Concept

Minor (linear algebra)

Related lectures (30)

Covers the concept of the matrix of cofactors and a formula to calculate the inverse of a matrix.

Covers the properties and calculations of matrix determinants, including special cases and examples.

Explores matrix determinant properties, quick calculations, and geometric interpretation in vector spaces.

Explores the change of variables formula in higher dimensions and its application through determinant algebra and a practical example.

Explores multilinear groin and determinants, including the Sarrus rule and proof by induction.

Covers calculations and algorithms for determining the determinant of a matrix, including permuting rows and multiplying by coefficients.

Explores GPS error analysis, signal acquisition, positioning techniques, and DGPS systems.

Covers the calculation of determinants in matrix algebra and their application in solving linear equations.

Covers the properties of matrix determinants and strategies for their calculation.

Explores the change of variables formula in higher dimensions and its application through an example.

Covers strategies for calculating determinants and their geometric interpretation in various dimensions.

Explores the theory and applications of matrix determinants, emphasizing their role in matrix invertibility and solution uniqueness.

Explores the conditions for LU factorization without permutation and diagonal dominance in matrices.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores the spectral theorem, Coxeter graphs, eigenvalues, and determinants of positive definite matrices.

Explores unipotent matrices with eigenvalues equal to 1 and their properties.

Explores Cramer's Rule for solving linear equations and calculating matrix inverses.

Explains how to find the values for which -1 is an eigenvalue of a 3 by 3 matrix.

Explores matrix determinant properties, inverse, transpose, and applications in solving equations.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.