Work

Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization

Year: 2023

Type: article

Source: Journal of Optimization Theory and Applications

DOI: 10.1007/s10957-023-02183-7

Authors: Yasushi Narushima, Shummin Nakayama, Masashi Takemura, Hiroshi Yabe

Institutions: Keio University, University of Electro-Communications, Lattice Technology Co., Ltd. (Japan), Tokyo University of Science, Center for Data Science, Tokyo University of Science

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: closed

Funder: Japan Society for the Promotion of Science

Awards: 大規模非線形最適化問題に対する数値計算法の理論的研究およびその実装, Analysis of equilibrium problems arising within supply chain networks, Development of sustainable service systems that take into account strategic goal mitigation and simplification, Development and deepening of nonlinear optimization in machine learning, Study on algorithms of numerical methods for large scale nonlinear optimization problems and their implementation

OpenAlex ID: W4353044672

FWCI: 1.527

Cites: 50

Cited by: 8

Related to: 10