Lectures related to Complexity Analysis of FGD Algorithm

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

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

Covers classical and quantum computational complexity concepts and implications.

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

Explores the Simon Problem 4, analyzing quantum states and algorithm complexity.

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

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

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

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

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

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

Explains the Quantum Phase Estimation (QPE) algorithm and its complexity using two registers and SWAP gates.

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

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

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

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

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

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

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

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