Complexity & Induction: Algorithms & Proofs

Related lectures (17)

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.

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

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

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.

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

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

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

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Introduces Cartesian product and induction for proofs using integers and sets.

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

Explores linear search, insertion sort, coin change, maximum matching, and the halting problem in algorithms.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

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

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