Novak E. — Deterministic and Stochastic Error Bounds in Numerical Analysis :: Электронная библиотека попечительского совета мехмата МГУ

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Novak E. — Deterministic and Stochastic Error Bounds in Numerical Analysis

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Название: Deterministic and Stochastic Error Bounds in Numerical Analysis

Автор: Novak E.

Язык:

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1988

Количество страниц: 113

Добавлена в каталог: 12.05.2008

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Предметный указатель

$\epsilon$ -approximation      1.1.2
Adaption constant of a normed space      1.3.1
Adaptive method, adaptive information      1.1.3
Approximation of 5      1.1.1
Approximation which uses a certain information      1.1.2
Center of a measure      A.4
Center of a set      A.3
Central algorithm      3.2.2 A.3
Coapproximation, best      A.4
complexity      1.1.7
Concentration of measure phenomenon      3.1.4
Covering constant of a metric space      1.3.6
Diameter of a set      1.3.1
Differential equations      1.1.5
Doubling condition      3.1.5
Equidistribution on a compact metric space      3.1.6
Equivalence (strong and weak) of sequences      1.3.6 1.3.9
Errors (and error bounds), average a posteriori error      3.2.2 3.2.7
Errors (and error bounds), average error of a deterministic method      2.1.9 3.1.2 A.5
Errors (and error bounds), average error of a Monte Carlo method      3.1.3
Errors (and error bounds), dispersion of a Monte Carlo method      2.1.2
Errors (and error bounds), error function      1.1.1
Errors (and error bounds), error of a Monte Carlo method      2.1.2
Errors (and error bounds), local error      3.2.1
Errors (and error bounds), maximal error of a deterministic method      1.1.1 1.1.3
Gauss quadrature      A.2
Holder classes of functions      1.3.9
Homogeneous measure on a compact metric space      3.1.5
ill-posed problems      1.1.6
Information, information operator      1.1.2 1.1.3

Information-based complexity      1.1.7
Integral equations      1.1.5
Jung constant of a normed space      1.3.1
Linear problems      1.3.1
Lipschitz classes of functions      1.3.6
Lipschitz problem      1.2.4
Measurable algorithm      A.6
Metric dimension      3.1.5
Monte Carlo method (generalized and restricted)      2.1.2 2.1.4
Nonadaptive method, nonadaptive information      1.1.3
Optimal algorithm (average case)      A.5
Optimal algorithm (worst case)      A.3
Optimal quadrature formulas      1.3.12 A.2
Optimal recovery      1.1.7
Packing constant of a metric space      1.3.7
Problem 5      1.1.1
Problem App      1.1.4
Problem Int      1.1.4
Problems Opt and Opt*      1.1.4
Quasi Monte Carlo methods      2.1.6
Radius, average radius of an information      3.2.2
Radius, Chebyshev radius of a set      1.3.1 A.3
Radius, maximal radius of an information      1.1.2
Radius, p-average radius of an information      A.5
Radius, p-radius of a measure      A.4
Random, random number      2.1.3 2.1.5
Selection theorem      A.6
Sobolev classes of functions      1.3.10 1.3.11
Solvable problem      1.2.4
Varying cardinality      2.1.10 3.1.10
Widths in B(X), linear widths in B(X)      1.2.1 1.2.6

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