Demo Data from VLDB 2026 / reviewers in the wild / expert
L. Duane Pyle
dblp:38/2336
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1964
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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.
| Theoretical computer science
1 paper |
Mathematical optimization · 50% Algorithms and data structures · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › numerical linear algebra
generalized inverse |
0.0 | 1 | 1964 | Generalized Inverse Computations Using the Gradient Projection Method · J. ACM 1964 |
Mathematical optimization
linear programming |
0.0 | 1 | 1964 | Generalized Inverse Computations Using the Gradient Projection Method · J. ACM 1964 |
Methods — techniques the papers use, named apart from their topics
gram-schmidt orthogonalization · 0.0gradient projection · 0.0conjugate directions · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1964 | Generalized Inverse Computations Using the Gradient Projection MethodabstractA number of methods have been proposed for computing the (Moore-Penrose-Bjerhammar) generalized inverse A + of an arbitrary m X n complex matrix A of rank r < m _~ n.Boot, Ben~Israel and Wersan have recently published methods which require the formation either of AA* or BB*, where B is an r X n submatrix of A having rank r.In this paper a procedure for computing A + is given which consists of a variant of the gradient projection method.The procedure, which is equivalent to a Hestenes conjugate directions method in the special case r = m, may be applied to any complex matrix.The basic procedure requires the application of the Gram-Schmidt orthogonalization process, first to the column vectors of A*, then, if A is not of full row rank, to the column vectors of A. A computer program (FoRTRXN IV for the IBM 7090) using the method has been tested and used in connection with the generalized inverse-eigenvector method for solving linear programruing problems. L. Duane Pyle |
J. ACM | 1 |