Lectures related to Piecewise Linear Interpolation

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

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

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

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

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

Explores polynomial interpolation theory, emphasizing error expression and piecewise definition.

Covers polynomial interpolation, error analysis, stability, and piecewise linear interpolation using equally distributed nodes.

Covers piecewise linear and spline interpolation methods, stability, convergence, and data interpolation using splines.

Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.

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

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.

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

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

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

Covers piecewise polynomial interpolation with splines, focusing on Lagrange interpolation with Chebyshev nodes and error convergence.

Covers revisions on calculus, derivation rules, interpolation, and tangent vectors.

Covers the Newton method for finding zeros of functions using data interpolation.

Explores numerical differentiation methods and round-off errors in computer computations.

Covers the least squares method for approximating data and handling errors.

Explores image interpolation, splines, wavelets, and geometric transformations in image processing.