Algorithm Analysis: Growth, Search, and Logarithm

Related lectures (21)

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

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

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

Explores examples of algorithm complexity, sorting, and polynomial computations.

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Explains binary search, the logarithm function, and recursive algorithms for factorial and Fibonacci sequences.

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

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

Explores binary search trees, covering operations, implementations, and real-world applications involving train tracks.