Abstract
To improve the computational efficiency, based on the generalized alternating direction method of multipliers (GADMM), we consider a class of accelerated method for solving multi-block nonconvex and nonsmooth optimization problems. First, we linearize the smooth part of the objective function and add proximal terms in subproblems, resulting in the proximal linearized GADMM. Then, we introduce an inertial technique and give the inertial proximal linearized GADMM. The convergence of the regularized augmented Lagrangian function sequence is proved under some appropriate assumptions. When some component functions of the objective function are convex, we use the error bound condition and obtain that the sequences generated by the algorithms locally converge to the critical point in a R-linear rate. Moreover, we apply the proposed algorithms to SCAD and robust PCA problems to verify the efficiency of the algorithms.
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22 April 2024
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The work described in this paper was jointly supported by grants from the National Natural Science Foundation of China (Nos. 72071202, 71971108), Top Six Talents’ Project of Jiangsu Province (No. XNYQC-001), Mathematics Tianyuan Fund of the National Natural Sciences Foundation of China (No. 12326321), Key Laboratory of Mathematics and Engineering Applications, Ministry of Education and Jiangsu National Applied Mathematics Center.
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All authors read and approved the final manuscript. K.Z. is mainly responsible for theoretical analysis and numerical experiments; H.S. and T.W. mainly contribute to algorithm design and theoretical analysis; X.W. is mainly contributing to algorithm design and numerical experiments.
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Appendix. The proof of (34)
Appendix. The proof of (34)
Here we give the proof of the inequality (34).
Proof
Let \({\bar{w}}^{k+1}=({\bar{x}}^{k+1},{\bar{y}}^{k+1},{\bar{\lambda }}^{k+1})\in \text {crit}\mathcal {L}_{\beta }\), then
By the definition of \(\mathcal {L}_\beta (\cdot )\) (2), it holds that
From the convexity of \(f_i\), we get
And from the Lipschitz continuity of \(\nabla g\) and \(B^{\top }\lambda ^{k+1}=\nabla g(y^{k})+Q\Delta y^{k+1}\), we have
Combining (A1), (A2) and (A3), it follows that
Considering that \(\sum \nolimits _{i=1}^{n}{{{A}_{i}}{\bar{x}_{i}}^{k+1}}+B{\bar{y}^{k+1}}-b=0\) and \({{\lambda }^{k+1}}={{\lambda }^{k}}-\beta (\sum \limits _{i=1}^{n}{{{A}_{i}}}{x}_{i}^{k+1}+B{{y}^{k+1}}-b)+\beta (1-s)(\sum \limits _{i=1}^{n}{{{A}_{i}}}{x}_{i}^{k+1}+B{{y}^{k}}-b)\), we have
Further, from \(s\beta \sum \nolimits _{i=1}^nA_ix_i^{k+1}=\lambda ^k-\lambda ^{k+1}-\beta (By^{k+1}-b)+\beta (1-s)(By^k-b)\), we obtain following two equalities
and
Substituting the above two equations into (A4), then we obtain (34).\(\square \)
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Zhang, K., Shao, H., Wu, T. et al. A class of accelerated GADMM-based method for multi-block nonconvex optimization problems. Numer Algor (2024). https://doi.org/10.1007/s11075-024-01821-z
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DOI: https://doi.org/10.1007/s11075-024-01821-z