Algorithm Analysis: Complexity and Pseudo-code

Related lectures (26)

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

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.

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

Delves into the complexity of factoring polynomials and the implications for security.

Explains Big-O notation for algorithm complexity and polynomial growth rates.

Covers Dijkstra's algorithm and its application to the all-pairs shortest path problem.

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

Analyzes the growth of functions to understand algorithm complexity and efficiency.

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.