We present two block variants of the discrete empirical interpolation method (DEIM); as a particular application, we will consider a CUR factorization. The block DEIM algorithms are based on the rank-revealing QR factorization and the concept of the maximum volume of submatrices. We also present a version of the block DEIM procedures, which allows for adaptive choice of block size. Experiments demonstrate that the block DEIM algorithms may provide a better low-rank approximation, and may also be computationally more efficient than the standard DEIM procedure
We propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to find k largest ...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
We introduce a parallel algorithm for computing the low rank approximation $A_k$ of a large matrix $...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
We derive a CUR approximate matrix factorization based on the discrete empirical interpolation metho...
The discrete empirical interpolation method (DEIM) may be used as an index selection strategy for fo...
We propose a generalized CUR (GCUR) decomposition for matrix pairs (A,B). Given matrices A and B wit...
Low-rank approximations which are computed from selected rows and columns of a given data matrix hav...
In this paper, we introduce a new column selection strategy, named here “Deviation Maximization”, an...
In this paper we introduce a new column selection strategy, named here ``Deviation Maximization", an...
In this paper we introduce a new column selection strategy, named here ``Deviation Maximization", an...
This paper presents a new approach to construct more efficient reduced-order models for nonlinear pa...
We propose a restricted SVD based CUR (RSVD-CUR) decomposition for matrix triplets $(A, B, G)$. Give...
This article describes a suite of codes as well as associated testing and timing drivers for computi...
The CUR matrix decomposition and the Nyström approximation are two important low-rank matrix approx...
We propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to find k largest ...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
We introduce a parallel algorithm for computing the low rank approximation $A_k$ of a large matrix $...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
We derive a CUR approximate matrix factorization based on the discrete empirical interpolation metho...
The discrete empirical interpolation method (DEIM) may be used as an index selection strategy for fo...
We propose a generalized CUR (GCUR) decomposition for matrix pairs (A,B). Given matrices A and B wit...
Low-rank approximations which are computed from selected rows and columns of a given data matrix hav...
In this paper, we introduce a new column selection strategy, named here “Deviation Maximization”, an...
In this paper we introduce a new column selection strategy, named here ``Deviation Maximization", an...
In this paper we introduce a new column selection strategy, named here ``Deviation Maximization", an...
This paper presents a new approach to construct more efficient reduced-order models for nonlinear pa...
We propose a restricted SVD based CUR (RSVD-CUR) decomposition for matrix triplets $(A, B, G)$. Give...
This article describes a suite of codes as well as associated testing and timing drivers for computi...
The CUR matrix decomposition and the Nyström approximation are two important low-rank matrix approx...
We propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to find k largest ...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
We introduce a parallel algorithm for computing the low rank approximation $A_k$ of a large matrix $...