Abstract
We study a class of constrained sparse optimization problems with cardinality penalty, where the feasible set is defined by box constraint, and the loss function is convex but not necessarily smooth. First, we propose an accelerated smoothing hard thresholding (ASHT) algorithm for solving such problems, which combines smoothing approximation, extrapolation technique and iterative hard thresholding method. The extrapolation coefficients can be chosen to satisfy \(\sup _k \beta _k=1\). We discuss the convergence of ASHT algorithm with different extrapolation coefficients, and give a sufficient condition to ensure that any accumulation point of the iterates is a local minimizer of the original problem. For a class of special updating schemes on the extrapolation coefficients, we obtain that the iterates are convergent to a local minimizer of the problem, and the convergence rate is \(o(\ln ^{\sigma } k/k)\) with \(\sigma \in (1/2, 1]\) on the loss and objective function values. Second, we consider the case in which the loss function is Lipschitz continuously differentiable, and develop an accelerated hard thresholding (AHT) algorithm to solve it. We prove that the iterates of AHT algorithm converge to a local minimizer of the problem that satisfies a desirable lower bound property. Moreover, we show that the convergence rates of loss and objective function values are \(o(k^{-2})\). Finally, some numerical examples are presented to show the theoretical results.
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Data Availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
References
Adly, S., Attouch, H.: Finite convergence of proximal-gradient inertial algorithms combining dry friction with Hessian-driven damping. SIAM J. Optim. 30(3), 2134–2162 (2020)
Alecsa, C.D., László, S.C., Pinţa, T.: An extension of the second order dynamical system that model Nesterov’s convex gradient method. Appl. Math. Optim. 84(2), 1687–1716 (2021)
Attouch, H., László, S.: Newton-like inertial dynamics and proximal algorithms governed by maximally monotone operators. SIAM J. Optim. 30(4), 3252–3283 (2020)
Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward-backward method is actually faster than \(1/k^2\). SIAM J. Optim. 26(3), 1824–1834 (2016)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont (1999)
Bian, W.: Smoothing accelerated algorithm for constrained nonsmooth convex optimization problems (in chinese). Sci. Sin. Math. 50, 1651–1666 (2020)
Bian, W., Chen, X.: Optimality and complexity for constrained optimization problems with nonconvex regularization. Math. Oper. Res. 42(4), 1063–1084 (2017)
Bian, W., Chen, X.: A smoothing proximal gradient algorithm for nonsmooth convex regression with cardinality penalty. SIAM J. Numer. Anal. 58(1), 858–883 (2020)
Bian, W., Chen, X., Ye, Y.Y.: Complexity analysis of interior point algorithms for non-Lipschitz and nonconvex minimization. Math. Program. 149(1–2), 301–327 (2015)
Blumensath, T., Davies, M.: Sparse and shift-invariant representations of music. IEEE Trans. Audio Speech Lang. Process. 14(1), 50–57 (2006)
Blumensath, T., Davies, M.: Iterative thresholding for sparse approximations. J. Fourier Anal. Appl. 14(5–6), 629–654 (2008)
Blumensath, T., Davies, M.: Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal. 27(3), 265–274 (2009)
Boţ, R.I., Böhm, A.: Variable smoothing for convex optimization problems using stochastic gradients. J. Sci. Comput. https://doi.org/10.1007/s10915-020-01332-8 (2020)
Bruckstein, A., Donoho, D., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51(1), 34–81 (2009)
Candès, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
Chambolle, A., DeVore, R., Lee, N., Lucier, B.: Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans. Image Process. 7(3), 319–335 (1998)
Chen, X.: Smoothing methods for nonsmooth, nonconvex minimization. Math. Program. 134(1), 71–99 (2012)
Combettes, P., Wajs, V.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005)
Dai, W., Milenkovic, O.: Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inf. Theory 55(5), 2230–2249 (2009)
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)
Doikov, N., Nesterov, Y.: Contracting proximal methods for smooth convex optimization. SIAM J. Optim. 30(4), 3146–3169 (2020)
Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Fan, J., Li, R.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96(456), 1348–1360 (2001)
Hale, E., Yin, W., Zhang, Y.: Fixed-point continuation for \(\ell _1\)-minimization: methodology and convergence. SIAM J. Optim. 19(3), 1107–1130 (2008)
Hoda, S., Gilpin, A., Pena, J., Sandholm, T.: Smoothing techniques for computing Nash equilibria of sequential games. Math. Oper. Res. 35(2), 494–512 (2010)
Liu, Y., Wu, Y.: Variable selection via a combination of the \(\ell _0\) and \(\ell _1\) penalties. J. Comput. Graph. Stat. 16(4), 782–798 (2007)
Lu, Z.: Iterative hard thresholding methods for \(\ell _0\) regularized convex cone programming. Math. Program. 147(1–2), 125–154 (2014)
Lu, Z., Zhang, Y.: Sparse approximation via penalty decomposition methods. SIAM J. Optim. 23(4), 2448–2478 (2013)
Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)
Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103(1), 127–152 (2005)
Nesterov, Y.: Gradient methods for minimizing composite functions. Math. Program. 140(1), 125–161 (2013)
Nikolova, M.: Local strong homogeneity of a regularized estimator. SIAM J. Appl. Math. 61(2), 633–658 (2000)
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit-recursive function approximation with applications to wavelet decomposition. In: Conference Record of the Twenty-Seventh Asilomar Conference on Signal, Systems and Computers, vol. 1–2, pp. 40–44 (1993)
Peleg, D., Meir, R.: A bilinear formulation for vector sparsity optimization. Signal Process. 88(2), 375–389 (2008)
Soubies, E., Blanc-Feraud, L., Aubert, G.: A continuous exact \(\ell _0\) penalty (CEL0) for least squares regularized problem. SIAM J. Imaging Sci. 8(3), 1607–1639 (2015)
Su, W., Boyd, S., Candès, E.: A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights. J. Mach. Learn. Res. 17(153), 1–43 (2016)
Tan, C.H., Qian, Y.Q., Ma, S.Q., Zhang, T.: Accelerated dual-averaging primal-dual method for composite convex minimization. Optim. Method Softw. 35(4), 741–766 (2020)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B-Methodol. 58(1), 267–288 (1996)
Wen, B., Chen, X., Pong, T.: Linear convergence of proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth minimization problems. SIAM J. Optim. 27(1), 124–145 (2017)
Wen, B., Xue, X.P.: On the convergence of the iterates of proximal gradient algorithm with extrapolation for convex nonsmooth minimization problems. J. Glob. Optim. 75(3), 767–787 (2019)
Wu, F., Bian, W.: Accelerated iterative hard thresholding algorithm for \(\ell _0\) regularized regression problem. J. Glob. Optim. 76(4), 819–840 (2020)
Wu, F., Bian, W.: Accelerated forward-backward method with fast convergence rate for nonsmooth convex optimization beyond differentiability. arXiv:2110.01454v1 (2021)
Yu, Q., Zhang, X.Z.: A smoothing proximal gradient algorithm for matrix rank minimization problem. Comput. Optim. Appl. 81(2), 519–538 (2022)
Zhang, C., Chen, X.: A smoothing active set method for linearly constrained non-Lipschitz nonconvex optimization. SIAM J. Optim. 30(1), 1–30 (2020)
Zheng, Z., Fan, Y., Lv, J.: High dimensional thresholded regression and shrinkage effect. J. R. Stat. Soc. Ser. B-Stat. Methodol. 76(3), 627–649 (2014)
Funding
This work is supported by the National Natural Science Foundation of China grants (No. 12271127, 62176073), the National Key Research and Development Program of China (No. 2021YFA1003500) and the Fundamental Research Funds for the Central Universities (No. 2022FRFK0600XX).
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Bian, W., Wu, F. Accelerated Smoothing Hard Thresholding Algorithms for \(\ell _0\) Regularized Nonsmooth Convex Regression Problem. J Sci Comput 96, 33 (2023). https://doi.org/10.1007/s10915-023-02249-8
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DOI: https://doi.org/10.1007/s10915-023-02249-8
Keywords
- Nonsmooth optimization
- Smoothing method
- Cardinality penalty
- Accelerated algorithm with extrapolation
- Convergence rate