AN EFFICIENT ALGORITHM FOR THE CLOSEST STRING PROBLEM

2020 ◽  
pp. 10-22
Author(s):  
Omar Latorre Vilca ◽  
Mário Salvatierra Júnior
Keyword(s):  
Efficient Algorithm ◽  
String Problem

scholarly journals An efficient algorithm for the Closest String Problem

2018 ◽  
Author(s):  
Omar Vilca ◽  
Rosiane De Freitas
Keyword(s):  
Efficient Algorithm ◽  
Coding Theory ◽  
Numerical Experiments ◽  
Hamming Distance ◽  
Linear Time ◽  
Optimal Solution ◽  
Formal Proof ◽  
Computational Molecular Biology ◽  
String Problem ◽  
Np Hard Problem

The closest string problem that arises in computational molecular biology and coding theory is to find a string that minimizes the maximum Hamming distance from a given set of strings, the CSP is NP-hard problem. This article proposes an efficient algorithm for this problem with three strings. The key idea is to apply normalization for the CSP instance. This enables us to decompose the problem in five different cases corresponding to each position of the strings. Furthermore, an optimal solution can be easily obtained in linear time. A formal proof of the algorithm will be presented, also numerical experiments will show the effectiveness of the proposed algorithm.  

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.

Moving to Solution

2013 ◽  
Vol 60 (6) ◽  
pp. 403-409 ◽  
Author(s):  
K. Werner ◽  
M. Raab
Keyword(s):  
Problem Solving ◽  
Embodied Cognition ◽  
Cognitive Functions ◽  
Problem Space ◽  
Arm Swing ◽  
String Problem ◽  
Problem Solving Process ◽  
Problem Solving Task ◽  
Bodily Movements

Embodied cognition theories suggest a link between bodily movements and cognitive functions. Given such a link, it is assumed that movement influences the two main stages of problem solving: creating a problem space and creating solutions. This study explores how specific the link between bodily movements and the problem-solving process is. Seventy-two participants were tested with variations of the two-string problem (Experiment 1) and the water-jar problem (Experiment 2), allowing for two possible solutions. In Experiment 1 participants were primed with arm-swing movements (swing group) and step movements on a chair (step group). In Experiment 2 participants sat in front of three jars with glass marbles and had to sort these marbles from the outer jars to the middle one (plus group) or vice versa (minus group). Results showed more swing-like solutions in the swing group and more step-like solutions in the step group, and more addition solutions in the plus group and more subtraction solutions in the minus group. This specificity of the connection between movement and problem-solving task will allow further experiments to investigate how bodily movements influence the stages of problem solving.

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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