[1] Zhang, Y., Dong, B., Lu, Z.: 0 Minimization for wavelet frame based image restoration. Math. Comp. 82(282), 995-1015(2012)
[2] He, L., Wang, Y., Xiang, Z.: Wavelet frame-based image restoration using sparsity, nonlocal, and support prior of frame coefficients. Visual Comput. 35(2), 151-174(2019)
[3] Gong, M., Liu, J., Li, H., Cai, Q., Su, L.: A multiobjective sparse feature learning model for deep neural networks. IEEE T. Neur. Net. Lear. 26(12), 3263-3277(2017)
[4] Wang, C., Zeng, L., Zhang, L., Guo, Y., Yu, W.: An adaptive iteration reconstruction method for limited-angle CT image reconstruction. J. Inverse Ill-Posed Probl. 26(6), (2018)
[5] Lu, X., Wang, Y., Yuan, Y.: Sparse coding from a Bayesian perspective. IEEE T. Neur. Net. Lear. 24(6), 929-939(2013)
[6] Kiefer, L., Storath, M., Weinmann, A.: Iterative potts minimization for the recovery of signals with discontinuities from indirect measurements: the multivariate case. Found. Comput. Math. 21, 649-694(2021)
[7] Chen, J., Zhang, M., Li, Y.: A reconstruction algorithm for electrical capacitance tomography via total variation and 0-norm regularizations using experimental data. arXiv:1711.02544(2017)
[8] Lions, P.L., Mercier, B.: Splitting algorithms for the sum of two nonlinear operators. SIAM J. Numer. Anal. 16(6), 964-979(1979)
[9] Passty, G.B.: Ergodic convergence to a zero of the sum of monotone operators in Hilbert space. J. Math. Anal. Appl. 72(2), 383-390(1979)
[10] Attouch, H., Bolte, J., Svaiter, B.F.: Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods. Math. Program. 137(1), 91-129(2013)
[11] Lu, Z.: Iterative hard thresholding methods for 0 regularized convex cone programming. Math. Program. 147(1-2), 125-154(2013)
[12] Jiao, Y., Jin, B., Lu, X.: Iterative soft/hard thresholding with homotopy continuation for sparse recovery. IEEE Signal Proc. Let. 24(6), 784-788(2017)
[13] Bot, R.I., Csetnek, E.R., László, S.C.: An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions. EURO J. Comput. Optim. 4(1), 3-25(2016)
[14] Liang, J., Fadili, J., Peyré, G.: A multi-step inertial forward-backward splitting method for non-convex optimization. arXiv:1606.02118(2016)
[15] Li, H., Lin, Z.: Accelerated proximal gradient methods for nonconvex programming. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. MIT Press 1,379-387(2015)
[16] Gu, B., Huo, Z., Huang, H.: Inexact proximal gradient methods for non-convex and non-smooth optimization. arXiv:1612.06003v2(2018)
[17] Yao, Q., Kwok, J.T., Gao, F., Chen, W., Liu, T.Y.: Efficient inexact proximal gradient algorithm for nonconvex problems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligenc. (2017)
[18] Zhang, X., Zhang, X.: A new proximal iterative hard thresholding method with extrapolation for 0 minimization. J. Sci. Comput. 79(2), 809-826(2019)
[19] Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward-backward method is actually faster than 1/k2. SIAM J. Optim. 26(3), 1824-1834(2015)
[20] Bonettini, S., Porta, F., Ruggiero, V.: A variable metric forward-backward method with extrapolation. SIAM J. Sci. Comput. 38(4), A2558-A2584(2016)
[21] Frankel, P., Garrigos, G., Peypouquet, J.: Splitting methods with variable metric for Kurdyka- Łojasiewicz functions and general convergence rates. J. Optimiz. Theory App. 165(3), 874-900(2015)
[22] Bonettini, S., Loris, I., Porta, F., Prato, M., Rebegoldi, S.: On the convergence of variable metric linesearch based proximal-gradient method under the Kurdyka-Łojasiewicz inequality. Inverse Probl. 33(5), (2016)
[23] Salzo, S.: The variable metric forward-backward splitting algorithm under mild differentiability assumptions. SIAM J. Optim. 27(4), 2153-2181(2017)
[24] Chouzenoux, E., Pesquet, J.C., Repetti, A.: A block coordinate variable metric forward-backward algorithm. J. Global Optim. 66(3), 457-485(2016)
[25] Bonettini, S., Porta, F., Ruggiero, V., Zanni, L.: Variable metric techniques for forward-backward methods in imaging. J. Comput. Appl. Math. 385, (2021)
[26] Ochs, P.: Unifying abstract inexact convergence theorems and block coordinate variable metric iPiano. SIAM J. Optim. 29(1), 541-570(2019)
[27] Bonettini, S., Prato, M.: New convergence results for the scaled gradient projection method. Inverse Probl. 31(9), (2015)
[28] Porta, F., Prato, M., Zanni, L.: A new steplength selection for scaled gradient methods with application to image deblurring. J. Sci. Comput. 65(3), 895-919(2015)
[29] Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183-202(2009)
[30] Bertsekas, D.: Convex Optimization Theory. Athena Scientific, Belmont (2009)
[31] Zhang, X., Zhang, X.: A note on the complexity of proximal iterative hard thresholding algorithm. J. Oper. Res. Soc. China 3(4), 459-473(2015)
[32] Nocedal, J., Wright, S.J.: Numerical Optimization Second Edition. World Scientific (1999)
[33] Dong, Y.: New step lengths in conjugate gradient methods. Comput. Math. Appl. 60(3), 563-571(2010)
[34] Polyak, B.: Introduction to optimization. Chapman and Hall (1987)
