Gram-Schmidt Algorithm: Orthogonalization and QR Factorization

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Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Covers the organization of linear algebra course and exercises for civil engineering and environmental sciences students.

Explores linear combinations, basis determination, and vector space dimensionality through practical examples and exercises.

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Explores solutions of linear equations, null spaces, subspaces, vector spaces, linear independence, bases, and dimensions.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Explains orthogonal families, bases, and projections in vector spaces.

Covers orthogonal bases, Gram-Schmidt method, linear independence, and orthonormal matrices in vector spaces.

Explores QR factorization and the least squares method for solving systems of equations.