Numerical Analysis: Advanced Topics

Related lectures (28)

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Explores implicit schemes in numerical analysis, emphasizing stability and convergence properties in solving differential equations.

Discusses finite differences and finite elements, focusing on variational formulation and numerical methods in engineering applications.

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Covers implicit schemes in numerical analysis for solving partial differential equations.

Covers Euler and Crank-Nicolson methods for solving differential equations.

Provides an overview of polynomial interpolation techniques in numerical analysis, focusing on Lagrange interpolation and error estimation methods.

Covers numerical integration methods, focusing on trapezoidal rules, degree of exactness, and error analysis.

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Covers the Runge-Kutta method for approximating solutions of differential equations.

Explores challenges in polynomial approximation, stability issues, and error analysis in numerical differentiation.