2A2-G06 A solution of Large-scale TSP by use of LCO with Divide and Conquer Algorithm

2010 ◽  
Vol 2010 (0) ◽  
pp. _2A2-G06_1-_2A2-G06_4
Author(s):  
Kazunori ISHIKAWA ◽  
Ikuo SUZUKI ◽  
Masahito YAMAMOTO ◽  
Masashi FURUKAWA
Keyword(s):  
Large Scale ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm

Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Petri Net ◽  
Cyber Security ◽  
Large Scale ◽  
Security System ◽  
Divide And Conquer ◽  
Defense System ◽  
Divide And Conquer Algorithm

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

scholarly journals An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1732
Author(s):  
Sun-Ho Choi ◽  
Yoonkyung Jang ◽  
Hyowon Seo ◽  
Bum Il Hong ◽  
Intae Ryoo
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Sensor Nodes ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm ◽  
Computational Speed ◽  
Large Scale Networks ◽  
Gateway Deployment ◽  
The Cost ◽  
The Given

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.

scholarly journals A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

2016 ◽  
Vol 42 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Yi Mei ◽  
Mohammad Nabi Omidvar ◽  
Xiaodong Li ◽  
Xin Yao
Keyword(s):  
Large Scale ◽  
Black Box ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

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