← Algorithm Design learning pathAlgorithm Design · Module 1 of 10
Algorithm analysis
Measure correctness and efficiency of algorithms.
📖 Key ideas
- An algorithm must be correct, finite and efficient.
- Asymptotic notations: Big-O (upper), Big-Ω (lower), Big-Θ (tight).
- Analyse best, worst and average cases.
- Recurrences describe recursive running times: T(n) = 2T(n/2) + n.
- The Master Theorem solves common recurrences.
💡 Real-life example: Asymptotic analysis is judging vehicles by highway speed, not by how they start at a traffic light.
Big-OUpper bound
Big-ΘTight bound
RecurrenceEquation for recursive time
Master TheoremSolves divide-and-conquer recurrences