[1] |
Censor Y, Elfving T. A multiprojection algorithm using Bregman projections in a product space. Numer Algorithms, 1994, 8: 221-239
|
[2] |
López G, Martín-Márquez V, Wang F H, et al. Solving the split feasibility problem without prior knowledge of matrix norms. Inverse Probl, 2012, 28(8): 085004
|
[3] |
Qu B, Xiu N H. A note on the CQ algorithm for the split feasibility problem. Inverse Probl, 2005, 21(5): 1655-1665
|
[4] |
Byrne C. A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl, 2003, 20(1): 103-120
|
[5] |
Aubin J P. Optima and Eauilibria: An Introduction to Nonliner Analysis. Berlin: Springer-Verlag, 1993
|
[6] |
Byrne C. Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl, 2002, 18(2): 441-453
|
[7] |
Yang Q Z. On variable-step relaxed projection algorithm for variational inequalities. J Math Anal Appl, 2005, 302(1): 166-179
|
[8] |
Zhao J L, Yang Q Z. Self-adaptive projection methods for the multiple-sets split feasibility problem. Inverse Probl, 2011, 27(3): 035009
|
[9] |
Wang F H, Xu H K. Cyclic algorithms for split feasibility problems in Hilbert spaces. Nonlinear Anal: Theory Methods Appl, 2011, 74(12): 4105-4111
|
[10] |
Qin X L, Wang L. A fixed point method for solving a split feasibility problem in Hilbert spaces. Rev R Acad Cienc Exactas Fís Nat Ser A Mat RACSAM, 2019, 113: 315-325
|
[11] |
Dong Q L, He S N, Rassias M T. General splitting methods with linearization for the split feasibility problem. J Glob Optim, 2021, 79: 813-836
|
[12] |
Dong Q L, Liu L L, Rassias M T. The strong convergence of Douglas-Rachford methods for the split feasibility problem// Parasidis I N, Providas E, Rassias M T. Mathematical Analysis in Interdisciplinary Research. Switzerland: Springer International Publishing, 2022: 213-233
|
[13] |
刘洋, 薛中会, 王永全, 等. 分裂可行性问题的外推加速线性交替方向乘子法及其全局收敛性. 计算机科学, 2023, 50(6): 261-265
doi: 10.11896/jsjkx.230100009
|
|
Liu Y, Xue Z H, Wang Y Q, et al. Extrapolation accelerated linear alternating direction multiplier method for split feasibility problems and its global convergence. Computer Science, 2023, 50(6): 261-265
doi: 10.11896/jsjkx.230100009
|
[14] |
Yu Z S, Lin J, Sun J, et al. Spectral gradient projection method for monotone nonlinear equations with convex constraints. Appl Numer Math, 2009, 59(10): 2416-2423
|
[15] |
尹江华, 简金宝, 江羡珍. 凸约束非光滑方程组基于自适应线搜索的谱梯度投影算法. 计算数学, 2020, 42(4): 457-471
doi: 10.12286/jssx.2020.4.457
|
|
Yin J H, Jian J B, Jiang X Z. A spectral gradient projection algorithm for convex constrained nonsmooth equations based on an adaptive line search. Math Numer Sin, 2020, 42(4): 457-471
doi: 10.12286/jssx.2020.4.457
|
[16] |
Sun M, Liu J. New hybrid conjugate gradient projection method for the convex constrained equations. Calcolo, 2016, 53: 399-411
|
[17] |
Ding Y Y, Xiao Y H, Li J W. A class of conjugate gradient methods for convex constrained monotone equations. Optimization, 2017, 66(12): 2309-2328
|
[18] |
Sun M, Liu J. Three derivative-free projection methods for nonlinear equations with convex constraints. J Appl Math Comput, 2015, 47: 265-276
|
[19] |
Liu J K, Li S J. Multivariate spectral DY-type projection method for convex constrained nonlinear monotone equations. J Ind Manag Optim, 2017, 13(1): 283-295
|
[20] |
Gao P T, He C J, Liu Y. An adaptive family of projection methods for constrained monotone nonlinear equations with applications. Appl Math Comput, 2019, 359: 1-16
|
[21] |
Ibrahim A H, Kumam P, Kumam W. A family of derivative-free conjugate gradient methods for constrained nonlinear equations and image restoration. IEEE Access, 2020, 8: 162714-162729
|
[22] |
Yin J H, Jian J B, Jiang X Z, et al. A hybrid three-term conjugate gradient projection method for constrained nonlinear monotone equations with applications. Numer Algorithms, 2021, 88: 389-418
|
[23] |
Polyak B T. Some methods of speeding up the convergence of iteration methods. USSR Comput Math Math Phys, 1964, 4(5): 1-17
|
[24] |
Sahu D R, Cho Y J, Dong Q L, et al. Inertial relaxed CQ algorithms for solving a split feasibility problem in Hilbert spaces. Numer Algorithms, 2021, 87: 1075-1095
|
[25] |
Suantai S, Panyanak B, Kesornprom S, et al. Inertial projection and contraction methods for split feasibility problem applied to compressed sensing and image restoration. Optim Lett, 2022, 16: 1725-1744
|
[26] |
Jian J B, Yin J H, Tang C M, et al. A family of inertial derivative-free projection methods for constrained nonlinear pseudo-monotone equations with applications. Comput Appl Math, 2022, 41(7): Article 309
|
[27] |
Jiang X Z, Ye X M, Huang Z F, et al. A family of hybrid conjugate gradient method with restart procedure for unconstrained optimizations and image restorations. Comput Oper Res, 2023, 159: 106341
|
[28] |
Zarantonello E H. Projections on convex sets in Hilbert space and spectral theory// Zarantonello E H. Contributions to Nonlinear Functional Analysis. New York: Academic Press, 1971: 237-424
|
[29] |
Polyak B T. Introduction to Optimization. New York: Optimization Software Inc, 1987
|
[30] |
Alves M M, Eckstein J, Geremia M, et al. Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms. Comput Optim Appl, 2020, 75: 389-422
|
[31] |
Ma G D, Jin J C, Jian J B, et al. 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
|
[32] |
Bauschke H H, Combettes P L. Correction to:Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Switzerland: Springer International Publishing, 2017
|
[33] |
尹江华. 非线性方程组投影型算法与非精确 Levenberg-Marquardt 型算法研究及其应用. 呼和浩特: 内蒙古大学, 2021
|
|
Yin J H. Research on projection type algorithms and intexact Levenberg-Marquardt type algorithms for nonlinear equations with applitions. Hohhot: Inner Mongolia University, 2021
|
[34] |
Yang Q Z. The relaxed CQ algorithm solving the split feasibility problem. Inverse Probl, 2004, 20(4): 1261-1266
|
[35] |
Shehu Y, Gibali A. New inertial relaxed method for solving split feasibilities. Optim Lett, 2020, 15: 2109-2126
|