scholarly journals Time evolution of an infinite projected entangled pair state: An efficient algorithm

2019 ◽  
Vol 99 (3) ◽  
Author(s):  
Piotr Czarnik ◽  
Jacek Dziarmaga ◽  
Philippe Corboz
Keyword(s):  
Time Evolution ◽  
Efficient Algorithm ◽  
Pair State

EQUIVALENCE BETWEEN THE FLR TIME DEPENDENT PERCOLATION MODEL AND THE NEWMAN–ZIFF ALGORITHM

2000 ◽  
Vol 11 (08) ◽  
pp. 1581-1584 ◽  
Author(s):  
JOAQUIM E. de FREITAS ◽  
LIACIR S. LUCENA
Keyword(s):  
Time Evolution ◽  
Efficient Algorithm ◽  
Critical Value ◽  
Percolation Model ◽  
Time Dependent ◽  
Occupation Probability ◽  
Cumulative Distribution ◽  
Percolation Clusters ◽  
Threshold Distribution ◽  
Dependent Percolation

We compare the results of a very efficient algorithm that we have proposed to study the time evolution of percolation clusters when the occupation probability swept through the critical value in the same sample and in a single run with another algorithm proposed by Newman and Ziff to allow fast calculations of the standard percolation model. Both have a complexity per site that is roughly independent of the size of the system. Our results show that for the derivative threshold distribution, the results ( exponent = 1.8 ± 0.2) are closer to Wester while for the cumulative distribution ( exponent = 1.5 ± 0.1), they are closer to Newman and Ziff.

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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