Algorithmic Challenges: Solutions and Optimization

Related lectures (23)

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Covers the analysis of quick sort, including time complexity and randomization for finding the k-th smallest number.

Provides an overview of essential search and sort algorithms in computer science.

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

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Explores the Merge Sort algorithm, applying the Divide and Conquer approach to sorting arrays efficiently.

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

Explores the Quick Sort algorithm, focusing on its divide-and-conquer approach and time complexity analysis.

Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.

Explores Merge Sort, a sorting algorithm that divides, conquers, and combines arrays efficiently to achieve O(nlog n) time complexity.

Covers the efficient implementation of hash tables and the Quick Sort algorithm.