Linear Algebra: Quantum Mechanics

Related lectures (26)

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Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

Explores the role of linear operators in Quantum Mechanics and linear algebra, emphasizing eigenvalues and basis transformations.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.

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