AbstractBasic questions of information-based complexity are strongly related to n-widths and s-numbers. In this paper we study Monte Carlo methods or randomized methods for linear operators. Similar as in the worst case, Mathé defined linear stochastic n-widths. Our main result is the characterization of these widths in the case of linear operators in Hilbert spaces
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
AbstractWe shall study maximal errors of approximating linear problems. As possible classes of infor...
Abstract In this paper, we present selected old and new results on the optimal solution of linear pr...
AbstractBasic questions of information-based complexity are strongly related to n-widths and s-numbe...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
This note deals with a problem of the probabilistic Ramsey theory in functional analysis. G...
We study the approximation of expectations E(f(X)) for Gaussian random elements X with values in a s...
We study optimal algorithms and optimal information for an average case model. This is done for line...
AbstractWe present general results on the average case complexity of approximating linear operators ...
AbstractThe possibility to approximate bounded linear mappings between Banach spaces depends on the ...
AbstractThis paper studies optimal information and optimal algorithms in Hilbert space for an n-dime...
... systems, Maximum-Likelihood (ML) decoding is equivalent to finding the closest lattice point in ...
AbstractWe study the worst case complexity of solving problems for which information is partial and ...
We intend to find optimal deterministic and randomized algorithms for three related problems: multiv...
The possibility to approximate bounded linear mappings between Banach spaces depends on the degree o...
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
AbstractWe shall study maximal errors of approximating linear problems. As possible classes of infor...
Abstract In this paper, we present selected old and new results on the optimal solution of linear pr...
AbstractBasic questions of information-based complexity are strongly related to n-widths and s-numbe...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
This note deals with a problem of the probabilistic Ramsey theory in functional analysis. G...
We study the approximation of expectations E(f(X)) for Gaussian random elements X with values in a s...
We study optimal algorithms and optimal information for an average case model. This is done for line...
AbstractWe present general results on the average case complexity of approximating linear operators ...
AbstractThe possibility to approximate bounded linear mappings between Banach spaces depends on the ...
AbstractThis paper studies optimal information and optimal algorithms in Hilbert space for an n-dime...
... systems, Maximum-Likelihood (ML) decoding is equivalent to finding the closest lattice point in ...
AbstractWe study the worst case complexity of solving problems for which information is partial and ...
We intend to find optimal deterministic and randomized algorithms for three related problems: multiv...
The possibility to approximate bounded linear mappings between Banach spaces depends on the degree o...
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
AbstractWe shall study maximal errors of approximating linear problems. As possible classes of infor...
Abstract In this paper, we present selected old and new results on the optimal solution of linear pr...
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