Theory of Computation: Complexity of Problems

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

Explores the Merge Sort algorithm, applying the Divide and Conquer approach to sorting arrays efficiently.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Explores examples of algorithm complexity, sorting, and polynomial computations.

Covers the time complexity of algorithms and includes a quiz.

Explores the Quick Sort algorithm, focusing on its divide-and-conquer approach and time complexity analysis.

Covers classical and quantum computational complexity concepts and implications.

Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.