Admission Open

Analysis of Algorithms Course in Mianwali

Course Title: Analysis of Algorithms

Course Overview:
The Analysis of Algorithms course delves into the study of algorithm design, complexity analysis, and optimization techniques. Students will learn how to analyze the efficiency and correctness of algorithms, understand algorithmic paradigms, and apply algorithmic strategies to solve computational problems. The course includes theoretical concepts, algorithmic analysis methods, and practical applications.

Course Objectives:

Understand algorithmic design paradigms and strategies.
Learn techniques for analyzing the time and space complexity of algorithms.
Study classic algorithms and data structures.
Explore algorithmic problem-solving approaches.
Gain practical experience through programming assignments and problem sets.
Develop critical thinking and algorithmic problem-solving skills.
Course Outline:

Introduction to Algorithms

Definition of algorithms and their importance in computing
Algorithm analysis techniques
Asymptotic notation (big-O, big-Theta, big-Omega)
Algorithm Design Techniques

Brute-force and exhaustive search algorithms
Divide and conquer approach
Greedy algorithms and their applications
Dynamic programming and memoization
Sorting and Searching Algorithms

Comparison-based sorting algorithms (e.g., bubble sort, insertion sort, merge sort, quicksort)
Non-comparison-based sorting algorithms (e.g., counting sort, radix sort)
Searching algorithms (linear search, binary search)
Graph Algorithms

Graph representation and terminology
Graph traversal algorithms (depth-first search, breadth-first search)
Shortest path algorithms (Dijkstra’s algorithm, Bellman-Ford algorithm)
Minimum spanning tree algorithms (Prim’s algorithm, Kruskal’s algorithm)
Dynamic Programming

Principles of dynamic programming
Examples of dynamic programming problems (knapsack problem, longest common subsequence)
Tabulation vs. memoization
Data Structures

Review of fundamental data structures (arrays, linked lists, stacks, queues)
Advanced data structures (trees, heaps, hash tables)
Choosing the right data structure for algorithm design
Complexity Analysis

Time complexity analysis using recurrence relations
Space complexity analysis
Best-case, worst-case, and average-case analysis
Optimization Techniques

Amortized analysis
Approximation algorithms
Randomized algorithms and their analysis
Algorithmic Problem-Solving

Solving computational problems using algorithmic techniques
Case studies and examples of real-world algorithmic applications
Challenges and limitations of algorithm design
Project Work and Programming Assignments

Implementing algorithms in programming languages (e.g., Python, Java)
Analyzing the performance of algorithms through experimentation
Solving algorithmic problems on platforms like LeetCode or HackerRank
Assessment Methods:

Problem-solving assignments and programming projects.
Algorithm analysis and complexity calculations.
Quizzes and exams covering algorithmic concepts and techniques.
Participation in algorithm design discussions and problem-solving sessions.

“Introduction to Algorithms” by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

“Algorithms” by Robert Sedgewick and Kevin Wayne
Online resources and tutorials on algorithm analysis and design

Admission Open for this course 
Contact Number: 03307615544

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