Generalized majorization-minimization for non-convex optimization
Majorization-Minimization (MM) algorithms optimize an objective function by iteratively minimizing its majorizing surrogate and offer attractively fast convergence rate for convex problems. However, their convergence behaviors for non-convex problems remain unclear. In this paper, we propose a novel...
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格式: | text |
語言: | English |
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Institutional Knowledge at Singapore Management University
2019
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在線閱讀: | https://ink.library.smu.edu.sg/sis_research/9006 https://ink.library.smu.edu.sg/context/sis_research/article/10009/viewcontent/2019_IJCAI_MM.pdf |
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機構: | Singapore Management University |
語言: | English |