Saddle point

Related lectures (31)

Page 3 of 4

Covers the classification of stationary points in functions of two variables using the Hessian matrix.

Explains local extremum conditions for n=2 and n=3, critical points, and stationary points.

Covers Taylor approximation and extrema in multivariable functions with examples.

Covers non-convex optimization techniques and their applications in machine learning.

Covers the analysis of stationary points in functions, focusing on optimization methods.

Explores the geometry of surfaces in R², including implicit descriptions, critical points, and regular surfaces.

Covers derivatives, hyperbolic functions, and theorems of Rolle and finite increments.

Covers the concept of partial derivatives and properties of saddle points.

Explores sufficient conditions for functions and implicit functions in finding solutions.

Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.