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Memoryless quasi-Newton methods based on spectral-scaling Broyden family for unconstrained optimization
- Source :
- Journal of Industrial & Management Optimization. 15:1773-1793
- Publication Year :
- 2019
- Publisher :
- American Institute of Mathematical Sciences (AIMS), 2019.
-
Abstract
- Memoryless quasi-Newton methods are studied for solving large-scale unconstrained optimization problems. Recently, memoryless quasi-Newton methods based on several kinds of updating formulas were proposed. Since the methods closely related to the conjugate gradient method, the methods are promising. In this paper, we propose a memoryless quasi-Newton method based on the Broyden family with the spectral-scaling secant condition. We focus on the convex and preconvex classes of the Broyden family, and we show that the proposed method satisfies the sufficient descent condition and converges globally. Finally, some numerical experiments are given.
- Subjects :
- 0209 industrial biotechnology
021103 operations research
Control and Optimization
Computer science
Applied Mathematics
Strategy and Management
0211 other engineering and technologies
Regular polygon
02 engineering and technology
Unconstrained optimization
Atomic and Molecular Physics, and Optics
020901 industrial engineering & automation
Conjugate gradient method
Applied mathematics
Business and International Management
Electrical and Electronic Engineering
Focus (optics)
Scaling
Descent (mathematics)
Subjects
Details
- ISSN :
- 1553166X
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Journal of Industrial & Management Optimization
- Accession number :
- edsair.doi...........d9724c8d85eb46fabeb0ecb37f472741