Piecewise Polynomial Interpolation: Splines

Related lectures (30)

Page 1 of 3

Introduces Lagrange interpolation for approximating data points with polynomials, discussing challenges and techniques for accurate interpolation.

Explores splines, emphasizing the least-squares method for interpolating splines and demonstrating its application using MATLAB.

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Explores trigonometric interpolation for approximating periodic functions and signals using equally spaced nodes.

Explores Lagrange interpolation, emphasizing uniqueness and simplicity in reconstructing functions from limited values.

Covers the concept of piecewise linear interpolation and the importance of dividing intervals correctly.

Covers the interpolation of nonlinear functions using Lagrange polynomials and error analysis.

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

Covers the optimization of error in polynomial interpolation, focusing on minimizing the error by strategically placing interpolation points.

Covers the basics of Newton interpolation and interpolation polynomials using Lagrange and Newton methods.