Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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Discusses advanced analysis concepts, focusing on differential equations and timers in microcontrollers.

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Covers numerical methods for finding roots, including secant, Newton, and fixed point iteration techniques.

Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.

Covers the convergence of fixed point methods for nonlinear equations, including global and local convergence theorems and the order of convergence.

Covers advanced topics in Python programming, focusing on control structures and functions.

Covers the basics of writing functions in Python and working with function arguments.

Introduces Python programming concepts, focusing on lists, functions, and their applications in problem-solving.

Covers the theory and applications of optimization with constraints, including key concepts and numerical methods.

Covers numerical integration techniques using SciPy and Matplotlib for visualizing functions and approximating integrals.