Data Structures and Algorithms — Complete Course Notes (BCA 3rd Sem)
Comprehensive lecture notes on asymptotic analysis, stacks, queues, linked lists, trees, graphs, and sorting algorithms for TU BCA.
Data Structures and Algorithms · CACS201 · Semester 3
Unit 2•Unit 2 of 5
Unit 2: 1. Algorithm Analysis & Asymptotic Notations
The efficiency of an algorithm is measured in terms of Time Complexity (CPU execution time) and Space Complexity (memory storage consumed) as a function of input size n.
Asymptotic Notations:
- Big O (O): Upper bound. Represents worst-case running time: f(n) ≤ c · g(n) for all n ≥ n0.
- Big Omega (Ω): Lower bound. Represents best-case running time: f(n) ≥ c · g(n).
- Big Theta (\Theta): Tight bound. Represents average-case when upper and lower bounds coincide.
Data Structures and Algorithms Complete Course (freeCodeCamp)
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- What is the worst-case time complexity of Quick Sort algorithm? (a) O(n) (b) O(n log n) (c) O(n^2) (d) O(log n)objective · 1 marks
- Differentiate between Linear Queue and Circular Queue. Explain how modulo arithmetic prevents false overflow.short · 5 marks