Knapsack Problem: Optimization and Traveling Salesman

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Covers classical optimization problems, brute force algorithms, and integer linear optimization.

Explores the theory of computability, decision problems, complexity classes, and the 'P vs. NP' conundrum.

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

Explores dynamic programming for efficient problem-solving, illustrated with binomial coefficients and Pascal's triangle.

Covers classical and quantum computational complexity concepts and implications.

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

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

Explores algorithmic challenges, time complexity, optimization, recursion, and probability calculations.

Introduces the Traveling Salesman Problem and explores approximation methods using Markov chains.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.