Optimization Methods: Lagrange Multipliers

Related lectures (28)

Page 1 of 3

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

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

Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.

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

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

Covers the Implicit Functions Theorem, explaining how equations can define functions implicitly.

Covers Taylor's formula, developments, and extrema of functions, discussing convexity and concavity.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Covers the statement of the Bernoulli's Hospital Rule and its application.

Explores techniques for minimizing functions and finding critical points.