Query：
学者姓名：林蓝玉
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
The Fantope-constrained sparse principal subspace estimation problem is initially proposed Vu et al. (Vu et al., 2013). This paper investigates a semismooth Newton based proximal point (P PASSN ) algorithm for solving the equivalent form of this problem, where a semismooth Newton (S SN ) method is utilized to optimize the inner problems involved in the P PASSN algorithm. Under standard conditions, the P PASSN algorithm is proven to achieve global convergence and asymptotic superlinear convergence rate. Computationally, we derive nontrivial expressions the Fantope projection and its generalized Jacobian, which are key ingredients for the P PASSN algorithm. Some numerical results on synthetic and real data sets are presented to illustrate the effectiveness of the proposed P PASSN algorithm for large-scale problems and superiority over the alternating direction method of multipliers (ADMM).
Keyword ：
Fantope projection Fantope projection Generalized Jacobian Generalized Jacobian Proximal point algorithm Proximal point algorithm Semismooth Newton algorithm Semismooth Newton algorithm
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Yong-Jin , Wan, Yuqi , Lin, Lanyu . An efficient algorithm for Fantope-constrained sparse principal subspace estimation problem [J]. | APPLIED MATHEMATICS AND COMPUTATION , 2024 , 475 . |
| MLA | Liu, Yong-Jin 等. "An efficient algorithm for Fantope-constrained sparse principal subspace estimation problem" . | APPLIED MATHEMATICS AND COMPUTATION 475 (2024) . |
| APA | Liu, Yong-Jin , Wan, Yuqi , Lin, Lanyu . An efficient algorithm for Fantope-constrained sparse principal subspace estimation problem . | APPLIED MATHEMATICS AND COMPUTATION , 2024 , 475 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
