5 papers · 1 filter
A proximal difference of convex functions algorithm using Barzilai-Borwein step size with nonmonotone line search and extrapolation
Kelin Wu, Hongpeng Sun
The paper proposes a novel proximal difference-of-convex (DC) algorithmic framework to solve general non-convex, non-smooth optimization problems. By combining Barzilai-Borwein (BB…
A boosted second-order convex splitting algorithm based on gradient flows
Xinhua Shen, Zaijiu Shang, Hongpeng Sun
This paper introduces a second-order convex splitting scheme for gradient flows arising in phase-field models, based on the backward differentiation formula (BDF2) for the implicit…
A preconditioned difference of convex functions algorithm with extrapolation and line search
Ran Zhang, Hongpeng Sun
This paper proposes a novel proximal difference-of-convex (DC) algorithm enhanced with extrapolation and aggressive non-monotone line search for solving non-convex optimization pro…
A preconditioned second-order convex splitting algorithm with extrapolation
Xinhua Shen, Hongpeng Sun
Nonconvex optimization problems are widespread in modern machine learning and data science. We introduce an extrapolation strategy into a class of preconditioned second-order conve…
A preconditioned third-order implicit-explicit algorithm with a difference of varying convex functions and extrapolation
Kelin Wu, Hongpeng Sun
This paper proposes a novel preconditioned implicit-explicit algorithm enhanced with the extrapolation technique for non-convex optimization problems. The algorithm employs a third…