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Lecture
Linear Systems Resolution
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Related lectures (36)
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems by decomposing a matrix A into P, T, and P_A.
Linear Systems: LU Factorization with Pivoting
Explains the Gaussian elimination algorithm with pivoting and LU factorization for linear systems.
Factorisation LU
Explains Gaussian elimination and LU factorization for solving linear systems of equations.
Linear Systems: Convergence and Methods
Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.
Direct Methods for Linear Systems of Equations
Explores direct methods for solving linear systems of equations, including Gauss elimination and LU decomposition.
Linear Algebra: Applications and Algorithms
Explores applications of linear algebra in image and signal processing, and introduces algorithms like Gaussian elimination and LU decomposition.
QR Factorization: Least Squares System Resolution
MOOC: Linear Algebra (Part 3)
Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.
Linear Systems Resolution
MOOC: Numerical Analysis for Engineers
Summarizes methods for resolving linear systems, including Gaussian elimination and LU decomposition.
Linear Systems Resolution
MOOC: Linear Algebra (Part 1)
Explores LU decomposition for solving linear systems and the associativity of operations.
Diagonalization Techniques: Jacobi Method
Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.
Diagonalization Techniques: Jacobi Method
Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.
Linear Systems: Chapters 4, 5, 6
Explores the link between linear systems and optimization through elimination and LU decomposition.
Vectorization in Python: Efficient Computation with Numpy
Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
Linear Systems: Direct Methods
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Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.
Construction of an Iterative Method
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Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
Numerical Analysis: Direct Methods for Linear Systems
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Covers direct methods for solving linear systems in numerical analysis.
Linear Systems: Direct Methods
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Explores linear systems, Gaussian elimination, LU decomposition, and matrix types.
Linear Systems: Factorization and Cholesky
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Explores linear systems, Cholesky factorization, LU factorization, and matrix precision.
Iterative Methods: Linear Systems
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Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.
Cholesky Factorization: Theory and Algorithm
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Explores the Cholesky factorization method for symmetric positive definite matrices.
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