Lectures related to Optimization Methods: Lagrange Multipliers

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.

Explores the integration by parts technique through examples, showcasing its step-by-step application to functions like cos(x) and sin(x.

Covers Morse theory, focusing on critical points and non-degeneracy.

Covers the optimization of functions, focusing on finding the maximum and minimum values.

Discusses optimization techniques, focusing on local and global extrema in functions.

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Covers the Implicit Functions Theorem, providing a general understanding of implicit functions.

Covers the properties and operations of convex functions.

Explores limits, continuity, and uniform continuity in functions, including properties at specific points and closed intervals.

Explores integration by substitution with proofs and examples on anti-derivatives and function equivalence.

Covers the review of derivatives and functions, including the concept of chain rule and graphical representation.