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MATH-251(c): Numerical analysis
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Lectures in this course (106)
Iterative Methods
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Explores iterative methods for solving linear systems of equations, including Jacobi and Gauss-Seidel.
Iterative Methods: Linear Systems
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Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.
Ordinary Differential Equations
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Covers the approximation of a system of differential equations using a method based on time intervals.
Finite Differences: Definition and Proof
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Covers the definition of the center of a function using finite differences and provides step-by-step proofs.
Construction of an Iterative Method II
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Covers the construction of an iterative method for linear systems and introduces relaxation and residual analysis.
Polynomial Interpolation: Error and Definition
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Explores polynomial interpolation theory, emphasizing error expression and piecewise definition.
Jacobi Method: Convergence Conditions
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Explores the Jacobi method for linear systems, emphasizing convergence conditions and matrix properties.
Jacobi Method: Part I
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Introduces the Jacobi method for solving linear systems by iteratively updating the diagonal elements of a matrix.
Ordinary Differential Equations
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Covers the extension of ordinary differential equations to n-dimensional space.
Linear Systems: Iterative Methods
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Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.
Bisection Method: Nonlinear Equations
Covers the bisection method for finding zeros of nonlinear functions.
Numerical Analysis: Newton's Method
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Explores Newton's method for finding roots of nonlinear equations and its interpretation as a second-order method.
Numerical Analysis: Stability in ODEs
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Covers the stability analysis of ODEs using numerical methods and discusses stability conditions.
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Numerical Analysis: Nonlinear Equations
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Explores the numerical analysis of nonlinear equations, focusing on convergence criteria and methods like bisection and fixed-point iteration.
Numerical Integration
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Explores numerical integration methods, including the composite quadrature formula and the efficiency of Simpson's rule, aiming to enhance student understanding and reduce stress.
Numerical Analysis: Linear Systems
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Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
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