Concept

Best, worst and average case

Related lectures (15)

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Explores loop invariants, time analysis, and the Divide-and-Conquer approach with a focus on Merge Sort.

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

Covers the complexity analyses of algorithms and their worst-case time complexities.

Explores worst-case complexity in computer science and the importance of real-life complexity in algorithm selection.

Covers topics related to induction and recursion, including mathematical induction proofs and recursive definitions.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Covers the growth of functions and time complexity analysis for search algorithms.

Covers the Heapsort algorithm, which sorts arrays efficiently using max-heaps and introduces priority queues.

Explores solving linear systems iteratively and compares different solvers based on worst-case assumptions and convergence measures.

Covers the complexity analyses of search and sorting algorithms.

Covers heaps, heapsort, heap data structure, storage in arrays, and heap property maintenance.

Explains errors in correlated and uncorrelated sampling, correlation function, time, and blocking analysis.

Explains recursive sorting using Merge Sort and its linearithmic complexity.