Lectures related to Analysis of algorithms

Covers the Mean Value Theorem in differential calculus, focusing on critical points and global extrema within intervals.

Introduces the Master Theorem for analyzing algorithm time complexity through problem subdivision and formula application.

Covers O-Notation, local extrema, and critical points in functions.

Introduces algorithm design and time complexity analysis using examples and notations like n² and O(n).

Discusses the complexity of a recursive algorithm to find the sum of integers.

Covers the analysis of algorithms, focusing on insertion sort and computational models.

Explores advanced big-O facts for powers, logarithms, factorials, and function combinations.

Discusses matrix multiplication using divide-and-conquer techniques and introduces Strassen's algorithm for improved efficiency.

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

Explores the complexity of calculating binomial coefficients using dynamic programming.

Covers the basics of distributed algorithms and their importance in building reliable and secure systems.

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

Explains Optimal Binary Search Trees using dynamic programming and covers a midterm exam from 2016.

Covers techniques for solving recurrences and introduces the Master Theorem.

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

Explains Big-O notation for algorithm complexity and polynomial growth rates.

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

Covers the von Neumann Extractor and its extensions, aiming to derive and analyze an optimal extractor.

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

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