Supporting Hyperplanes

Related lectures (29)

Covers supporting hyperplanes for approximating functions' graphs in different dimensions using hyperplanes and linear approximations.

Covers the definitions of differentiable and derivable functions and their geometric interpretation.

Explores finding the equation of the tangent line to a function's graph at a point.

Covers the concept of osculating planes and parameterized curves, discussing the speed of points and linear independence.

Explores differentiability in functions and geometric interpretation of tangent planes.

Explores differentiable functions, gradients, and tangent planes in two-variable functions.

Covers differentiable functions, derivatives, and tangent planes in mathematics.

Explores differentiability in two variables and the chain rule for compositions.

Covers the definition and properties of differentiable functions, focusing on differentiability, limits, continuity, and partial derivatives.

Explores bijective functions, coordinate transformations, and graphical representations in two dimensions.

Explains differentiability in multivariable functions and the geometric interpretation of tangent planes.

Explores the Local Inversion Theorem and extremum points in functions.

Explores Taylor series expansions for derivatives and integrals, including practical applications.

Explores derivatives and approximations, focusing on logarithmic functions and their properties.

Explores derivatives, Hessians, and Taylor approximations for differentiable functions.

Covers linear approximation, parametric derivatives, and differentiability conditions on intervals.

Explores differentiable functions, Taylor approximations, and directional derivatives in open sets.

Introduces differential forms on manifolds, covering tangent bundles and intersection pairings.

Covers the differentiability of functions of multiple variables and the significance of directional derivatives and gradients.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

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