A Heuristic Algorithm on Solving the Great Group Dividing of Figure

2012 ◽  
Vol 239-240 ◽  
pp. 1557-1560
Author(s):  
Hai Yan Zhou ◽  
Li Ping Wen
Keyword(s):  
Heuristic Algorithm ◽  
Time Complexity ◽  
Difficult Problem ◽  
Minimax Group ◽  
Maximum Time ◽  
Simple Figure ◽  
Time Price ◽  
Low Efficiency ◽  
Index Level ◽  
Complexity Of Algorithm

The problem of the great group of a figure is the famous NP-difficult problem. There exists an algorithm of solving the great group of figure or only applying to some of the special figure .There need time price is index level, and is low efficiency. It puts forward a kind of solving the minimax group partition algorithm with the most magnanimous nodes for elicitation information. This algorithm can be applied to any simple figure, and the maximum time complexity of algorithm is O(sn3).

scholarly journals Metric Embedding Learning on Multi-Directional Projections

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 133 ◽  
Author(s):  
Gábor Kertész
Keyword(s):  
Feature Extraction ◽  
High Performance ◽  
Classification Performance ◽  
Difficult Problem ◽  
Input Image ◽  
Image Size ◽  
Matching Problems ◽  
Low Efficiency ◽  
Multi Class Classification ◽  
Directional Image

Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision—mostly driven by deep learning—have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same; nature-inspired multi-layered convolutional nets learn the representations, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters.

scholarly journals On Rectilinear Distance-Preserving Trees

VLSI Design ◽  
1998 ◽  
Vol 7 (1) ◽  
pp. 15-30
Author(s):  
Gustavo E. Téllez ◽  
Majid Sarrafzadeh
Keyword(s):  
Approximation Algorithm ◽  
Heuristic Algorithm ◽  
Time Complexity ◽  
High Performance ◽  
Exact Algorithm ◽  
Wire Length ◽  
Worst Case ◽  
Rectilinear Distance ◽  
Source To Sink ◽  
Linear Delay

Given a set of terminals on the plane N={s,ν1,…,νn}, with a source terminal s, a Rectilinear Distance-Preserving Tree (RDPT) T(V, E) is defined as a tree rooted at s, connecting all terminals in N. An RDPT has the property that the length of every source to sink path is equal to the rectilinear distance between that source and sink. A Min- Cost Rectilinear Distance-Preserving Tree (MRDPT) minimizes the total wire length while maintaining minimal source to sink linear delay, making it suitable for high performance interconnect applications.This paper studies problems in the construction of RDPTs, including the following contributions. A new exact algorithm for a restricted version of the problem in one quadrant with O(n2) time complexity is proposed. A novel heuristic algorithm, which uses optimally solvable sub-problems, is proposed for the problem in a single quadrant. The average and worst-case time complexity for the proposed heuristic algorithm are O(n3/2) and O(n3), respectively. A 2-approximation of the quadrant merging problem is proposed. The proposed algorithm has time complexity O(α2T(n)+α3) for any constant α > 1, where T(n) is the time complexity of the solution of the RDPT problem on one quadrant. This result improves over the best previous quadrant merging solution which has O(n2T(n)+n3) time complexity.We test our algorithms on randomly uniform point sets and compare our heuristic RDPT construction against a Minimum Cost Rectilinear Steiner (MRST) tree approximation algorithm. Our results show that RDPTs are competitive with Steiner trees in total wire-length when the number of terminals is less than 32. This result makes RDPTs suitable for VLSI routing applications. We also compare our algorithm to the Rao-Shor RDPT approximation algorithm obtaining improvements of up to 10% in total wirelength. These comparisons show that the algorithms proposed herein produce promising results.

A Heterogeneity Based Greedy Algorithm for Scheduling Out-Tree Task Graphs

2013 ◽  
Vol 475-476 ◽  
pp. 972-977
Author(s):  
Jian Jun Zhang ◽  
Yong Qu ◽  
Dan Mei
Keyword(s):  
Greedy Algorithm ◽  
Time Complexity ◽  
Critical Factors ◽  
Task Graphs ◽  
Parallel Languages ◽  
Schedule Length ◽  
Task Duplication ◽  
Schedule Performance ◽  
Low Efficiency ◽  
And Task

The scheduling of Out-Tree task graphs is one of the critical factors in implementing the compilers of parallel languages and improving the performance of parallel computing. When applied to Out-Tree task graphs, many previous classical heterogeneity based algorithms always ignored the economization on processors and the minimization of the schedule length, which led to low efficiency in real applications. This paper proposes a heterogeneity based greedy algorithm for scheduling Out-Tree task graphs, which is based on list and task duplication, tries to find the best point between balancing loads and shortening the schedule length and improves the schedule performance without increasing the time complexity of the algorithm. The comparative experimental results demonstrate that the proposed algorithm could achieve shorter schedule length while using less number of processors.

A Generation Method of Network Security Hardening Strategy Based on Attack Graphs

2015 ◽  
Vol 12 (1) ◽  
pp. 45-61 ◽  
Author(s):  
Chao Zhao ◽  
Huiqiang Wang ◽  
Junyu Lin ◽  
Hongwu Lv ◽  
Yushu Zhang
Keyword(s):  
Network Security ◽  
Heuristic Algorithm ◽  
Scoring System ◽  
Time Complexity ◽  
Path Length ◽  
Low Cost ◽  
Attack Graphs ◽  
Running Time ◽  
Security Hardening ◽  
The Common

Analyzing attack graphs can provide network security hardening strategies for administrators. Concerning the problems of high time complexity and costly hardening strategies in previous methods, a method for generating low cost network security hardening strategies is proposed based on attack graphs. The authors' method assesses risks of attack paths according to path length and the common vulnerability scoring system, limits search scope with a threshold to reduce the time complexity, and lowers cost of hardening strategies by using a heuristic algorithm. The experimental results show that the authors' method has good scalability, and significantly reduces cost of network security hardening strategies with reasonable running time.

SYNTHESIS OF RELATIONAL WEB SERVICES

2010 ◽  
Vol 04 (03) ◽  
pp. 385-417 ◽  
Author(s):  
QI WANG ◽  
PHILLIP C.-Y. SHEU
Keyword(s):  
Web Services ◽  
Web Service ◽  
Time Complexity ◽  
Difficult Problem ◽  
Synthesis Problem ◽  
Complex Queries

Although widely addressed, automatically composing a web service from existing services is still a difficult problem. This paper focuses on the service synthesis problem for relational services. It considers answering complex queries using a set of available relational services collectively with a two step solution. The method guarantees to find a solution if there is one, and its time complexity is polynomial.

scholarly journals Constrained Multiple Sequence Alignment Tool Development and Its Application to RNase Family Alignment

2003 ◽  
Vol 01 (02) ◽  
pp. 267-287 ◽  
Author(s):  
Chuan Yi Tang ◽  
Chin Lung Lu ◽  
Margaret Dah-Tsyr Chang ◽  
Yin-Te Tsai ◽  
Yuh-Ju Sun ◽  
...  
Keyword(s):  
Sequence Alignment ◽  
Heuristic Algorithm ◽  
Multiple Sequence Alignment ◽  
Time Complexity ◽  
Software System ◽  
Tool Development ◽  
Multiple Sequence ◽  
Rna Molecules ◽  
Alignment Tool ◽  
Multiple Sequence Alignment Tool

In this paper, we design a heuristic algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant α, then the time-complexity of our CMSA algorithm for aligning K sequences is O(αKn4), where n is the maximum of the lengths of sequences. In addition, we have built up such a CMSA software system and made several experiments on the RNase sequences, which mainly function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.

A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem

2018 ◽  
Vol 45 (6) ◽  
pp. 794-817 ◽  
Author(s):  
Reham Shawqi Barham ◽  
Ahmad Sharieh ◽  
Azzam Sleit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Heuristic Algorithm ◽  
Time Complexity ◽  
Link Prediction ◽  
Prediction Problem ◽  
Under Sampling

This study presents a solution to a problem commonly known as link prediction problem. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Finding a solution to link prediction problem attracts variety of computer science fields such as data mining and machine learning. This attraction is due to its importance for many applications such as social networks, bioinformatics and co-authorship networks. Towards solving this problem, Evolutionary Link Prediction (EVO-LP) framework is proposed, presented, analysed and tested. EVO-LP is a framework that includes dataset preprocessing approach and a meta-heuristic algorithm based on clustering for prediction. EVO-LP is divided into preprocessing stage and link prediction stage. Feature extraction, data under-sampling and feature selection are utilised in the preprocessing stage, while in the prediction stage, a meta-heuristic algorithm based on clustering is used as an optimiser. Experimental results on a number of real networks show that EVO-LP improves the prediction quality with low time complexity.

Vibration-Based Outlier Detection on High Dimensional Data

2016 ◽  
Vol 25 (03) ◽  
pp. 1650013
Author(s):  
Shuyin Xia ◽  
Guoyin Wang ◽  
Hong Yu ◽  
Qun Liu ◽  
Jin Wang
Keyword(s):  
Outlier Detection ◽  
Time Complexity ◽  
State Of The Art ◽  
High Dimensional Data ◽  
Difficult Problem ◽  
Index Structure ◽  
High Dimensional ◽  
Basic Model ◽  
Traditional Approaches ◽  
Better Than

Outlier detection is a difficult problem due to its time complexity being quadratic or cube in most cases, which makes it necessary to develop corresponding acceleration algorithms. Since the index structure (c.f. R tree) is used in the main acceleration algorithms, those approaches deteriorate when the dimensionality increases. In this paper, an approach named VBOD (vibration-based outlier detection) is proposed, in which the main variants assess the vibration. Since the basic model and approximation algorithm FASTVBOD do not need to compute the index structure, their performances are less sensitive to increasing dimensions than traditional approaches. The basic model of this approach has only quadratic time complexity. Furthermore, accelerated algorithms decrease time complexity to [Formula: see text]. The fact that this approach does not rely on any parameter selection is another advantage. FASTVBOD was compared with other state-of-the-art algorithms, and it performed much better than other methods especially on high dimensional data.

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