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Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Many modern computer vision and machine learning ap- plications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user. We propose an adaptive method
doi:10.1109/cvpr.2017.765
dblp:conf/cvpr/0002FYSG17
fatcat:dilvj65ayncxviltne44qxnikq