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SPLITTING ALGORITHMS FOR NONCONVEX OPTIMIZATION: UNIFIED ANALYSIS AND NEWTON-TYPE ACCELERATION

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概要 We provide a unified interpretation of splitting algorithms for nonconvex optimization through the lens of majorization-minimization. Possibly under assumptions to compensate the lack of convexity, th...is setting is general enough to cover ADMM as well as forward-backward, Douglas-Rachford and Davis-Yin splittings. Proximal envelopes, a generalization of the Moreau envelope, are shown to be natural merit functions for establishing convergence results. Their regularity properties also enable the integration of fast direction of quasi-Newton-type, that differently from any other approach for nonsmooth optimization preserve the same operation complexity of the original splitting scheme.続きを見る
目次 Introduction
 Convex splitting algorithms
 Nonconvexity?
 Goals
Algorithmic design
 The majorization-minimization principle
 Generalized proximal MM algorithms
A unified convergence analysis
 Envelope functions
Notable examples
  DRS
  ADMM
  DYS
  (Proximal ADMM)
  (Chambolle-Pock)
“Acceleration”
 Challenges of higher-order methods
 The Continuous-Lyapunov Descent framework
 Simulations
Conclusions
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登録日 2023.06.16
更新日 2023.06.16