Linear Recurrences and Applications

Related lectures (29)

Page 2 of 3

Covers the Implicit Functions Theorem, explaining how equations can define functions implicitly.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers functions, compositions, and applications in linear algebra.

Explores linear algebra fundamentals, including key definitions, theorems, and practical applications in mathematics and technology.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Covers linear transformations using matrices, focusing on linearity, image, and kernel.

Explores the demonstration of a fundamental linear algebra theorem and the concept of linear independence and bases.

Covers second-order linear differential equations, focusing on their solutions and the concept of linear independence among them.

Covers the Riemann sphere, focusing on its conditions and correlation functions in mathematical and physical contexts.

Explores eigenvalues, eigenvectors, and diagonalizable transformations through various examples.