Design and Analysis of Algorithms (BCS401) VTU Notes – 4th Semester 2022 Scheme

Scoring well in Design and Analysis of Algorithms (ADA) requires more than just memorizing code—it requires understanding time complexity and algorithm design paradigms. Under the VTU 2022 Scheme, BCS401 focuses on both the efficiency of algorithms and various strategies like Divide & Conquer, Greedy, and Dynamic Programming.

To help you simplify these complex topics, we have provided unit-wise PDF notes and the most repeated exam questions.

???? Download VTU ADA Notes (BCS401) – 2022 Scheme


  • Module 1: Introduction & Analysis Framework https://searchcreators.org/search_blog

  • Highlights: Asymptotic Notations ($O, \Omega, \Theta$), Recursive vs. Non-recursive analysis, and Brute Force (Bubble/Selection Sort).

  • Module 2: Divide & Conquer and Decrease & Conquer https://searchcreators.org/search_blog

  • Highlights: Merge Sort, Quick Sort, Strassen's Matrix Multiplication, and Topological Sorting.

  • Module 3: Transform & Conquer and Space-Time Tradeoffs https://searchcreators.org/search_blog

  • Highlights: AVL Trees, Heapsort, Horspool’s Algorithm for String Matching.

  • Module 4: Dynamic Programming & Greedy Method https://searchcreators.org/search_blog

  • Highlights: Knapsack Problem, Warshall’s & Floyd’s Algorithms, Prim’s/Kruskal’s MST, and Dijkstra’s Shortest Path.

  • Module 5: Limitations of Algorithm Power https://searchcreators.org/search_blog


  • Highlights: P, NP, and NP-Complete problems, Backtracking (n-Queens), and Branch & Bound.




???? ADA Lab Manual & Model Papers

Don't forget the practical component! The ADA lab is where you implement these algorithms in Java or C++.


  • BCS401 Lab Manual: https://searchcreators.org/search_blog

  • Official Model Question Paper: https://searchcreators.org/search_blog

???? Most Important Questions for 2026 Exams


  • Asymptotic Notations: Define and graph $O, \Omega, \text{and } \Theta$.

  • Merge Sort Complexity: Derive the recurrence relation $T(n) = 2T(n/2) + f(n)$.

  • Knapsack Problem: Solve an instance using both Greedy and Dynamic Programming.

  • Dijkstra’s Algorithm: Find the single-source shortest path for a given weighted graph.

  • P vs NP: Explain the difference between NP-Hard and NP-Complete problems with examples.


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