Average-Case Analysis of Numerical Problems
Average-Case Analysis of Numerical Problems [electronic resource] /
edited by Klaus Ritter.
- XI, 252 p. online resource.
- Lecture Notes in Mathematics, 1733 0075-8434 ; .
- Lecture Notes in Mathematics, 1733 .
Linear problems: Definitions and a classical example -- Second-order results for linear problems -- Integration and approximation of univariate functions -- Linear problems for univariate functions with noisy data -- Integration and approximation of multivariate functions -- Nonlinear methods for linear problems -- Nonlinear problems.
The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided.
9783540455929
10.1007/BFb0103934 doi
Numerical analysis.
Statistics.
Numerical Analysis.
Statistics, general.
QA297-299.4
518
Linear problems: Definitions and a classical example -- Second-order results for linear problems -- Integration and approximation of univariate functions -- Linear problems for univariate functions with noisy data -- Integration and approximation of multivariate functions -- Nonlinear methods for linear problems -- Nonlinear problems.
The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided.
9783540455929
10.1007/BFb0103934 doi
Numerical analysis.
Statistics.
Numerical Analysis.
Statistics, general.
QA297-299.4
518