Lectures related to Galerkin Method: Applications and Formulation

Explores systems of equations, vector operations, and geometric interpretations in linear algebra.

Explores operations and applications of linear algebra in solving equations and transformations.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Introduces eigenspaces in linear algebra through definitions and practical examples of determining eigenspaces for matrices.

Covers the decomposition of a vector in a base and its applications in speed and distance calculations.

Introduces linear combinations of vectors in R^n and their properties.

Explores linear dependence in matrices, systems of equations, unique solutions, derivatives, and integrals of quadratic functions.

Explores linear dependence and independence of vectors in geometric spaces.

Explores linear combinations, matrix-vector product, and matrix equation solutions.

Covers exercises on matrices, vectors, and linear systems.

Introduces linear algebra concepts, focusing on solving systems of linear equations and their representations.

Covers matrix notation, linear equations, vector spaces, and applications of least squares.

Covers Cramer's Rule for solving linear equations using determinants.

Explores solving systems of equations using matrices and determinants.

Explores landmarks, coordinates, vectors, coplanarity, Cartesian equations, and geometric rules in space.

Explores linear independence, bases, and matrix applications in linear transformations.

Explores the definition and properties of vector subspaces in linear algebra.

Covers linear applications, matrices, transformations, and the principle of superposition.

Explores linear functions, systems of equations, and geometric interpretations of solutions.

Introduces linear equations, matrices, systems of equations, and solution sets for 2 or 3 unknowns.