Chebyshev Polynomial Calculator

T_n(x) = cos(n·arccos(x))

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About Chebyshev Polynomial Calculator

A Chebyshev polynomial calculator computing T_n(x) (first kind) and U_n(x) (second kind) using recurrence T_{n+1}=2xT_n−T_{n-1}. Chebyshev nodes minimize Runge's phenomenon. Used in spectral methods, filters, numerical integration. Client-side.

Chebyshev Polynomial Calculator Features

  • T_n(x) value
  • U_n(x) value
  • Coefficients
  • Recurrence
  • Nodes
Chebyshev polynomials: T_n(x)=cos(n·arccos x). T_0=1, T_1=x, T_2=2x²−1, T_3=4x³−3x. Recurrence: T_{n+1}=2xT_n−T_{n-1}. Best approximation property: among all degree-n polynomials with leading coefficient 2^{n-1}, T_n/2^{n-1} deviates least from 0 on [-1,1].

How to Use

Enter n and x:

  • T_n(x): First kind
  • U_n(x): Second kind
  • Coefficients: Polynomial form

Best Approximation

Chebyshev interpolation at nodes x_k=cos((2k+1)π/(2n)) avoids Runge's phenomenon. The error is within a factor of (4/π)log(n)+1 of the best possible. This makes Chebyshev the gold standard for polynomial approximation.

Applications

  • Spectral methods for PDEs
  • Digital filter design
  • Clenshaw-Curtis quadrature
  • Function approximation libraries

Step-by-Step Instructions

  1. 1Enter degree n.
  2. 2Enter x value.
  3. 3Compute T_n(x).
  4. 4Compute U_n(x).
  5. 5View coefficients.

Chebyshev Polynomial Calculator — Frequently Asked Questions

Why are Chebyshev polynomials special for approximation?+

They minimize the maximum error (minimax property). T_n(x)/2^{n-1} has the smallest ∞-norm among all monic polynomials of degree n on [-1,1]. This is the equioscillation theorem of Chebyshev.

What's the difference between T and U?+

T_n: cos(n·arccos x), satisfies T_n(cos θ)=cos(nθ). U_n: sin((n+1)·arccos x)/sin(arccos x). T is used for approximation, U for derivatives of T: T_n'(x)=nU_{n-1}(x).

How are they used in spectral methods?+

Expand the solution as Σa_kT_k(x). The coefficients decay exponentially for smooth functions. Differentiation and integration in Chebyshev space are simple matrix operations. This gives 'spectral accuracy' — exponential convergence.

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