Learn Design and Analysis of Algorithems Complete
1.0.1
An Algorithm is a sequence of steps to solve a problem. Design and Analysis of Algorithm is very important for designing algorithm to solve different types of problems in the branch of computer science and information technology. This tutorial introduces the fundamental concepts of Designing Strategies, Complexity analysis of Algorithms, followed by problems on Graph Theory and Sorting methods. This tutorial also includes the basic concepts on Complexity theory.
Audience
This tutorial has been designed for students pursuing a degree in any computer science, engineering, and/or information technology related fields. It attempts to help students to grasp the essential concepts involved in algorithm design.
Prerequisites
The readers should have basic knowledge of programming and mathematics. The readers should know data structure very well. Moreover, it is preferred if the readers have basic understanding of Formal Language and Automata Theory.
Chapters
Basics of Algorithms
DAA - Introduction
DAA - Analysis of Algorithms
DAA - Methodology of Analysis
Asymptotic Notations & Apriori Analysis
DAA - Space Complexities
Design Strategies
DAA - Divide & Conquer
DAA - Max-Min Problem
DAA - Merge Sort
DAA - Binary Search
Strassen’s Matrix Multiplication
DAA - Greedy Method
DAA - Fractional Knapsack
DAA - Job Sequencing with Deadline
DAA - Optimal Merge Pattern
DAA - Dynamic Programming
DAA - 0-1 Knapsack
Longest Common Subsequence
Graph Theory
DAA - Spanning Tree
DAA - Shortest Paths
DAA - Multistage Graph
Travelling Salesman Problem
Optimal Cost Binary Search Trees
Heap Algorithms
DAA - Binary Heap
DAA - Insert Method
DAA - Heapify Method
DAA - Extract Method
Sorting Methods
DAA - Bubble Sort
DAA - Insertion Sort
DAA - Selection Sort
DAA - Quick Sort
DAA - Radix Sort
Complexity Theory
Deterministic vs. Nondeterministic Computations
DAA - Max Cliques
DAA - Vertex Cover
DAA - P and NP Class
DAA - Cook’s Theorem
NP Hard & NP-Complete Classes
DAA - Hill Climbing Algorithm
*Note : If you are having trouble viewing formulas try connecting to internet once.
This app can be used offline as well.
Audience
This tutorial has been designed for students pursuing a degree in any computer science, engineering, and/or information technology related fields. It attempts to help students to grasp the essential concepts involved in algorithm design.
Prerequisites
The readers should have basic knowledge of programming and mathematics. The readers should know data structure very well. Moreover, it is preferred if the readers have basic understanding of Formal Language and Automata Theory.
Chapters
Basics of Algorithms
DAA - Introduction
DAA - Analysis of Algorithms
DAA - Methodology of Analysis
Asymptotic Notations & Apriori Analysis
DAA - Space Complexities
Design Strategies
DAA - Divide & Conquer
DAA - Max-Min Problem
DAA - Merge Sort
DAA - Binary Search
Strassen’s Matrix Multiplication
DAA - Greedy Method
DAA - Fractional Knapsack
DAA - Job Sequencing with Deadline
DAA - Optimal Merge Pattern
DAA - Dynamic Programming
DAA - 0-1 Knapsack
Longest Common Subsequence
Graph Theory
DAA - Spanning Tree
DAA - Shortest Paths
DAA - Multistage Graph
Travelling Salesman Problem
Optimal Cost Binary Search Trees
Heap Algorithms
DAA - Binary Heap
DAA - Insert Method
DAA - Heapify Method
DAA - Extract Method
Sorting Methods
DAA - Bubble Sort
DAA - Insertion Sort
DAA - Selection Sort
DAA - Quick Sort
DAA - Radix Sort
Complexity Theory
Deterministic vs. Nondeterministic Computations
DAA - Max Cliques
DAA - Vertex Cover
DAA - P and NP Class
DAA - Cook’s Theorem
NP Hard & NP-Complete Classes
DAA - Hill Climbing Algorithm
*Note : If you are having trouble viewing formulas try connecting to internet once.
This app can be used offline as well.
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Poslední aktualizace
15. 10. 2017
