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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Main Authors: | ZHANG, Hu, ZHOU, Pan, YANG, Yi, FENG, Jiashi |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2019
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Online Access: | 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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Institution: | Singapore Management University |
Language: | English |
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