Eigen Library for Linear Algebra

Related lectures (48)

Introduces Matlab basics, error handling, and billiards project concepts.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explores linear transformations, matrices, and their practical applications in various contexts.

Covers eigenvalues, eigenvectors, and matrix similarity for dynamic systems.

Covers the basics of linear mapping and coordinate systems.

Covers the definition of determinants using cofactors and matrix operations.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Covers matrix operations, equivalent matrices, and morphisms of groups.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Covers the basics of C programming, including syntax, structure, and compilation processes.

Covers the properties and calculations of the determinant of a matrix.

Explains kernel, image, and linear maps, illustrating concepts with examples.

Covers the basics of constrained optimization, including tangent directions, trust-region subproblems, and necessary optimality conditions.

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Explores symmetric matrices, diagonalization, and their real-world applications in data analysis.

Explains dynamic memory allocation in C, focusing on malloc and calloc functions and their differences, including error prevention techniques.

Covers prime numbers, vectors, 3D geometry, crystallography, and algebraic structures in materials science.

Covers the Hartree-Fock Roothaan Equations and different types of Hartree-Fock methods, discussing their performance.

Covers programming concepts, memory management, debugging, and advanced MATLAB functionalities.

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