Complexity of Algorithms: Big-O

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

Covers classical and quantum computational complexity concepts and implications.

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

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

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

Covers complexity analysis, pseudo-code, and algorithm justification through examples and theoretical explanations.

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

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

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

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