scholarly journals AnO(n2log⁡m)-time algorithm for the boxed-mesh permutation pattern matching problem

2018 ◽  
Vol 710 ◽  
pp. 35-43
Author(s):  
Sukhyeun Cho ◽  
Joong Chae Na ◽  
Jeong Seop Sim
Keyword(s):  
Pattern Matching ◽  
Time Algorithm ◽  
Matching Problem ◽  
Permutation Pattern

scholarly journals Permutation Pattern matching in (213, 231)-avoiding permutations

2017 ◽  
Vol Vol. 18 no. 2, Permutation... (Permutation Patterns) ◽  
Author(s):  
Both Neou ◽  
Romeo Rizzi ◽  
Stéphane Vialette
Keyword(s):  
Pattern Matching ◽  
Related Problem ◽  
Linear Time ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Matching Problem ◽  
Permutation Pattern ◽  
Special Case ◽  
Longest Subsequence

Given permutations σ of size k and π of size n with k < n, the permutation pattern matching problem is to decide whether σ occurs in π as an order-isomorphic subsequence. We give a linear-time algorithm in case both π and σ avoid the two size-3 permutations 213 and 231. For the special case where only σ avoids 213 and 231, we present a O(max(kn 2 , n 2 log log n)-time algorithm. We extend our research to bivincular patterns that avoid 213 and 231 and present a O(kn 4)-time algorithm. Finally we look at the related problem of the longest subsequence which avoids 213 and 231.

A fast expected time algorithm for the 2-D point pattern matching problem

2004 ◽  
Vol 37 (8) ◽  
pp. 1699-1711 ◽  
Author(s):  
P.B. Van Wamelen ◽  
Z. Li ◽  
S.S. Iyengar
Keyword(s):  
Pattern Matching ◽  
Time Algorithm ◽  
Point Pattern ◽  
Matching Problem ◽  
Point Pattern Matching ◽  
Expected Time

Parallel Algorithms for the Boxed-Mesh Permutation Pattern Matching Problem

2019 ◽  
Vol 46 (4) ◽  
pp. 299-307
Author(s):  
Jihyo Choi ◽  
Youngho Kim ◽  
Joong Chae Na ◽  
Jeong Seop Sim
Keyword(s):  
Parallel Algorithms ◽  
Pattern Matching ◽  
Matching Problem ◽  
Permutation Pattern

scholarly journals The Complexity of Pattern Matching for $321$-Avoiding and Skew-Merged Permutations

2016 ◽  
Vol Vol. 18 no. 2, Permutation... (Permutation Patterns) ◽  
Author(s):  
Michael H. Albert ◽  
Marie-Louise Lackner ◽  
Martin Lackner ◽  
Vincent Vatter
Keyword(s):  
Pattern Matching ◽  
Polynomial Time ◽  
Matching Problem ◽  
Polynomial Time Algorithms ◽  
Permutation Pattern ◽  
Special Cases ◽  
Np Complete

The Permutation Pattern Matching problem, asking whether a pattern permutation $\pi$ is contained in a permutation $\tau$, is known to be NP-complete. In this paper we present two polynomial time algorithms for special cases. The first algorithm is applicable if both $\pi$ and $\tau$ are $321$-avoiding; the second is applicable if $\pi$ and $\tau$ are skew-merged. Both algorithms have a runtime of $O(kn)$, where $k$ is the length of $\pi$ and $n$ the length of $\tau$.

A linear time algorithm for consecutive permutation pattern matching

2013 ◽  
Vol 113 (12) ◽  
pp. 430-433 ◽  
Author(s):  
M. Kubica ◽  
T. Kulczyński ◽  
J. Radoszewski ◽  
W. Rytter ◽  
T. Waleń
Keyword(s):  
Pattern Matching ◽  
Linear Time ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Permutation Pattern

A new algorithm for the small-field astrometric point-pattern matching problem

2018 ◽  
Vol 72 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Cláudio P. Santiago ◽  
Carlile Lavor ◽  
Sérgio Assunção Monteiro ◽  
Alberto Kroner-Martins
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Small Field ◽  
Matching Problem ◽  
Point Pattern Matching

scholarly journals Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

2010 ◽  
Vol 20 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Ewa Świercz
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Pattern Matching ◽  
Classification Algorithm ◽  
Statistical Distribution ◽  
Classification Rule ◽  
Matching Problem ◽  
Time Frequency ◽  
Stationary Signals ◽  
Non Stationary Signal

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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