Local Extremum Conditions: n=2 and n=3

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Explores stationary points, saddle points, symmetric matrices, and orthogonal properties in optimization.

Focuses on determining local extremum points of functions through various examples.

Revisits local and absolute extremums of multivariable functions, emphasizing critical points and their classification.

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

Covers the discussion of local extrema, concavity, convexity, and inflection points in functions.

Explains extrema of functions in several variables, stationary points, saddle points, and the role of the Hessian matrix.

Covers the conditions for finding absolute extrema in multivariable functions.

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

Explores applications of differential calculus, including theorems, convexity, extrema, and inflection points.