Minimal polynomial (linear algebra)

Related lectures (32)

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Explores eigenvalues and minimal polynomial, emphasizing their importance in linear algebra.

Covers the solutions of homogeneous linear differential equations and how to find linearly independent solutions.

Covers the theory and applications of orthogonal projections, focusing on the Gram-Schmidt process for orthonormalization.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Covers the concept of polynomial characteristic of a matrix and its relation to eigenvalues.

Explores characteristic polynomials, eigenvalues, and eigenvectors of matrices A, B, C, D, and E.

Explores diagonalizing matrices, similarity, eigenvectors, and proper spaces.

Explores Schur's lemma and its applications in representations of an associative algebra over an algebraically closed field.

Explains the step-by-step process of diagonalization for linear transformations on finite-dimensional vector spaces.

Covers the Jordan Normal Form theorem and invariance of kernels under transformations.