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Lecture
Richardson's Methods for Linear Systems
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Related lectures (29)
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Iterative Methods for Linear Equations
Covers iterative methods for solving linear equations and analyzing convergence, including error control and positive definite matrices.
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.
Regression & Systemed Lineaires
Covers the principles of regression and linear systems, focusing on iterative methods.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Linear Systems: Iterative Methods
Explores linear systems and iterative methods like gradient descent and conjugate gradient for efficient solutions.
Linear Systems: Direct Methods
Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.
Iterative Methods for Linear Equations
Introduces iterative methods for solving linear equations and discusses the gradient method for minimizing errors.
Cholesky Factorization: Theory and Algorithm
Explores the Cholesky factorization method for symmetric positive definite matrices.
Direct and Iterative Methods for Linear Equations
Explores direct and iterative methods for solving linear equations, emphasizing symmetric matrices and computational cost.
Effect of Rounding Errors in Linear Systems
Explores the effect of rounding errors in solving linear systems using the LU factorization method.
