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Lecture
Iterative Methods: Linear Systems
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Related lectures (30)
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Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Iterative Methods: Linear Systems
Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.
Jacobi and Gauss-Seidel methods
Explains the Jacobi and Gauss-Seidel methods for solving linear systems iteratively.
Linear Systems: Iterative Methods
Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.
Eigenvalues and Eigenvectors
Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.
Linear Systems: Convergence and Methods
Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.
Linear Systems: Direct Methods
Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.
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.
Iterative Methods for Linear Equations
Explores iterative methods for linear equations, including Jacobi and Gauss-Seidel methods, convergence criteria, and the conjugate gradient method.
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
