Lectures related to Exponentiation: Time Complexity

Explores optimizing exponentiation in modular arithmetic for efficient calculations and prime number determination.

Covers prime numbers, primality testing algorithms, modular arithmetic, and efficient exponentiation methods.

Covers examples of modular exponentiation, complexities, Lame's Theorem, Collatz Conjecture, and prime numbers.

Introduces deterministic approaches to identify prime numbers and covers algorithms and modular arithmetic for prime number testing.

Explores prime numbers, modular arithmetic, Wilson's theorem, and complexity analysis.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

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

Covers classical and quantum computational complexity concepts and implications.

Covers commutative groups and their significance in cryptography.

Covers the time complexity of algorithms and includes a quiz.

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

Covers algorithms for big numbers, Z_n, and orders in a group, explaining arithmetic operations and cryptographic concepts.

Covers prime numbers, RSA cryptography, and primality testing, including the Chinese Remainder Theorem and the Miller-Rabin test.

Explores elementary algebra concepts related to numeric sets and prime numbers, including unique factorization and properties.

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

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

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

Explores models of card shuffling, state spaces, and time complexity.

Explores the Shor algorithm circuit details for efficient number factoring using quantum computing.

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