Eisenstein Series: Decomposition and Unitarizability

Related lectures (25)

Page 1 of 3

Explains orthogonal families, bases, and projections in vector spaces.

Covers the theorems related to orthogonal projection and orthonormal bases.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

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

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Explores the uniqueness and properties of orthogonal projection, including decomposition, associated matrix, linearity, and practical examples.

Explores orthogonal and orthonormal bases, Gram-Schmidt process, and orthogonal polynomials in physics.

Explores symmetrical bilinear forms, the Pythagorean theorem, inequalities, and orthogonal matrices.

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.