Inverting a Vandermonde matrix in minimum parallel time

1991 ◽  
Vol 38 (6) ◽  
pp. 291-294 ◽  
Author(s):  
Franco P. Preparata
Keyword(s):  
Vandermonde Matrix ◽  
Parallel Time

Highly parallel time integration of viscoelastic flows

2001 ◽  
Vol 100 (1-3) ◽  
pp. 191-216 ◽  
Author(s):  
A.E. Caola ◽  
Y.L. Joo ◽  
R.C. Armstrong ◽  
R.A. Brown
Keyword(s):  
Time Integration ◽  
Viscoelastic Flows ◽  
Parallel Time

Evaluating uniform expressions within two steps of minimum parallel time

1997 ◽  
Vol 44 (2) ◽  
pp. 345-361
Author(s):  
Robert A. Wagner
Keyword(s):  
Parallel Time

CALCULATING RECURRENCES USING THE BIRD-MEERTENS FORMALISM

1995 ◽  
Vol 05 (02) ◽  
pp. 179-190 ◽  
Author(s):  
WENTONG CAI ◽  
DAVID B. SKILLICORN
Keyword(s):  
Software Development ◽  
Parallel Algorithms ◽  
Time Complexity ◽  
Large Range ◽  
Parallel Solution ◽  
Parallel Time

The Bird-Meetens formalism is an approach to software development and computation based on datatype theories. In this paper we build new operators for the theory of lists that compute generalized recurrences and show that they have logarithmic parallel time complexity. As many applications can be cast as forms of recurrences, this allows a large range of parallel algorithms to be derived within the Bird-Meertens formalism. We illustrate by deriving a parallel solution to the maximum segment sum problem.

scholarly journals Parallel time courses of acetylcholine-induced releases of catecholamine and enkephalin-like immunoreactive peptides from perfused dog adrenal glands

1983 ◽  
Vol 4 (4) ◽  
pp. 413-416 ◽  
Author(s):  
NAOTO ASADA ◽  
TOSHIYUKI SAITO ◽  
TOMIO KANNO ◽  
KAZUWA NAKAO ◽  
TAKAAKI YOSHIMASA ◽  
...  
Keyword(s):  
Parallel Time ◽  
Time Courses

scholarly journals Learning Coefficient of Vandermonde Matrix-Type Singularities in Model Selection

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 561
Author(s):  
Miki Aoyagi
Keyword(s):  
Model Selection ◽  
Information Criteria ◽  
Learning Systems ◽  
Vandermonde Matrix ◽  
Selection Methods ◽  
Learning Models ◽  
Matrix Type ◽  
Log Canonical Threshold ◽  
Log Canonical ◽  
Blowing Up

In recent years, selecting appropriate learning models has become more important with the increased need to analyze learning systems, and many model selection methods have been developed. The learning coefficient in Bayesian estimation, which serves to measure the learning efficiency in singular learning models, has an important role in several information criteria. The learning coefficient in regular models is known as the dimension of the parameter space over two, while that in singular models is smaller and varies in learning models. The learning coefficient is known mathematically as the log canonical threshold. In this paper, we provide a new rational blowing-up method for obtaining these coefficients. In the application to Vandermonde matrix-type singularities, we show the efficiency of such methods.

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