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Lecture
Iterative Methods for Linear Equations
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Related lectures (27)
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Iterative Methods for Linear Equations
Introduces iterative methods for solving linear equations and discusses the gradient method for minimizing errors.
Jacobi and Gauss-Seidel methods
Explains the Jacobi and Gauss-Seidel methods for solving linear systems iteratively.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Crank-Nicolson and Heun's Methods
Covers the Crank-Nicolson and Heun's methods, discussing uniqueness of solutions and truncation errors in numerical methods.
Newton's Method: Convergence and Criteria
Explores the Newton method for non-linear equations, discussing convergence criteria and stopping conditions.
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.
Numerical Analysis: Stability in ODEs
Covers the stability analysis of ODEs using numerical methods and discusses stability conditions.
Numerical Methods: Euler and Crank-Nicolson
Covers Euler and Crank-Nicolson methods for solving differential equations.
Direct and Iterative Methods for Linear Equations
Explores direct and iterative methods for solving linear equations, emphasizing symmetric matrices and computational cost.
Newton's Method: Order 2
Explains Newton's method of order 2 for finding function zeros.
