Add to ORKGWhen working on multidimensional problems, the number of points needed when using a tensor product grid. This is known as the curse of dimensionality. In this thesis we propose a set of sparse grids, which can be used to dampen this curse. The grid is chosen such that we can apply the Fourier transform on functions sampeled on it. The thesis describes such an algorithm.MAMN-MABMAB39
Computing the dominant Fourier coefficients of a vector is a common task in many fields, such as sig...
Mit Hilfe von Dünnen Gittern können Funktionen in mehreren Veränderlichen mit einer Anzahl von Freih...
Trigonometric transforms like the Fourier transform or the discrete cosine transform (DCT) are of im...
When working on multidimensional problems, the number of points needed when using a tensor product g...
The technique of sparse grids allows to overcome the curse of dimensionality, which prevents the use...
In the recent decade, there has been growing interest in the numerical treatment of high-dimensional...
In real-world applications mathematical models often involve more than one variable. For example, a...
Abstract. Approximation problems in high dimensions arise in numerous applications such as problems ...
In this paper we describe methods to approximate functions and differential operators on adaptive sp...
In the sparse recovery problem, we have a signal x in R^N that is sparse; i.e., it consists of k sig...
Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the...
AbstractSparse grids allow one to employ grid-based discretization methods in data-driven problems. ...
The discrete Fourier transform (DFT) is a well-known transform with many applications in various fie...
For the approximation of multidimensional functions, using classical numerical discretization scheme...
We show that the logarithmic factor in the standard error estimate for sparse finite element (FE) sp...
Computing the dominant Fourier coefficients of a vector is a common task in many fields, such as sig...
Mit Hilfe von Dünnen Gittern können Funktionen in mehreren Veränderlichen mit einer Anzahl von Freih...
Trigonometric transforms like the Fourier transform or the discrete cosine transform (DCT) are of im...
When working on multidimensional problems, the number of points needed when using a tensor product g...
The technique of sparse grids allows to overcome the curse of dimensionality, which prevents the use...
In the recent decade, there has been growing interest in the numerical treatment of high-dimensional...
In real-world applications mathematical models often involve more than one variable. For example, a...
Abstract. Approximation problems in high dimensions arise in numerous applications such as problems ...
In this paper we describe methods to approximate functions and differential operators on adaptive sp...
In the sparse recovery problem, we have a signal x in R^N that is sparse; i.e., it consists of k sig...
Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the...
AbstractSparse grids allow one to employ grid-based discretization methods in data-driven problems. ...
The discrete Fourier transform (DFT) is a well-known transform with many applications in various fie...
For the approximation of multidimensional functions, using classical numerical discretization scheme...
We show that the logarithmic factor in the standard error estimate for sparse finite element (FE) sp...
Computing the dominant Fourier coefficients of a vector is a common task in many fields, such as sig...
Mit Hilfe von Dünnen Gittern können Funktionen in mehreren Veränderlichen mit einer Anzahl von Freih...
Trigonometric transforms like the Fourier transform or the discrete cosine transform (DCT) are of im...