Orthogonality: Norm, Scalar Product, Perpendicularity

Related lectures (28)

Page 1 of 3

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Explains orthogonal projection on a vector subspace in Euclidean space.

Covers orthogonal projections, rectors, norms, and geometric observations in vector spaces.

Explores orthogonal complement and projection theorems in vector spaces.