optimization
对角缩放无记忆对称秩一方法
Diagonally scaled memoryless symmetric rank-one methodsA class of optimization algorithms that utilize a diagonally scaled memoryless symmetric rank-one update to approximate the inverse Hessian matrix, improving convergence properties for specific problem structures. / 一类利用对角缩放无记忆对称秩一更新来近似逆Hessian矩阵的优化算法，针对特定问题结构改善了收敛性质。
- Knowledge type
- 数学理论
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-10-23 17:00:42
Sub-knowledge points
Convergence analysisApplication to non-linear systems
Higher-order knowledge points
Quasi-Newton methodsOptimization theory
Main parameters and performance indicators
Diagonal scaling matrix: Used to precondition the optimization problemMemoryless update: The algorithm does not store past information, relying only on the current stepSymmetric rank-one update: A method to update the inverse Hessian approximation
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsDiagonally scaled memoryless symmetric rank-one methods with application to compressed sensingOpen source
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1 recordsRecord title: Diagonally scaled memoryless symmetric rank-one methods.
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