Parallel algorithm

Describe how the quicksort works including a discussion of the pivot, how it is selected, and why the pivot is important to the quicksort. Let’s start describing what pivot is, its function and why is so important. The pivot is the element that basically divides the array in two halves, where the left half contains the smaller values and the right half contains the greater values. The pivot can be selected in many ways. First, last, middle element, actually any element of the array can be equally chosen for the pivot it all depends on how you want to implemented the array; if you select first or last will cause worst-case performance if you choose middle would be a better choice because it minimizes the chances of encountering worse case O(n2). Now let’s talk how quicksort works. It may look a little confusing but it is not as complicated as it seems but the best way to do this is with an example. Let’s assume that we have an array with 5 elements (4 2 5 1 3) and we are going to arrange the array in ascending order. 0 1 2 3 4 4 pivot 2 5 1 3 Index Elements If we look at the chart above we have 4 as the first element on the left of the array and 3 as the last element on the right of the array now we have to remember a simple rule “elements to the right of the pivot are suppose to have greater values than elements to the left of the pivot” consequently if we select 4 as the pivot (worst-case) and quick sort the array, we do a comparison check; is 4 (pivot) less than right…

Usability Report on Cogstate’s Brief Battery Louis Gurrieri Royal Melbourne Institute of Technology Executive Summary The Cogstate Brief Battery is a cognitive battery that is designed to assess aspects of working memory and processing speed. It is a computerised test comprised of tasks such as the Detection Task (DET), One Card Learning Task (OCL). All the tasks involve the use of playing cards as the stimuli for the tests, and by using cards it is easy to understand for…

• PSO Algorithm to generate test cases A. Initialize the population with N Particles where Program will search for optimal solution through the movement of these particles. And Set iterations counter I = 0. B. Apply Fitness function: Calculating the fitness value by calculating the percentage of this particle will share in minimizing the total processing time to find the optimal solution. C. Compare the calculated fitness value of each particle with its (lbest). If current value is better…

Traveling Salesman Problem (TSP) is one of combinatorial optimization problems. X TSP is NP-hard problem which defined as a set of cities and each city should be visited once with minimum tour length. This paper solved this problem using Firefly Algorithm (FA) and k-means clustering by three steps: cluster the nodes, finding optimal path in each cluster and connect the clusters. The first step is to divide all nodes into sub-problems using k-means clustering, the second step is to use FA to find…

A COMPARITIVE STUDY OF NEAREST NEIGHBOUR ALGORITHM AND GENETIC ALGORITHM IN SOLVING TRAVELLING SALESMAN PROBLEM Ajaz Ahmed Khan Electronics and communication department SSGI FET Bhilai, India ajz70277@gmail.com Mrs. Himani Agrawal Electronics and communication department SSGI FET Bhilai, India Abstract—In this paper, we have used two algorithms, i.e. the Nearest Neighbor algorithm and Genetic Algorithm to solve the Travelling Salesman problem. The Travelling Salesman…

I used the parallel format to restate and summarize these same points in the conclusion. For example, I started my speech asking how many people had heard of the quote and ended the presentation by bringing that same idea back into the conclusion. I also made sure to restate the ideas in my thesis and my specific speech purpose from the introduction in the conclusion. Throughout the entire speech, I made sure to explain to the audience what I was about go talk about or go over, making my topic…

in Computational Physics, but a car accident forced me to focus on my recovery for several months so I never participated in that program. Afterward, I worked as Algorithm optimizer and back-end developer in some companies(listed in my CV). In one of the companies, my responsibility was to optimize their current algorithms. The company was a hardware firewall provider. Upon my investigations, I came to the understanding that their parallelization was not effective at all. In a standard test of…

Abstract- SVM(Support vector machine) is Mainly established for linear two-class classification through building an optimal splitting hyper plane, here the margin is maximized. SVM is useful for kernel trick to map the novel input space into a high dimensional feature space to improve the classifier generalization ability when the training data is not linear splitable. GA(Genetic Algorithm) is a stochastic and the empirical searching algorithm that is stimulated by natural evolution. The…

I became interested with an Information Security Analyst because my father is one. Every take your child to work day I would wake up early with my dad and run to his car excited to go to his office for the day. The building is a plain, brown brick building, but every time I saw it, I instantly got excited. Walking into the office everyone knew my name, I would always wave at everyone I saw. The secretary always gave me a lollipop and called me her “Little bundle of sunlight”. I would then go…

“It definitely sharpened my interest in language, the way people used language, slang words, speech patterns. There 's a big advantage to being the outsider” (1). This is a quote by Amy Heckerling, and it explains how speech patterns often go unnoticed by people of a certain group. A person with a southern accent believes that their accent is the normal, and a person with a Boston accent does not think they have one. Clive Thompson’s article “He and She; What’s the Real Difference?” explains an…