Complexity & Induction: Algorithms & Proofs

Related lectures (17)

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

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

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

Covers the complexity analyses of search and sorting algorithms.

Covers the time complexity of algorithms and includes a quiz.

Explores dynamic programming for palindromic subsequences, merging binary search trees, and finding the median of two sorted arrays.

Explores optimization problems and greedy algorithms for efficient decision-making.

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

Explores sorting and recursive algorithms, including complexity analysis, maximal value, and binary search.

Explores the binary search algorithm's efficiency in reducing search time.

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

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

Introduces recursion and binary search algorithms for efficient problem-solving.

Covers algorithms for searching, sorting, optimization, and the Halting Problem.

Explores binary search fundamentals, efficiency, and temporal complexity in algorithmic searching.

Explores algorithm growth, search complexities, and logarithmic properties in depth, with practical exercises included.

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