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Computational Linear Algebra course covering topics like iterative methods, matrix decompositions, and applications. It includes theoretical concepts, practical exercises, and code. Advanced methods like QR factorization, spectral theorem, and iterative solvers for linear systems.
Fast routines for solving large systems of linear equations in R. Makes Eigen Cholesky-, LU-, QR-, and iterative (Conjugate Gradient, BiCGSTAB) solvers for both dense and sparse problems available.
Numerical methods for engineers used for finding roots, solving matrix, finding functions from given values, performing integrals whose analytical solution is exhaustive, and solutions by approximation for differential equations.
Comparison of different implementations of the Cholesky decomposition method on different open-source languages and Matlab, for the resolution of linear systems for sparse, symmetric and positive definite matrices.