Center for Applied Mathematics and Artificial Intelligence, Guangxi Minzu University – sequence: 2 givenname: Jiachen surname: Jin fullname: Jin, Jiachen organization: College of Mathematics and Physics, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Center for Applied Mathematics and Artificial Intelligence, Guangxi Minzu University – sequence: 3 givenname: Jinbao orcidid: 0000-0001-8048-7397 surname: Jian fullname: Jian, Jinbao email: jianjb@gxu.edu.cn organization: College of Mathematics and Physics, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Center for Applied Mathematics and Artificial Intelligence, Guangxi Minzu University – sequence: 4 givenname: Jianghua surname: Yin fullname: Yin, Jianghua organization: College of Mathematics and Physics, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Center for Applied Mathematics and Artificial Intelligence, Guangxi Minzu University – sequence: 5 givenname: Daolan surname: Han fullname: Han, Daolan organization: College of Mathematics and Physics, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Center for Applied Mathematics and Artificial Intelligence, Guangxi Minzu University |
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| Cites_doi | 10.1016/j.apnum.2021.03.005 10.1007/s11075-015-9961-1 10.1145/355934.355936 10.3934/jimo.2018149 10.1080/02331934.2020.1716752 10.1007/s10898-019-00819-5 10.1016/j.na.2011.02.040 10.1016/j.cam.2020.112781 10.1007/BF02591989 10.1016/j.cam.2021.113423 10.1007/s10107-002-0305-2 10.1109/JSTSP.2007.910281 10.1137/15100463X 10.1093/imanum/drq015 10.1007/s00186-006-0140-y 10.1090/S0025-5718-1980-0572855-7 10.1016/0041-5553(64)90137-5 10.1090/S0002-9939-1957-0087897-7 10.1090/S0025-5718-06-01840-0 10.1007/s11075-018-0603-2 10.1016/j.apnum.2020.02.017 10.1137/030601880 10.1016/j.apnum.2020.08.009 10.1016/j.apnum.2009.04.004 10.1007/s101070100263 10.1023/B:JOTA.0000025712.43243.eb 10.1360/N012016-00134 10.1007/s11075-018-0527-x 10.1137/140980910 10.1109/TNNLS.2014.2310059 10.1007/s10957-004-1721-7 10.1016/j.cam.2004.02.013 10.1007/s10092-018-0291-2 10.1007/s11075-020-01043-z 10.3934/jimo.2013.9.117 10.3934/jimo.2021173 10.1016/B978-0-12-775850-3.50013-3 10.1016/j.cam.2019.03.025 10.1007/978-1-4757-6388-1_18 |
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| References | Yu, Niu, Ma (CR37) 2013; 9 Figueiredo, Nowak, Wright (CR42) 2007; 1 Meintjes, Morgan (CR1) 1987; 22 Sun, Womersley, Qi (CR4) 2002; 94 Dolan, Moré (CR41) 2002; 91 Qi, Tong, Li (CR5) 2004; 120 Sabi’u, Shah, Waziri (CR11) 2020; 153 Phelps (CR33) 1957; 8 Pang (CR43) 1986; 36 Jolaoso, Alakoya, Taiwo (CR27) 2021; 70 Hager, Zhang (CR8) 2005; 16 CR34 Xiao, Wang, Hu (CR2) 2011; 74 Li (CR28) 2020; 16 Bot, Csetnek, Hendrich (CR26) 2015; 256 La Cruz, Martínez, Raydan (CR38) 2006; 75 Polyak (CR19) 1964; 4 Polyak (CR35) 1987 Hu, Wu, Yuan (CR12) 2020; 158 Koorapetse, Kaelo, Lekoko (CR13) 2021; 165 Gao, Cai, Han (CR22) 2020; 76 Li, Li (CR32) 2011; 31 Yin, Jian, Jiang (CR15) 2021; 88 Yin, Jian, Jiang (CR18) 2021; 391 Moré, Garbow, Hillstrom (CR39) 1981; 7 Liu, Feng (CR10) 2019; 82 CR9 Thong, Van Hieu (CR25) 2019; 80 Chorowski, Zurada (CR3) 2014; 26 Wang, Wang, Xu (CR36) 2007; 66 Yu, Lin, Sun (CR31) 2009; 59 CR23 Gao, He, Liu (CR14) 2019; 359 Chen, Ma, Yang (CR24) 2015; 25 Nocedal (CR29) 1980; 35 Zhou, Li (CR40) 2007; 25 Zheng, Yang, Liang (CR17) 2020; 375 Chen, Chan, Ma (CR20) 2015; 8 Amini, Kamandi (CR30) 2015; 70 Zhou, Toh (CR7) 2005; 125 Gao, He (CR16) 2018; 55 Dou, Li, Liu (CR21) 2017; 47 Kanzow, Yamashita, Fukushima (CR6) 2004; 172 C Kanzow (1356_CR6) 2004; 172 L Zheng (1356_CR17) 2020; 375 X Gao (1356_CR22) 2020; 76 DV Thong (1356_CR25) 2019; 80 J Nocedal (1356_CR29) 1980; 35 LO Jolaoso (1356_CR27) 2021; 70 MY Dou (1356_CR21) 2017; 47 RI Bot (1356_CR26) 2015; 256 WJ Zhou (1356_CR40) 2007; 25 JH Yin (1356_CR15) 2021; 88 CH Chen (1356_CR24) 2015; 25 WW Hager (1356_CR8) 2005; 16 1356_CR34 M Li (1356_CR28) 2020; 16 GH Yu (1356_CR37) 2013; 9 M Koorapetse (1356_CR13) 2021; 165 PT Gao (1356_CR14) 2019; 359 WJ Hu (1356_CR12) 2020; 158 W La Cruz (1356_CR38) 2006; 75 BT Polyak (1356_CR35) 1987 CW Wang (1356_CR36) 2007; 66 K Meintjes (1356_CR1) 1987; 22 JH Yin (1356_CR18) 2021; 391 DF Sun (1356_CR4) 2002; 94 1356_CR9 G Zhou (1356_CR7) 2005; 125 RR Phelps (1356_CR33) 1957; 8 YH Xiao (1356_CR2) 2011; 74 PT Gao (1356_CR16) 2018; 55 LQ Qi (1356_CR5) 2004; 120 JJ Moré (1356_CR39) 1981; 7 JS Pang (1356_CR43) 1986; 36 1356_CR23 MA Figueiredo (1356_CR42) 2007; 1 K Amini (1356_CR30) 2015; 70 J Chorowski (1356_CR3) 2014; 26 ED Dolan (1356_CR41) 2002; 91 BT Polyak (1356_CR19) 1964; 4 CH Chen (1356_CR20) 2015; 8 Q Li (1356_CR32) 2011; 31 J Sabi’u (1356_CR11) 2020; 153 ZS Yu (1356_CR31) 2009; 59 JK Liu (1356_CR10) 2019; 82 |
| References_xml | – volume: 165 start-page: 431 year: 2021 end-page: 441 ident: CR13 article-title: A derivative-free RMIL conjugate gradient projection method for convex constrained nonlinear monotone equations with applications in compressive sensing publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2021.03.005 – volume: 70 start-page: 559 issue: 3 year: 2015 end-page: 570 ident: CR30 article-title: A new line search strategy for finding separating hyperplane in projection-based methods publication-title: Numer. Algorithms doi: 10.1007/s11075-015-9961-1 – volume: 7 start-page: 17 issue: 1 year: 1981 end-page: 41 ident: CR39 article-title: Testing unconstrained optimization software publication-title: ACM Trans. Math. Softw. doi: 10.1145/355934.355936 – volume: 16 start-page: 245 issue: 1 year: 2020 end-page: 260 ident: CR28 article-title: A three term Polak-Ribiére-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method publication-title: J. Ind.Manag. Optim. doi: 10.3934/jimo.2018149 – volume: 70 start-page: 387 issue: 2 year: 2021 end-page: 412 ident: CR27 article-title: Inertial extragradient method via viscosity approximation approach for solving equilibrium problem in Hilbert space publication-title: Optim. doi: 10.1080/02331934.2020.1716752 – year: 1987 ident: CR35 publication-title: Introduction to Optimization, Optimization Software, p. 49 – volume: 76 start-page: 863 issue: 4 year: 2020 end-page: 887 ident: CR22 article-title: A Gauss-Seidel type inertial proximal alternating linearized minimization for a class of nonconvex optimization problems publication-title: J. Glob. Optim. doi: 10.1007/s10898-019-00819-5 – volume: 256 start-page: 472 year: 2015 end-page: 487 ident: CR26 article-title: Inertial Douglas-Rachford splitting for monotone inclusion problems publication-title: Appl. Math. Comput. – volume: 22 start-page: 333 issue: 4 year: 1987 end-page: 361 ident: CR1 article-title: A methodology for solving chemical equilibrium systems publication-title: Appl. Math. Comput. – volume: 74 start-page: 3570 issue: 11 year: 2011 end-page: 3577 ident: CR2 article-title: Non-smooth equations based method for ℓ1-norm problems with applications to compressed sensing publication-title: Nonlinear Anal. Theory Methods Appl. doi: 10.1016/j.na.2011.02.040 – volume: 375 start-page: 112781 year: 2020 ident: CR17 article-title: A conjugate gradient projection method for solving equations with convex constraints publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2020.112781 – volume: 36 start-page: 54 issue: 1 year: 1986 end-page: 71 ident: CR43 article-title: Inexact Newton methods for the nonlinear complementarity problem publication-title: Math. Program. doi: 10.1007/BF02591989 – volume: 391 start-page: 113423 year: 2021 ident: CR18 article-title: A generalized hybrid CGPM-based algorithm for solving large-scale convex constrained equations with applications to image restoration publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2021.113423 – volume: 94 start-page: 167 issue: 1 year: 2002 end-page: 187 ident: CR4 article-title: A feasible semismooth asymptotically Newton method for mixed complementarity problems publication-title: Math. Program. doi: 10.1007/s10107-002-0305-2 – volume: 1 start-page: 586 issue: 4 year: 2007 end-page: 597 ident: CR42 article-title: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2007.910281 – volume: 8 start-page: 2239 issue: 4 year: 2015 end-page: 2267 ident: CR20 article-title: Inertial proximal ADMM for linearly constrained separable convex optimization publication-title: SIAM J. Imaging Sci. doi: 10.1137/15100463X – volume: 31 start-page: 1625 issue: 4 year: 2011 end-page: 1635 ident: CR32 article-title: A class of derivative-free methods for large-scale nonlinear monotone equations publication-title: IMA J. Numer. Anal. doi: 10.1093/imanum/drq015 – volume: 66 start-page: 33 issue: 1 year: 2007 end-page: 46 ident: CR36 article-title: A projection method for a system of nonlinear monotone equations with convex constraints publication-title: Math. Methods Oper. Res. doi: 10.1007/s00186-006-0140-y – ident: CR23 – volume: 35 start-page: 773 issue: 151 year: 1980 end-page: 782 ident: CR29 article-title: Updating quasi-Newton matrices with limited storage publication-title: Math. Comput. doi: 10.1090/S0025-5718-1980-0572855-7 – volume: 4 start-page: 1 issue: 5 year: 1964 end-page: 17 ident: CR19 article-title: Some methods of speeding up the convergence of iteration methods publication-title: USSR Comput. Math. Math. Phys. doi: 10.1016/0041-5553(64)90137-5 – volume: 8 start-page: 790 issue: 4 year: 1957 end-page: 797 ident: CR33 article-title: Convex sets and nearest points publication-title: Proc. Am. Math. Soc. doi: 10.1090/S0002-9939-1957-0087897-7 – volume: 75 start-page: 1429 issue: 255 year: 2006 end-page: 1448 ident: CR38 article-title: Spectral residual method without gradient information for solving large-scale nonlinear systems of equations publication-title: Math. Comput. doi: 10.1090/S0025-5718-06-01840-0 – volume: 82 start-page: 245 issue: 1 year: 2019 end-page: 262 ident: CR10 article-title: A derivative-free iterative method for nonlinear monotone equations with convex constraints publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0603-2 – volume: 153 start-page: 217 year: 2020 end-page: 233 ident: CR11 article-title: Two optimal Hager-Zhang conjugate gradient methods for solving monotone nonlinear equations publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2020.02.017 – volume: 16 start-page: 170 issue: 1 year: 2005 end-page: 192 ident: CR8 article-title: A new conjugate gradient method with guaranteed descent and an efficient line search publication-title: SIAM J. Optim. doi: 10.1137/030601880 – volume: 359 start-page: 1 year: 2019 end-page: 16 ident: CR14 article-title: An adaptive family of projection methods for constrained monotone nonlinear equations with applications publication-title: Appl. Math. Comput. – volume: 158 start-page: 360 year: 2020 end-page: 376 ident: CR12 article-title: Some modified Hestenes-Stiefel conjugate gradient algorithms with application in image restoration publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2020.08.009 – volume: 59 start-page: 2416 issue: 10 year: 2009 end-page: 2423 ident: CR31 article-title: Spectral gradient projection method for monotone nonlinear equations with convex constraints publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2009.04.004 – volume: 91 start-page: 201 issue: 2 year: 2002 end-page: 213 ident: CR41 article-title: Benchmarking optimization software with performance profiles publication-title: Math. program. doi: 10.1007/s101070100263 – volume: 120 start-page: 601 issue: 3 year: 2004 end-page: 625 ident: CR5 article-title: Active-set projected trust-region algorithm for box-constrained nonsmooth equations publication-title: J. Optim. Theory Appl. doi: 10.1023/B:JOTA.0000025712.43243.eb – volume: 47 start-page: 333 issue: 2 year: 2017 end-page: 348 ident: CR21 article-title: An inertial proximal Peaceman-Rachford splitting method (in chinese) publication-title: Scientia Sinica Mathematica doi: 10.1360/N012016-00134 – volume: 80 start-page: 1283 issue: 4 year: 2019 end-page: 1307 ident: CR25 article-title: Inertial subgradient extragradient algorithms with line-search process for solving variational inequality problems and fixed point problems publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0527-x – volume: 25 start-page: 2120 issue: 4 year: 2015 end-page: 2142 ident: CR24 article-title: A general inertial proximal point algorithm for mixed variational inequality problem publication-title: SIAM J. Optim. doi: 10.1137/140980910 – volume: 26 start-page: 62 issue: 1 year: 2014 end-page: 69 ident: CR3 article-title: Learning understandable neural networks with nonnegative weight constraints publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2014.2310059 – ident: CR9 – ident: CR34 – volume: 125 start-page: 205 issue: 1 year: 2005 end-page: 221 ident: CR7 article-title: Superline convergence of a Newton-type algorithm for monotone equations publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-004-1721-7 – volume: 172 start-page: 375 year: 2004 end-page: 397 ident: CR6 article-title: Levenberg-Marquardt methods for constrained nonlinear equations with strong local convergence properties publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2004.02.013 – volume: 55 start-page: 1 issue: 4 year: 2018 end-page: 17 ident: CR16 article-title: An efficient three-term conjugate gradient method for nonlinear monotone equations with convex constraints publication-title: Calcolo. doi: 10.1007/s10092-018-0291-2 – volume: 88 start-page: 389 issue: 1 year: 2021 end-page: 418 ident: CR15 article-title: A hybrid three-term conjugate gradient projection method for constrained nonlinear monotone equations with applications publication-title: Numer. Algorithms doi: 10.1007/s11075-020-01043-z – volume: 9 start-page: 117 issue: 1 year: 2013 end-page: 129 ident: CR37 article-title: Multivariate spectral gradient projection method for nonlinear monotone equations with convex constraints publication-title: J. Ind. Manag. Optim. doi: 10.3934/jimo.2013.9.117 – volume: 25 start-page: 89 year: 2007 end-page: 96 ident: CR40 article-title: Limited memory BFGS method for nonlinear monotone equations publication-title: J. Comput. Math. – volume: 31 start-page: 1625 issue: 4 year: 2011 ident: 1356_CR32 publication-title: IMA J. Numer. Anal. doi: 10.1093/imanum/drq015 – volume: 70 start-page: 387 issue: 2 year: 2021 ident: 1356_CR27 publication-title: Optim. doi: 10.1080/02331934.2020.1716752 – volume: 125 start-page: 205 issue: 1 year: 2005 ident: 1356_CR7 publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-004-1721-7 – ident: 1356_CR23 doi: 10.3934/jimo.2021173 – volume: 25 start-page: 2120 issue: 4 year: 2015 ident: 1356_CR24 publication-title: SIAM J. Optim. doi: 10.1137/140980910 – volume: 25 start-page: 89 year: 2007 ident: 1356_CR40 publication-title: J. Comput. Math. – volume: 391 start-page: 113423 year: 2021 ident: 1356_CR18 publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2021.113423 – volume: 26 start-page: 62 issue: 1 year: 2014 ident: 1356_CR3 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2014.2310059 – volume: 165 start-page: 431 year: 2021 ident: 1356_CR13 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2021.03.005 – volume: 8 start-page: 2239 issue: 4 year: 2015 ident: 1356_CR20 publication-title: SIAM J. Imaging Sci. doi: 10.1137/15100463X – volume: 55 start-page: 1 issue: 4 year: 2018 ident: 1356_CR16 publication-title: Calcolo. doi: 10.1007/s10092-018-0291-2 – volume: 120 start-page: 601 issue: 3 year: 2004 ident: 1356_CR5 publication-title: J. Optim. Theory Appl. doi: 10.1023/B:JOTA.0000025712.43243.eb – volume: 91 start-page: 201 issue: 2 year: 2002 ident: 1356_CR41 publication-title: Math. program. doi: 10.1007/s101070100263 – ident: 1356_CR34 doi: 10.1016/B978-0-12-775850-3.50013-3 – volume: 8 start-page: 790 issue: 4 year: 1957 ident: 1356_CR33 publication-title: Proc. Am. Math. Soc. doi: 10.1090/S0002-9939-1957-0087897-7 – volume: 94 start-page: 167 issue: 1 year: 2002 ident: 1356_CR4 publication-title: Math. Program. doi: 10.1007/s10107-002-0305-2 – volume: 36 start-page: 54 issue: 1 year: 1986 ident: 1356_CR43 publication-title: Math. Program. doi: 10.1007/BF02591989 – volume: 1 start-page: 586 issue: 4 year: 2007 ident: 1356_CR42 publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2007.910281 – volume: 359 start-page: 1 year: 2019 ident: 1356_CR14 publication-title: Appl. Math. Comput. doi: 10.1016/j.cam.2019.03.025 – volume: 9 start-page: 117 issue: 1 year: 2013 ident: 1356_CR37 publication-title: J. Ind. Manag. Optim. doi: 10.3934/jimo.2013.9.117 – volume: 7 start-page: 17 issue: 1 year: 1981 ident: 1356_CR39 publication-title: ACM Trans. Math. Softw. doi: 10.1145/355934.355936 – volume: 172 start-page: 375 year: 2004 ident: 1356_CR6 publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2004.02.013 – ident: 1356_CR9 doi: 10.1007/978-1-4757-6388-1_18 – volume: 4 start-page: 1 issue: 5 year: 1964 ident: 1356_CR19 publication-title: USSR Comput. Math. Math. Phys. doi: 10.1016/0041-5553(64)90137-5 – volume: 88 start-page: 389 issue: 1 year: 2021 ident: 1356_CR15 publication-title: Numer. Algorithms doi: 10.1007/s11075-020-01043-z – volume: 59 start-page: 2416 issue: 10 year: 2009 ident: 1356_CR31 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2009.04.004 – volume: 75 start-page: 1429 issue: 255 year: 2006 ident: 1356_CR38 publication-title: Math. Comput. doi: 10.1090/S0025-5718-06-01840-0 – volume: 16 start-page: 170 issue: 1 year: 2005 ident: 1356_CR8 publication-title: SIAM J. Optim. doi: 10.1137/030601880 – volume: 82 start-page: 245 issue: 1 year: 2019 ident: 1356_CR10 publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0603-2 – volume: 153 start-page: 217 year: 2020 ident: 1356_CR11 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2020.02.017 – volume: 66 start-page: 33 issue: 1 year: 2007 ident: 1356_CR36 publication-title: Math. Methods Oper. Res. doi: 10.1007/s00186-006-0140-y – volume: 158 start-page: 360 year: 2020 ident: 1356_CR12 publication-title: Appl. Numer. Math. doi: 10.1016/j.apnum.2020.08.009 – volume: 375 start-page: 112781 year: 2020 ident: 1356_CR17 publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2020.112781 – volume: 47 start-page: 333 issue: 2 year: 2017 ident: 1356_CR21 publication-title: Scientia Sinica Mathematica doi: 10.1360/N012016-00134 – volume: 22 start-page: 333 issue: 4 year: 1987 ident: 1356_CR1 publication-title: Appl. Math. Comput. – volume: 256 start-page: 472 year: 2015 ident: 1356_CR26 publication-title: Appl. Math. Comput. – volume: 70 start-page: 559 issue: 3 year: 2015 ident: 1356_CR30 publication-title: Numer. Algorithms doi: 10.1007/s11075-015-9961-1 – volume: 80 start-page: 1283 issue: 4 year: 2019 ident: 1356_CR25 publication-title: Numer. Algorithms doi: 10.1007/s11075-018-0527-x – volume: 76 start-page: 863 issue: 4 year: 2020 ident: 1356_CR22 publication-title: J. Glob. Optim. doi: 10.1007/s10898-019-00819-5 – volume: 16 start-page: 245 issue: 1 year: 2020 ident: 1356_CR28 publication-title: J. Ind.Manag. Optim. doi: 10.3934/jimo.2018149 – volume: 35 start-page: 773 issue: 151 year: 1980 ident: 1356_CR29 publication-title: Math. Comput. doi: 10.1090/S0025-5718-1980-0572855-7 – volume-title: Introduction to Optimization, Optimization Software, p. 49 year: 1987 ident: 1356_CR35 – volume: 74 start-page: 3570 issue: 11 year: 2011 ident: 1356_CR2 publication-title: Nonlinear Anal. Theory Methods Appl. doi: 10.1016/j.na.2011.02.040 |
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| SubjectTerms | Algebra Algorithms Computer Science Conjugate gradient method Constraints Convergence Hilbert space Image restoration Mathematical analysis Methods Nonlinear equations Numeric Computing Numerical Analysis Optimization Original Paper Random variables Robustness (mathematics) Theory of Computation |
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| Title | A modified inertial three-term conjugate gradient projection method for constrained nonlinear equations with applications in compressed sensing |
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