Convergence analysis for proximal gradient-type methods with arbitrary proximal terms in nonconvex composite optimization, without requiring the global descent property between the smooth function and its proximal mapping.
Difference-of-convex learning: directional stationarity, opti- mality, and sparsity.SIAM Journal on Optimization, 27(3):1637–1665
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Proximal gradient-type method with generalized distance and convergence analysis without global descent lemma
Convergence analysis for proximal gradient-type methods with arbitrary proximal terms in nonconvex composite optimization, without requiring the global descent property between the smooth function and its proximal mapping.