Complexity of Algorithms: Growth of Functions

Related lectures (26)

Page 2 of 3

Explains Big-O notation for algorithm complexity analysis through polynomial examples and growth rate identification.

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

In this lecture, you will learn to use conditional statements in Scratch to create interactive programs.

Explores algorithm complexity, big-O notation, induction, recursion, and analysis of running times, covering NP problems and complexity classes.

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

Explores the analysis of the measurement process in the Shor Algorithm.

Covers control operations, algorithmic complexity, function calls, and travel time analysis.

Explores recursion in programming, discussing its benefits, challenges, and impact on algorithm complexity.

Explores advanced big-O facts for powers, logarithms, factorials, and function combinations.

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