An Efficient Algorithm for Computing Parametric Multivariate Polynomial GCD

2018 ◽  
Author(s):  
Deepak Kapur ◽  
Dong Lu ◽  
Michael Monagan ◽  
Yao Sun ◽  
Dingkang Wang
Keyword(s):  
Efficient Algorithm ◽  
Multivariate Polynomial ◽  
Polynomial Gcd

scholarly journals Three new algorithms for multivariate polynomial GCD

1992 ◽  
Vol 13 (4) ◽  
pp. 395-411 ◽  
Author(s):  
Tateaki Sasaki ◽  
Masayuki Suzuki
Keyword(s):  
Multivariate Polynomial ◽  
Polynomial Gcd ◽  
New Algorithms

scholarly journals Polynomial GCD Derived through Monic Polynomial Subtractions

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Feng Cheng Chang
Keyword(s):  
Efficient Algorithm ◽  
Monic Polynomial ◽  
Univariate Polynomials ◽  
Polynomial Gcd ◽  
High Degree ◽  
Very High

A simple and efficient algorithm for finding the GCD of a pair of univariate polynomials is derived. The process requires only successive subtraction of two monic polynomials. Amazingly, this approach gives the desired GCD for a pair of test polynomials of very high degree, such as and its derivative.

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Energy efficient algorithm for lookup-tables on mobile devices

2016 ◽  
Vol 2016 (7) ◽  
pp. 1-6
Author(s):  
Sergey Makov ◽  
Vladimir Frantc ◽  
Viacheslav Voronin ◽  
Igor Shrayfel ◽  
Vadim Dubovskov ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Efficient Algorithm ◽  
Energy Efficient ◽  
Lookup Tables
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