A comprehensive academic resource for Analysis of Algorithm (AOA) and Analysis of Algorithm Laboratory (AOA Lab), covering algorithm design paradigms, complexity analysis, and implementation techniques essential for computer engineering foundations.
Overview · Contents · Reference Books · The Wall · Personal Preparation · Assignments · Practice Test · Laboratory · Syllabus · Usage Guidelines · License · About · Acknowledgments
Analysis of Algorithm (CSC402) and Analysis of Algorithm Lab (CSL401) are core subjects in the Second Year (Semester IV) of the Computer Engineering curriculum at the University of Mumbai. These courses establish the foundational understanding of algorithm design, analysis techniques, and optimization strategies crucial for developing efficient computational solutions.
The curriculum encompasses several key domains in algorithm theory and application:
- Algorithm Design Techniques: Divide and Conquer, Greedy Method, Dynamic Programming, and Backtracking.
- Algorithm Analysis: Time complexity, space complexity, and asymptotic notation (Big-O, Omega, Theta).
- Sorting Algorithms: Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort.
- Searching Algorithms: Binary Search, Linear Search, and advanced search techniques.
- Graph Algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), and shortest path algorithms.
- String Matching: Pattern matching algorithms and string processing.
- NP-Completeness: Complexity classes (P, NP, NP-Complete, NP-Hard) and reduction techniques.
- Advanced Topics: Floyd-Warshall, Longest Common Subsequence (LCS), and optimization problems.
This repository represents a curated collection of study materials, reference books, assignments, and personal preparation notes compiled during my academic journey. The primary motivation for creating and maintaining this archive is simple yet profound: to preserve knowledge for continuous learning and future reference.
As I progress in my career, I recognize that algorithmic fundamentals remain essential for software engineering, competitive programming, and technical interviews. This repository serves as my intellectual reference point: a resource I can return to for relearning concepts, reviewing methodologies, and strengthening understanding when needed.
Why this repository exists:
- Knowledge Preservation: To maintain organized access to comprehensive study materials beyond the classroom.
- Continuous Learning: To support lifelong learning by enabling easy revisitation of fundamental algorithm concepts.
- Academic Documentation: To authentically document my learning journey through AOA and AOA Lab.
- Community Contribution: To share these resources with students and learners who may benefit from them.
Note
All materials in this repository were created, compiled, and organized by me throughout my undergraduate program (2018-2022) as part of my coursework, laboratory assignments, and project implementations.
This collection includes comprehensive reference materials covering all major topics:
| # | Resource | Focus Area |
|---|---|---|
| 1 | AOA Techmax | Complete syllabus coverage |
| 2 | Banker's Algorithm | Deadlock avoidance algorithm |
| 3 | Binary Search | Divide and conquer search |
| 4 | Knapsack Problem | Dynamic programming optimization |
| 5 | Master Theorem | Recurrence relation analysis |
| 6 | Merge Sort | Divide and conquer sorting |
| 7 | MIN_MAX Algorithm | Game theory and decision trees |
| 8 | AOA Question Bank | Additional practice questions |
| 9 | AOA VIVA | Preparation guide for oral examinations |
| 10 | SEM - IV Books List | Curated book list |
Collaborative Study Notes by Amey & Mega
 Amey Thakur  |
 Mega Satish  |
|---|
The Wall - Notes Authored by MEGA SATISH
Comprehensive chapter-wise notes curated by Mega Satish, covering all essential topics:
| Chapter | Resource | Topics Covered |
|---|---|---|
| 1 | AOA Chapter - 1 | Introduction to algorithms and complexity analysis |
| 2 | AOA Chapter - 2 | Algorithm design techniques |
| 3 | AOA Chapter - 3 | Advanced algorithms and optimization |
| 4 | AOA Chapter - 4 | Graph algorithms and NP-completeness |
Important
COVID-19 Impact: This coursework was completed during the COVID-19 pandemic in 2020. Due to the nationwide lockdown and the sudden transition to online learning, several planned laboratory sessions and collaborative note-taking activities could not be carried out as originally intended. Despite these challenges, efforts were made to adapt and preserve as much work as possible. The limited chapter notes in this section reflect the disruption caused by the pandemic, rather than a lack of effort or commitment.
Study materials and planning resources for effective exam preparation:
| # | Resource | Description |
|---|---|---|
| 1 | Syllabus Breakdown | Detailed module-wise syllabus notes |
| 2 | Module Planning | Topic organization and study schedule |
| 3 | Examination Blueprint | Question paper pattern and marking scheme |
| 4 | Achievement Certificate | Recognition in algorithmic excellence |
Academic assignments for comprehensive learning and practice:
| # | Assignment | Description | Date |
|---|---|---|---|
| 1 | Assignment 1 | Fundamental concepts in algorithm analysis | January 23, 2020 |
| 2 | Assignment 2 | Advanced topics in algorithm design | February 13, 2020 |
| 3 | Friday Assignment | Special assignment | March 13, 2020 |
Topics Covered: Algorithm Analysis (time/space complexity) · Design Paradigms (Divide & Conquer, Greedy, Dynamic Programming, Backtracking) · Sorting & Searching · Graph Algorithms · NP-Completeness
Practice assessment details:
| # | Test | Date | Time | Class |
|---|---|---|---|---|
| 1 | Practice Test AOA | May 6, 2020 | 10:00 AM - 11:00 AM | SE B-50 |
The laboratory component (CSL401) focuses on hands-on implementation of various algorithms and data structures using C, providing practical experience in analyzing and optimizing computational solutions.
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Tip
Live Implementation: For a comprehensive visual showcase, visit the AOA Lab Portfolio Dashboard. For the complete source code and detailed documentation, visit the AOA Lab directory. Visualization is key. Always trace your algorithm's execution flow on paper before coding. Create recursion trees and tables for dynamic programming to optimize computational efficiency.
| # | Experiment | Programs | Source Code |
|---|---|---|---|
| 1 | Insertion Sort, Selection Sort | 2 | View |
| 2 | Binary Search (Divide and Conquer) | 1 | View |
| 3 | Merge Sort (Divide and Conquer) | 1 | View |
| 4 | Longest Common Subsequence (Dynamic Programming) | 1 | View |
| 5 | All Pairs Shortest Path (Floyd-Warshall Algorithm) | 1 | View |
The portfolio includes a real-time interactive visualization suite (Algorithmic Analysis Visualizer) featuring:
- Network Optimizer: Visualizes the Floyd-Warshall Algorithm ($O(n^3)$) on a cyber-themed graph topology.
- String DNA: Interactive Longest Common Subsequence (LCS) matcher (Default: "AMEY_THAKUR" vs "MEGA_SATISH").
- Complexity Race: A grand performance showdown between Binary Search ($O(\log n)$), Merge Sort ($O(n \log n)$), Insertion Sort ($O(n^2)$), and Floyd-Warshall ($O(n^3)$).
Experiment 1: Sorting Algorithms (2 Programs)
| Program | Algorithm | Time Complexity | Code |
|---|---|---|---|
Insertion_Sort.c |
Insertion Sort | O(n²) | View |
Selection_Sort.c |
Selection Sort | O(n²) | View |
Experiment 2: Divide and Conquer - Search (1 Program)
| Program | Algorithm | Time Complexity | Code |
|---|---|---|---|
Binary_Search.c |
Recursive Binary Search | O(log n) | View |
Experiment 3: Divide and Conquer - Sort (1 Program)
| Program | Algorithm | Time Complexity | Code |
|---|---|---|---|
Merge_Sort.c |
Merge Sort | O(n log n) | View |
Experiment 4: Dynamic Programming (1 Program)
| Program | Algorithm | Time Complexity | Code |
|---|---|---|---|
Longest_Common_Subsequence.c |
LCS (Dynamic Programming) | O(m×n) | View |
Experiment 5: Graph Algorithms (1 Program)
| Program | Algorithm | Time Complexity | Code |
|---|---|---|---|
All_Pair_Shortest_Path.c |
Floyd-Warshall | O(n³) | View |
| # | Resource | Description |
|---|---|---|
| 1 | Interactive Lab Portfolio | Interactive dashboard with source code descriptions and visualizations |
| 2 | Laboratory Journal | Complete record of experiments with code, outputs, and analysis |
| 3 | Lab README | Detailed navigation guide with program descriptions |
Official CBCGS Syllabus
Complete Second Year Computer Engineering syllabus document from the University of Mumbai, including detailed course outcomes, assessment criteria, and module specifications for Analysis of Algorithm and Analysis of Algorithm Lab.
Important
Always verify the latest syllabus details with the official University of Mumbai website, as curriculum updates may occur after this repository's archival date.
This repository is openly shared to support learning and knowledge exchange across the academic community.
For Students
Use these resources as reference materials for understanding concepts, reviewing problem-solving techniques, and preparing for examinations. All content is organized for self-paced learning.
For Educators
These materials may serve as curriculum references, assignment examples, or supplementary teaching resources. Attribution is appreciated when utilizing content.
For Researchers
The documentation and organization may provide insights into academic resource curation and educational content structuring.
This repository and all linked academic content are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). See the LICENSE file for complete terms.
Note
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original author.
Created & Maintained by: Amey Thakur
Academic Journey: Bachelor of Engineering in Computer Engineering (2018-2022)
Institution: Terna Engineering College, Navi Mumbai
University: University of Mumbai
This repository represents a comprehensive collection of study materials, reference books, assignments, and personal preparation notes curated during my academic journey. All content has been carefully organized and documented to serve as a valuable resource for students pursuing Analysis of Algorithm and Analysis of Algorithm Lab.
Connect: GitHub · LinkedIn · ORCID
Grateful acknowledgment to Mega Satish for her exceptional contribution to this repository through "THE WALL" - comprehensive chapter-wise notes that became an invaluable resource for understanding complex algorithmic concepts. Her constant support, patience, and clarity throughout this journey made a real difference, not only because she explained concepts so clearly, but because she truly cared about understanding them together. Her thoughtful approach to teaching, openness to discussion, and steady encouragement turned challenges into meaningful learning moments. This work reflects the growth that came from learning side by side. Thank you, Mega, for everything you shared and taught along the way.
Grateful acknowledgment to the faculty members of the Department of Computer Engineering at Terna Engineering College for their guidance and instruction in Analysis of Algorithm. Their clear teaching and continued support helped develop a strong understanding of algorithmic design and complexity analysis.
Special thanks to the mentors and peers whose encouragement, discussions, and support contributed meaningfully to this learning experience.
Overview · Contents · Reference Books · The Wall · Personal Preparation · Assignments · Practice Test · Laboratory · Syllabus · Usage Guidelines · License · About · Acknowledgments
Computer Engineering (B.E.) - University of Mumbai
Semester-wise curriculum, laboratories, projects, and academic notes.