Global convergence of proximal iteratively reweighted algorithm |
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| Authors: | Tao Sun Hao Jiang Lizhi Cheng |
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| Institution: | 1.College of Science,National University of Defense Technology,Changsha,People’s Republic of China;2.College of Computer,National University of Defense Technology,Changsha,People’s Republic of China;3.The State Key Laboratory for High Performance Computation,National University of Defense Technology,Changsha,People’s Republic of China |
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| Abstract: | In this paper, we investigate the convergence of the proximal iteratively reweighted algorithm for a class of nonconvex and nonsmooth problems. Such problems actually include numerous models in the area of signal processing and machine learning research. Two extensions of the algorithm are also studied. We provide a unified scheme for these three algorithms. With the Kurdyka–?ojasiewicz property, we prove that the unified algorithm globally converges to a critical point of the objective function. |
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