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.
The trimmed lasso: Sparse recovery guarantees and practical optimization by the generalized soft-min penalty.SIAM Journal on Mathematics of Data Science, 3(3): 900–929, 2021
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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.