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Lecture
Linear Systems: Direct Methods
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Related lectures (27)
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Linear Systems: Factorization and Cholesky
Explores linear systems, Cholesky factorization, LU factorization, and matrix precision.
Cholesky Factorization: Theory and Algorithm
Explores the Cholesky factorization method for symmetric positive definite matrices.
Iterative Methods for Linear Systems
Covers iterative methods for solving linear systems of equations and discusses the convergence properties of methods like Richardson's method.
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: Convergence and Methods
Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.
Singular Value Decomposition: Applications and Interpretation
Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
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: Diagonal and Triangular Matrices, LU Factorization
Covers linear systems, diagonal and triangular matrices, and LU factorization.
Iterative Methods: Linear Systems
Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.
