Complexity of Algorithms: Growth and Laws

Related lectures (22)

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

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers the introduction to logarithms, algorithmic notations, and analysis of algorithm travel times.

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

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

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

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

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

Covers classical and quantum computational complexity concepts and implications.

Covers the complexity analyses of search and sorting algorithms.