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math.OC2023
Proximal Diagonal Newton Methods for Composite Optimization Problems
Shotaro Yagishita, Shummin Nakayama
This paper proposes new proximal Newton-type methods with a diagonal metric for solving composite optimization problems whose objective function is the sum of a twice continuously…
math.OC2021
Inexact proximal DC Newton-type method for nonconvex composite functions
Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe
We consider a class of difference-of-convex (DC) optimization problems where the objective function is the sum of a smooth function and a possible nonsmooth DC function. The applic…
math.OC2020★ 1 cited
On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression
Shummin Nakayama, Jun-ya Gotoh
This paper conducts a comparative study of proximal gradient methods (PGMs) and proximal DC algorithms (PDCAs) for sparse regression problems which can be cast as Difference-of-two…