Demo Data from EDBT 2026 / reviewers in the wild / expert
Lars-Magnus Ewerbring
dblp:11/4060
· DBLP profile ↗
5ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 87% Parallel and multicore computing · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing
parallel numerical algorithms |
0.0 | 1 | 1990 | Computing the Singular Value Decomposition on the Connection Machine · IEEE Trans. Computers 1990 |
High-performance computing › numerical linear algebra
singular value decomposition |
0.0 | 1 | 1990 | Computing the Singular Value Decomposition on the Connection Machine · IEEE Trans. Computers 1990 |
Parallel and multicore computing › parallel architecture
massively parallel processor |
0.0 | 1 | 1990 | Computing the Singular Value Decomposition on the Connection Machine · IEEE Trans. Computers 1990 |
Methods — techniques the papers use, named apart from their topics
parallel implementation · 0.0Jacobi SVD algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | A coherent detection scheme for the uplink channel in a CDMA systemabstractIn a joint European research effort, code division multiple access is studied as a possible candidate for future mobile systems. In the paper a coherent detection scheme is described which uses only a modest overhead.> Gustav Brismark, Lars-Magnus Ewerbring, Tomas Sandin, Paul Teder |
VTC | 2 |
| 1993 | An Algorithm for the Generalized Singular Value Decomposition on Massively Parallel Computers
Haesun Park, Lars-Magnus Ewerbring |
J. Parallel Distributed Comput. | 2 |
| 1991 | An algorithm for the generalized singular value decomposition on massively parallel computersabstractA new algorithm for computing the generalized singular value decomposition that diagonalizes two matrices is introduced.First, two existing algorithms are studied, one due to Paige and the other to Hari and Veselic.The former requires only orthogonal plane transformations, resulting in two matrices with parallel rows.The latter employs nonsingular (not necessarily orthogonal) transformations to directly diagonalize the two data matrices.For many applications, it is preferable to be given both the diagonal matrices and the transformation matrices explicitly.Our algorithm has the advantages that the nonsingtdar transformation is readily available, computation of transformations is simple, convergence test is efficient in a parallel computing environment, and the condition number of each nonsingular transformation can be controlled.We present implementation results, obtained on a Connection Machine 2, to compare our new algorithm with that of Paige. Haesun Park, Lars-Magnus Ewerbring |
ICS | 2 |
| 1991 | An accurate product SVD algorithmabstractIn this paper, we propose a new algorithm for computing a singular value decomposition of a product of three matrices. We show that our algorithm is numerically desirable in that all relevant residual elements will be numerically small In diesem Beitrag wird ein neuer Algorithmus zur Berechnung der Singulärwertzerlegung des Produktes dreier Matrizen vorgestellt. Es wird gezeigt, daβ der Algorithmus ein gutartiges numerisches Verhalten aufweist, weil alle relevanten Residualgröβen klein werden. Nous proposons dans cet article un nouvel algorithme de calcul de la décomposition en valeurs singulières d'un produit de trois matrices. Nous montrons que notre algorithme est attrayant du point de vue numérique du fait que tous les résidus pertinents sont numériquement petits. Adam W. Bojanczyk, Lars-Magnus Ewerbring, Franklin T. Luk, Paul Van Dooren |
Signal Process. | 2 |
| 1990 | Computing the Singular Value Decomposition on the Connection MachineabstractConsideration is given to the computation of the singular value decomposition (SVD) on the Connection Machine (CM). Brief descriptions of the Lisp language and some typical matrix manipulating functions are given. Implementation details of various Jacobi-SVD algorithms on an 8192-processor CM are presented. For n*n matrices, where n> Lars-Magnus Ewerbring, Franklin T. Luk |
IEEE Trans. Computers | 1 |