Lectures related to Optimization Problems

Covers the method of Lagrange multipliers to find extrema subject to constraints.

Covers optimization problems, focusing on finding extrema of functions subject to constraints.

Covers advanced optimization methods using Lagrange multipliers to find extrema of functions subject to constraints.

Covers directional derivatives, implicit functions, and the concept of the maximum.

Explores real functions, covering parity, periodicity, and polynomial functions.

Discusses local extrema of functions in two variables around the point (0,0).

Explores functions' complements, parameter order, and list manipulation, including counting occurrences and solving the '100 prisoners problem'.

Explains how to find local extrema of functions using derivatives and critical points.

Covers functions, including even and odd functions, periodicity, and function operations.

Provides a Python crash course covering basic arithmetics, complex numbers, strings, lists, dictionaries, and more.

Introduces the fundamentals of functions in microcontroller programming, emphasizing naming rules and step-by-step development.

Covers the concept of partial derivatives and their applications in mathematical analysis.

Covers the definition of a function to determine if a given number is prime.

Covers entropy, its definition, and its implications in thermodynamics.

Covers the definitions and notations related to functions.

Covers the study of functions, focusing on convexity criteria within closed subintervals.

Covers advanced Python topics like numpy operations, scipy linear algebra, and matplotlib for creating figures.

Covers the optimization of functions, focusing on finding the maximum and minimum values over a given domain.

Covers conditions, loops, functions, and graphics in R programming with practical examples.

Covers the properties and operations of convex functions.