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Numerical Methods for Very Large Sparse Dynamical Systems

Numerical Methods for Very Large Sparse Dynamical Systems
超大型稀疏动力系统的数值方法
批准号:
9405380
负责人:
Daniel Boley
金额:
$18.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1994
资助国家:
美国
项目状态:
已结题
起止时间:
1994-09-15 至 1998-08-31

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中文摘要
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英文摘要
9405380 Boley This project will develop and experiment with a variety of core numerical algorithms for large sparse or structured linear algebra problems. Part of the research is the development of effective algorithms for the analysis and design of very large control models such as those related to large flexible space structures. To accomplish this, a major focus of this research will be the design of effective and efficient linear algebra algorithms. Examples of problems to be addressed are large Lyapunov equations, large order matrix pencils, large eigenvalue and singular values solvers. Parallel algorithms will be explored and exploited. Approximate solutions obtained via Krylov space methods, or other methods, will be analyzed. A standard sparse matrix library (SPARSKIT) will be used as the foundation for the software development. To evaluate the effectiveness of the algorithms, the quality of controller designs obtained from the approximate solutions will be explored. ***
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REU Site: Computational Methods for Discovery Driven by Big Data
  • 批准号:
    1460620
  • 项目类别:
    Standard Grant
  • 资助金额:
    $36.0万
  • 财政年份:
    2015
  • 负责人:
    Daniel Boley
  • 依托单位:
III: Small: Effective Convex Solvers for Machine Learning
  • 批准号:
    1319749
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $43.75万
  • 财政年份:
    2013
  • 负责人:
    Daniel Boley
  • 依托单位:
Effective Learning by Leveraging Supervised and Unsupervised Techniques
  • 批准号:
    0534286
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2005
  • 负责人:
    Daniel Boley
  • 依托单位:
General Purpose Methods for Unsupervised Exploration of Large Datasets
  • 批准号:
    0208621
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $15.0万
  • 财政年份:
    2002
  • 负责人:
    Daniel Boley
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data