Algebraic Multiplicity, Geometric Multiplicity

Related lectures (28)

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Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers the concept of diagonalization of matrices through the study of eigenvectors and eigenvalues.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Explores matrices, change of bases, eigenvectors, eigenvalues, and vector spaces.

Explores examples of diagonalization in linear algebra, focusing on eigenvalues and eigenvectors.

Explores matrix representation of linear applications, emphasizing eigenvalues and bases.

Covers eigenvalues, eigenvectors, and characteristic polynomials in matrix transformations.

Covers eigenvectors, eigenvalues, and their significance in linear applications.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Explores matrix similarity, eigenvalues, and diagonalization in linear algebra.