Skip to main content
Mathematics LibreTexts

Chapter 3: Hermite Interpolation

  • Page ID
    209035
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\dsum}{\displaystyle\sum\limits} \)

    \( \newcommand{\dint}{\displaystyle\int\limits} \)

    \( \newcommand{\dlim}{\displaystyle\lim\limits} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)

    The idea of Lagrange interpolation and \(\sigma \)-Lagrange interpolation can be generalized in various ways. Here, in this section we consider one simple extension where a polynomial \(p \)is required to take given values and derivative values at the interpolation points. For given \(\sigma \)-distinct points \(x_j \), \(j\in \{0, 1, \ldots, n\} \), and two sets of real numbers \(y_j \), \(z_j \), \(j\in \{0, 1, \ldots, n\} \), \(n\in \mathbb{N}_0 \), we need to find a polynomial \(p_{2n+1}\in \mathcal{P}_{2n+1} \)satisfying the conditions
    \[
    p_{2n+1}(x_j)=y_j,\quad p_{2n+1}^{\Delta}(x_j)=z_j,\quad j\in \{0, 1, \ldots, n\}.
    \notag\]
    The construction of such a polynomial is similar to that of the Lagrange interpolation polynomial and it is given in the following theorem.

    Theorem (Hermite Interpolation Theorem)

    Let \(n\in \mathbb{N}_0 \)and let \(a \), \(b\in \mathbb{T} \), \(a<b \), and \(x_j\in \mathbb{T} \), \(j\in \{0, 1, \ldots, n\} \), be \(\sigma \)-distinct and \(x_j \neq \sigma(x_{k}) \)for all \(j,k\in \{0,1, \ldots, n\} \).
    Let also, \(y_j, z_j \in \mathbb{R} \), \(j\in \{0, 1, \ldots, n\} \). Then there exists a unique polynomial \(p_{2n+1}\in \mathcal{P}_{2n+1} \)such that
    \begin{equation}
    \label{1.13} p_{2n+1}(x_j)=y_j,\quad p_{2n+1}^{\Delta}(x_j)=z_j,\quad j\in \{0, 1, \ldots, n\}.
    \end{equation}

    Proof


    For \(x\in [a, b] \), define the polynomial
    \[
    M_k(x)= \prod_{j=0, j\ne k}^n \frac{x-\sigma(x_j)}{x_k-\sigma(x_j)},\quad k\in \{0, 1, \ldots, n\}.
    \notag\]
    We have
    \[
    M_k(\sigma(x_j))=0,\quad M_k(x_k)=1,\quad j, k\in \{0, 1, \ldots, n\},\quad j\ne k.
    \notag\]
    We will search a polynomial \(p_{2n+1}\in \mathcal{P}_{2n+1} \)in the following form
    \[
    p_{2n+1}(x)= \sum_{j=0}^n \left(y_j+(x-x_j)(\alpha_j y_j+\beta_j z_j)\right)M_j(x) L_j(x),\quad x\in [a, b],
    \notag\]
    where \(\alpha_j \), \(\beta_j\in \mathbb{R} \), \(j\in \{0, 1, \ldots, n\} \), will be determined by the conditions \eqref{1.13}. We have
    \[\begin{aligned}
    p_{2n+1}(x_k)&=& \sum_{j=0}^n \left(y_j+(x_k-x_j)(\alpha_j y_j+\beta_j z_j)\right)M_j(x_k) L_j(x_k)\\ \\
    &=& y_k,\\ \\
    p_{2n+1}^{\Delta}(x)&=& \sum_{j=0}^n (\alpha_j y_j+\beta_j z_j)M_j(\sigma(x))L_j(\sigma(x))\\ \\
    &&+ \sum_{j=0}^n \left(y_j+(x-x_j)(\alpha_j y_j+\beta_j z_j)\right)\left(M_j^{\Delta}(x)L_j(x)+ M_j(\sigma(x))L_j^{\Delta}(x)\right),\\ \\
    p_{2n+1}^{\Delta}(x_k)&=& \sum_{j=0}^n (\alpha_j y_j+\beta_j z_j)M_j(\sigma(x_k))L_j(\sigma(x_k))\\ \\
    &&+\sum_{j=0}^n \left(y_j+(x_k-x_j)(\alpha_j y_j +\beta_j z_j)\right)\left(M_j^{\Delta}(x_k)L_j(x_k) +M_j(\sigma(x_k))L_j^{\Delta}(x_k)\right)\\ \\
    &=& (\alpha_k y_k+\beta_k z_k)M_k(\sigma(x_k))L_k(\sigma(x_k))\\ \\
    &&+ y_k\left(M_k^{\Delta}(x_k)+M_k(\sigma(x_k))L_k^{\Delta}(x_k)\right)\\ \\
    &=& z_k
    \end{aligned}\notag\]
    or
    \[
    \left(\alpha_k y_k+\beta_k z_k\right)M_k(\sigma(x_k))L_k(\sigma(x_k))=z_k -y_k\left(M_k^{\Delta}(x_k)+M_k(\sigma(x_k))L_k^{\Delta}(x_k)\right),
    \notag\]
    or
    \[
    \alpha_k y_k+\beta_k z_k= \frac{z_k -y_k\left(M_k^{\Delta}(x_k)+M_k(\sigma(x_k))L_k^{\Delta}(x_k)\right)}{M_k(\sigma(x_k))L_k(\sigma(x_k))},
    \notag\]
    and
    \[\begin{aligned}
    p_{2n+1}(x)&=& \sum_{j=0}^n \left(y_j+\frac{z_j-y_j\left(M_j^{\Delta}(x_j)+M_j(\sigma(x_j))L_j^{\Delta}(x_j)\right)}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\right)M_j(x)L_j(x)\\ \\
    &=& \sum_{j=0}^n \bigg( \left(1-\frac{M_j^{\Delta}(x_j)+M_j(\sigma(x_j))L_j^{\Delta}(x_j)}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\right)y_j\\ \\
    &&+ \frac{z_j}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\bigg) M_j(x) L_j(x),
    \end{aligned}\notag\]
    \(x\in [a, b] \). Now, suppose that there are two polynomials such that \(p_{2n+1}, q_{2n+1}\in \mathcal{P}_{2n+1} \)and
    \[
    p_{2n+1}(x_k)=q_{2n+1}(x_k)=y_k,\quad p_{2n+1}^{\Delta}(x_k)=q_{2n+1}^{\Delta}(x_k)=z_k,\quad k\in \{0, 1, \ldots, n\}.
    \notag\]
    Let \(h_{2n+1}=p_{2n+1}-q_{2n+1} \). Then \(h_{2n+1}\in \mathcal{P}_{2n+1} \)and it has at least \(2n+2 \)GZs. Thus,
    \[
    h_{2n+1}\equiv 0\quad \text{or}\quad p_{2n+1}\equiv q_{2n+1}\quad \text{on}\quad [a, b].
    \notag\]
    This completes the proof.


    Next, we give the definition and the general structure of a Hermite interpolation polynomial.

    Definition:

    Let \(n\in \mathbb{N}_0 \)and let \(a \), \(b\in \mathbb{T} \), \(a<b \), and \(x_j\in \mathbb{T} \), \(j\in \{0, 1, \ldots, n\} \), be \(\sigma \)-distinct.
    Let also, \(y_j, z_j \in \mathbb{R} \), \(j\in \{0, 1, \ldots, n\} \). Then the polynomial
    \[\begin{aligned}
    p_{2n+1}(x)&=& \sum_{j=0}^n \bigg( \left(1-\frac{M_j^{\Delta}(x_j)+M_j(\sigma(x_j))L_j^{\Delta}(x_j)}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\right)y_j\\ \\
    &&+ \frac{z_j}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\bigg) M_j(x) L_j(x),
    \end{aligned}\notag\]
    \(x\in [a, b] \), is called Hermite interpolation polynomial for the set of values given in
    \[
    \{(x_j, y_j, z_j): j\in \{0, 1, \ldots, n\}\}.
    \notag\]

    In the next remark we prove that the Hermite interpolation polynomial reduces to the classical Hermite interpolation polynomial whenever the time scale is the set of real numbers.

    Note

    If \(\mathbb{T}=\mathbb{R} \), then
    \[\begin{aligned}
    M_j^{\Delta}(x_j)&=& M_j^{\prime}(x_j)\\ \\
    &=& L_j^{\prime}(x_j),\\ \\
    L_j^{\Delta}(x_j)&=& L_j^{\prime}(x_j),\\ \\
    M_j(\sigma(x_j))&=& L_j(\sigma(x_j))\\ \\
    &=& M_j(x_j)\\ \\
    &=& L_j(x_j)\\ \\
    &=& 1,\quad j\in \{0, 1, \ldots, n\}.
    \end{aligned}\notag\]
    Hence,
    \[
    p_{2n+1}(x)= \sum_{j=0}^n\left(\left(1- 2L_j^{\prime}(x_j)(x-x_j)\right)y_j+z_j(x-x_j)\right)(L_j(x))^2,
    \notag\]
    \(x\in [a, b] \). Thus, we get the classical Hermite interpolation polynomial.

    Example

    Let \(\mathbb{T}=2^{\mathbb{N}_0} \),
    \[\begin{aligned}
    x_0&=& 1,\quad x_1= 4,\\ \\
    y_0&=& 1,\quad y_1=-1,\\ \\
    z_0&=& 1,\quad z_1=2.
    \end{aligned}\notag\]
    Here \(n=1 \), \(\sigma(x)=2x \), \(x\in \mathbb{T} \). Then
    \[\begin{aligned}
    L_0(x)&=& \frac{x-x_1}{x_0-x_1}= -\frac{1}{3}(x-4),\\ \\
    L_0^{\Delta}(x)&=& -\frac{1}{3},\\ \\
    L_0(\sigma(x_0))&=& L_0(2)\ \frac{2}{3},\\ \\
    L_1(x)&=& \frac{x-x_0}{x_1-x_0}=\frac{1}{3}(x-1),\\ \\
    L_1^{\Delta}(x)&=& \frac{1}{3},\quad x\in \mathbb{T},\\ \\
    L_1(\sigma(x_1))&=& L_1(8)=\frac{7}{3},\\ \\
    M_0(x)&=& \frac{x-\sigma(x_1)}{x_0-\sigma(x_1)}= -\frac{1}{7}(x-8),\\ \\
    M_0^{\Delta}(x)&=& -\frac{1}{7},\\ \\
    M_0(\sigma(x_0))&=& M_0(2)=\frac{6}{7},\\ \\
    M_1(x)&=& \frac{x-\sigma(x_0)}{x_1-\sigma(x_0)}= \frac{1}{2}(x-2),\\ \\
    M_1^{\Delta}(x)&=& \frac{1}{2},\\ \\
    M_1(\sigma(x_1))&=& M_1(8)=3, \quad x\in \mathbb{T}.
    \end{aligned}\notag\]
    Hence,
    \[\begin{aligned}
    p_3(x)&=& \left(y_0+\frac{z_0-y_0\left(M_0^{\Delta}(x_0)+M_0(\sigma(x_0))L_0^{\Delta}(x_0)\right)}{M_0(\sigma(x_0))L_0(\sigma(x_0))}(x-x_0)\right)M_0(x)L_0(x)\\ \\
    &&+ \left(y_1+\frac{z_1-y_1\left(M_1^{\Delta}(x_1)+M_1(\sigma(x_1))L_1^{\Delta}(x_1)\right)}{M_1(\sigma(x_1))L_1(\sigma(x_1))}(x-x_1)\right)M_1(x)L_1(x)\\ \\
    &=& \left(1+\frac{1-\left(-\frac{1}{7}+\frac{6}{7}\left(-\frac{1}{3}\right)\right)}{\frac{6}{7}\cdot \frac{2}{3}}(x-1)\right)\left(-\frac{1}{7}(x-8)\right)\left(-\frac{1}{3}(x-4)\right)\\ \\
    &&+ \left(-1 +\frac{2+\left(\frac{1}{2}+3\cdot \frac{1}{3}\right)}{3\cdot \frac{7}{3}}(x-4)\right)\left(\frac{1}{2}(x-2)\right)\left(\frac{1}{3}(x-1)\right)\\ \\
    &=& \frac{1}{21}\left(1+\frac{5}{2}(x-1)\right)(x-4)(x-8)+ \frac{1}{6}\left(-1+\frac{1}{2}(x-4)\right)(x-1)(x-2)\\ \\
    &=& \frac{1}{42}(5x-3)(x-4)(x-8)+ \frac{1}{12}(x-6)(x-1)(x-2),\quad x\in [1, 8].
    \end{aligned}\notag\]

    Exercise

    Let \(\mathbb{T}=2\mathbb{Z} \). Find the Hermite interpolation polynomial for the set
    \[\begin{aligned}
    x_0&=& -4,\quad x_1=0,\quad x_2=4,\\ \\
    y_0&=& -1,\quad y_1=1,\quad y_2=-1,\\ \\
    z_0&=& 1,\quad z_1=-1,\quad z_2=1.
    \end{aligned}\notag\]

    In some applications, instead of a data set of points, function values and delta derivative values at these points a function itself may be given.
    The Hermite polynomial for this function, that is, the polynomial which takes the same values as the function and whose delta derivative takes the values of the delta derivative of the function at given points is defined below.

    Definition:

    Let \(n\in \mathbb{N}_0 \)and let \(a \), \(b\in \mathbb{T} \), \(a<b \), and \(x_j\in \mathbb{T} \), \(j\in \{0, 1, \ldots, n\} \), be \(\sigma \)-distinct.
    Let also, \(f: [a, b]\to \mathbb{R} \)be delta differentiable on \([a, b] \). Then the polynomial
    \[\begin{aligned}
    p_{2n+1}(x)&=& \sum_{j=0}^n \bigg( \left(1-\frac{M_j^{\Delta}(x_j)+M_j(\sigma(x_j))L_j^{\Delta}(x_j)}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\right)f(x_j)\\ \\
    &&+ \frac{f^{\Delta}(x_j)}{M_j(\sigma(x_j))L_j(\sigma(x_j))}(x-x_j)\bigg) M_j(x) L_j(x),
    \end{aligned}\notag\]
    \(x\in [a, b] \), is called Hermite interpolation polynomial for the function \(f \).

    Exercise

    Let \(\mathbb{T}=3^{\mathbb{N}_0} \), \(f: \mathbb{T}\to \mathbb{R} \)is given by
    \[
    f(x)= x^3+3x^2+e_2(x, 1),\quad x\in \mathbb{T}.
    \notag\]
    Let also,
    \[
    x_0=1,\quad x_1=9,\quad x_2=81.
    \notag\]
    Find the Hermite interpolation polynomial for the function \(f \).

    Let \(n\in \mathbb{N}_0 \)and \(a \), \(b\in \mathbb{T} \), \(a<b \), and \(x_j\in \mathbb{T} \), \(j\in \{0, 1, \ldots, n\} \), be \(\sigma \)-distinct.
    Define the polynomials
    \[
    \zeta_n(x)= \prod_{j=0}^n(x-\sigma(x_j)), \quad x\in [a, b].
    \notag\]

    The error in the Hermite interpolation is given in the following theorem.

    Theorem

    Suppose that \(n\in \mathbb{N}_0 \), \(a, b\in \mathbb{T} \), \(a<b \), \(x_j\in [a, b] \), \(j\in \{0, 1, \ldots, n\} \), are \(\sigma \)-distinct and \(f: [a, b]\to \mathbb{R} \), \(f^{\Delta^k}(x) \)exist for any \(x\in [a, b] \)and for any \(k\in \{1, \ldots, 2n+2\} \). Then for any \(x\in [a, b] \)there exists \(\xi=\xi(x)\in (a, b) \)such that
    \[
    f(x)-p_{2n+1}(x)= \frac{f^{\Delta^{2n+2}}(\xi)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi), \quad x\in [a, b]
    \notag\]
    or
    \[
    G_{min, 2n+2}(\xi)\leq \frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\leq G_{max, 2n+2}(\xi), \quad x\in [a, b]
    \notag\]
    where
    \[\begin{aligned}
    G_{max, 2n+2}(\xi)&=& \max\left\{ \frac{f^{\Delta^{2n+2}}(\xi)}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)}, \frac{f^{\Delta^{2n+2}}(\rho(\xi))}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))}\right\},\\ \\
    G_{min, 2n+2}(\xi)&=& \min\left\{ \frac{f^{\Delta^{2n+2}}(\xi)}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)}, \frac{f^{\Delta^{2n+2}}(\rho(\xi))}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))}\right\}.
    \end{aligned}\notag\]

    Proof


    Let \(p_{2n+1} \)be the Hermite interpolation polynomial for the function \(f \)with interpolation points \(x_j \), \(j\in \{0, 1, \ldots, n\} \). Define the function
    \[
    \psi(t)= f(t)-p_{2n+1}(t)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\pi_{n+1}(t)\zeta_{n+1}(t),\quad t\in [a, b].
    \notag\]
    Then
    \[\begin{aligned}
    \psi(x_j)&=& f(x_j)-p_{2n+1}(x_j)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\pi_{n+1}(x_j)\zeta_{n+1}(x_j)\\ \\
    &=& f(x_j)-f(x_j)\\ \\
    &=& 0, \quad x\in [a, b], \quad j\in \{0, 1, \ldots, n\},
    \end{aligned}\notag\]
    and \(\psi(x)=0, \quad x\in [a, b] \). Thus, \(\psi: [a, b]\to \mathbb{R} \)has at least \(n+2 \)GZs. Hence and the Rolle theorem (see the Appendix of this book), it follows that \(\psi^{\Delta} \)has at least \(n+1 \)GZs on \((a, b) \)that do not coincide with \(x_j \), \(j\in \{0, 1, \ldots, n\} \). Next,
    \[\begin{aligned}
    \psi^{\Delta}(t)&=& f^{\Delta}(t)- p_{2n+1}^{\Delta}(t)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}(\pi_{n+1}\zeta_{n+1})^{\Delta}(t)\\ \\
    &=& f^{\Delta}(t)-p_{2n+1}^{\Delta}(t)\\ \\
    &&- \frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}^{\Delta}(t)\zeta_{n+1}(\sigma(t))+\pi_{n+1}(t)\zeta_{n+1}^{\Delta}(t)\right),
    \end{aligned}\notag\]
    \(t\in [a, b] \). Hence,
    \[\begin{aligned}
    \psi^{\Delta}(x_j)&=& f^{\Delta}(x_j)-p_{2n+1}^{\Delta}(x_j)\\ \\
    &&-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}^{\Delta}(x_j) \zeta_{n+1}(\sigma(x_j))+\pi_{n+1}(x_j)\zeta_{n+1}^{\Delta}(x_j)\right)\\ \\
    &=& 0,\quad j\in \{0, 1, \ldots, n\},
    \end{aligned}\notag\]
    i.e., \(\psi^{\Delta} \)has at least \(n+1 \)GZs at \(x_j \), \(j\in \{0, 1, \ldots, n\} \). Therefore \(\psi^{\Delta} \)has at least \(2n+2 \)GZs in \([a, b] \).
    Then \(\psi^{\Delta^{2n+2}} \)has at least one GZ in \((a, b) \)and there exists a \(\xi=\xi(x)\in (a, b) \)such that
    \[
    \psi^{\Delta^{2n+2}}(\xi)=0\quad \text{or}\quad \psi^{\Delta^{2n+2}}(\rho(\xi))\psi^{\Delta^{2n+2}}(\xi)<0.
    \notag\]
    Observe that
    \[
    \psi^{\Delta^{2n+2}}(t)= f^{\Delta^{2n+2}}(t)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(t),\quad t\in [a, b].
    \notag\]

    1. Let \(\psi^{\Delta^{2n+2}}(\xi)=0 \). Then
    \[
    f^{\Delta^{2n+2}}(\xi)= \frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi), \quad x\in [a, b],
    \notag\]
    or
    \[\begin{aligned}
    f(x)-p_{2n+1}(x)&=& \frac{f^{\Delta^{2n+2}}(\xi)}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)}\pi_{n+1}(x)\zeta_{n+1}(x), \quad x\in [a, b].
    \end{aligned}\notag\]

    2. Let
    \[
    \psi^{\Delta^{2n+2}}(\rho(\xi))\psi^{\Delta^{2n+2}}(\xi)<0.
    \notag\]
    Then
    \[\begin{aligned}
    \psi^{\Delta^{2n+2}}(\rho(\xi))&=& f^{\Delta^{2n+2}}(\rho(\xi))-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi)), \quad x\in [a, b],
    \end{aligned}\notag\]
    and
    \[
    \psi^{\Delta^{2n+2}}(\xi)= f^{\Delta^{2n+2}}(\xi)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi,
    \quad x\in [a, b].
    \notag\]
    Then we have,
    \[\begin{aligned}
    0&>& \psi^{\Delta^{2n+2}}(\rho(\xi))\psi^{\Delta^{2n+2}}(\xi)\\ \\
    &=& \left(f^{\Delta^{2n+2}}(\rho(\xi))-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))\right)\\ \\
    &&\times \left(f^{\Delta^{2n+2}}(\xi)-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)\right)\\ \\
    &=& \left(\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\right)^2 \left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))\\ \\
    &&-\frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\left(\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))
    f^{\Delta^{2n+2}}(\xi)+\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi) f^{\Delta^{2n+2}}(\rho(\xi))\right)\\ \\
    &&+f^{\Delta^{2n+2}}(\rho(\xi))f^{\Delta^{2n+2}}(\xi), \quad x\in [a, b].
    \end{aligned}\notag\]
    Hence,
    \[
    G_{min, 2n+2}(\xi)\leq \frac{f(x)-p_{2n+1}(x)}{\pi_{n+1}(x)\zeta_{n+1}(x)}\leq G_{max, 2n+2}(\xi), \quad x\in [a, b].
    \notag\]
    This completes the proof.

    Note

    Suppose that all conditions of Theorem \ref{theorem1.15} hold. If
    \[
    \lim_{n\to\infty} \max_{x\in [a, b]}\left(\frac{f^{\Delta^{2n+2}}(\xi)}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\xi)}\pi_{n+1}(x)\zeta_{n+1}(x)\right)=0
    \notag\]
    and
    \[
    \lim_{n\to\infty} \max_{x\in [a, b]}\left(\frac{f^{\Delta^{2n+2}}(\rho(\xi))}{\left(\pi_{n+1}\zeta_{n+1}\right)^{\Delta^{2n+2}}(\rho(\xi))}\pi_{n+1}(x)\zeta_{n+1}(x)\right)=0,
    \notag\]
    then
    \[
    \lim_{n\to\infty}\max_{x\in [a, b]}|f(x)-p_{2n+1}(x)|=0.
    \notag\]

    In the following example we compute the Hermite polynomial for a given function and compare it graphically with the function.

    Example

    Consider the time scale \(\mathbb{T}=2\mathbb{Z}=\{\ldots, -4,-2,0,2,4,\ldots\} \).
    Let \(a=x_0=-4, x_1=0,x_2=4, b=6 \)and \(f(x)=\displaystyle \frac{1}{x^2+1}, \quad x\in \mathbb{T} \).

    We will compute the Hermite polynomial \(p_5(x) \)and compare the graphs of \(p_5 \)and \(f \)as well as the graphs of
    \(p_5^{\Delta} \)and \(f^{\Delta} \).

    For this time scale we have \(\sigma(x)=x+2, \quad x\in \mathbb{T} \), so that \(\sigma(x_0)=-2 \), \(\sigma(x_1)=2 \)and \(\sigma(x_2)=6 \).
    We have
    \[\begin{aligned}
    f^\Delta(x)&=&\displaystyle \frac{\frac{1}{(x+2)^2+1}- \frac{1}{x^2+1}}{x+2-x}\\
    &=& \displaystyle -\frac{2x+2}{(x^2+1)(x^2+4x+5)}, \quad x\in \mathbb{T}.
    \end{aligned}\notag\]
    Hence,
    \[\begin{aligned}
    y_0&=&f(x_0)=f(-4)=\frac{1}{17},\\
    y_1&=&f(x_1)=f(0)=1,\\
    y_2&=&f(x_2)=f(4)= \frac{1}{17},\\
    z_0&=&f^\Delta(x_0)=f^\Delta(-4)=\frac{6}{85},\\
    z_1&=&f^\Delta(x_1)=f^\Delta(0)=-\frac{2}{5},\\
    z_2&=&f^\Delta(x_2)=f^\Delta(4)=-\frac{10}{629}.\\
    \end{aligned}\notag\]
    First we compute \(L_i \)and \(M_i \)for \(i=0,1,2 \).
    \[\begin{aligned}
    L_0(x)&=&\displaystyle \frac{(x-0)(x-4)}{(-4-0)(-4-4)}\\
    &=&\frac{1}{32}(x^2-4x),\\
    L_1(x)&=&\displaystyle \frac{(x+4)(x-4)}{(0+4)(0-4)}\\
    &=&-\frac{1}{16}(x^2-16),\\
    L_2(x)&=&\displaystyle \frac{(x+4)(x-0)}{(4+4)(4-0)}\\
    &=&\frac{1}{32}(x^2+4x),
    \end{aligned}\notag\]
    \[\begin{aligned}
    M_0(x)&=&\displaystyle \frac{(x-\sigma(0))(x-\sigma(4))}{(-4-\sigma(0))(-4-\sigma(4))}\\
    &=&\displaystyle \frac{(x-2)(x-6)}{(-4-2)(-4-6)}=\frac{1}{60}(x^2-8x+12),\\
    M_1(x)&=&\displaystyle \frac{(x-\sigma(-4))(x-\sigma(4))}{(0-\sigma(-4))(0-\sigma(4))}\\
    &=& \displaystyle\frac{(x+2)(x-6)}{(0+2)(0-6)}=-\frac{1}{12}(x^2-4x-12),\\
    M_2(x)&=&\displaystyle \frac{(x-\sigma(-4))(x-\sigma(0))}{(4-\sigma(-4))(4-\sigma(0))}\\
    &=& \displaystyle \frac{(x+2)(x-2)}{(4+2)(4-2)}=\frac{1}{12}(x^2-4), \quad x\in \mathbb{T}.
    \end{aligned}\notag\]
    Let
    \[
    h(x)=x^2,\quad x\in \mathbb{T}.
    \notag\]
    Then,
    \[
    (h(x))^\Delta=x+\sigma(x)=x+x+2=2x+2,\quad x\in \mathbb{T}.
    \notag\]
    We compute \(L_i^\Delta \)and \(M_i^\Delta \)for \(i=0,1,2 \).
    \[\begin{aligned}
    L_0^\Delta (x)&=&\displaystyle \frac{1}{32}(2x+2-4)=\frac{1}{16}(x-1),\\
    L_1^\Delta (x)&=&\displaystyle -\frac{1}{16}(2x+2)=-\frac{1}{8}(x+1),\\
    L_2^\Delta (x)&=&\displaystyle \frac{1}{32}(2x+2+4)=\frac{1}{16}(x+3),
    \end{aligned}\notag\]
    \[\begin{aligned}
    M_0^\Delta (x)&=&\displaystyle =\frac{1}{60}(2x+2-8)=\frac{1}{30}(x-3),\\
    M_1^\Delta (x)&=&\displaystyle =-\frac{1}{12}(2x+2-4)=-\frac{1}{6}(x-1),\\
    M_2^\Delta (x)&=&\displaystyle =\frac{1}{12}(2x+2)=\frac{1}{6}(x+1),\quad x\in \mathbb{T}.
    \end{aligned}\notag\]
    The values involved in the Hermite polynomial are computed as
    \[\begin{aligned}
    L_0(\sigma(x_0))&=& L_0(-2)=\frac{1}{32}(4+8)=\frac{3}{8},\\
    L_0^\Delta(x_0)&=& L_0^\Delta(-4)=\frac{1}{16}(-4-1)=-\frac{5}{16},\\
    L_1(\sigma(x_1))&=& L_1(2)=-\frac{1}{16}(4-16)=\frac{3}{4},\\
    L_1^\Delta(x_1)&=& L_1^\Delta(0)=-\frac{1}{8}(0+1)=-\frac{1}{8},\\
    L_2(\sigma(x_2))&=& L_2(6)=\frac{1}{32}(36+24)=\frac{15}{8},\\
    L_2^\Delta(x_2)&=& L_2^\Delta(4)=\frac{1}{16}(4+3)=\frac{7}{16}, \quad x\in \mathbb{T}.\\
    \end{aligned}\notag\]
    and
    \[\begin{aligned}
    M_0(\sigma(x_0))&=& M_0(-2)=\frac{1}{60}(4+16+12)=\frac{8}{15},\\
    M_0^\Delta(x_0)&=& M_0^\Delta(-4)=\frac{1}{30}(-4-3)=-\frac{7}{30},\\
    M_1(\sigma(x_1))&=& M_1(2)=-\frac{1}{12}(4-8-12)=\frac{4}{3},\\
    M_1^\Delta(x_1)&=& M_1^\Delta(0)=-\frac{1}{6}(0-1)=\frac{1}{6},\\
    M_2(\sigma(x_2))&=& M_2(6)=\frac{1}{12}(36-4)=\frac{8}{3},\\
    M_2^\Delta(x_2)&=& M_2^\Delta(4)=\frac{1}{6}(4+1)=\frac{5}{6}, \quad x\in \mathbb{T}.
    \end{aligned}\notag\]
    The the Hermite polynomial \(p_5(x) \)is computed as
    \[\begin{aligned}
    p_5(x)&=&\left[\left(1-\frac{-\frac{7}{30}-\frac{8}{15}.\frac{5}{16}}{\frac{8}{15}\cdot\frac{3}{8}}(x+4)\right).\frac{1}{17}
    +\frac{\frac{6}{85}}{\frac{8}{15}\cdot\frac{3}{8}}(x+4)\right]L_0(x)M_0(x)\\
    &+& \left[\left(1-\frac{\frac{1}{6}-\frac{4}{3}\cdot\frac{1}{8}}{\frac{4}{3}\cdot\frac{3}{4}}(x-0)\right).1
    +\frac{-\frac{2}{5}}{\frac{4}{3}.\frac{3}{4}}(x-0)\right]L_1(x)M_1(x)\\
    &+&\left[\left(1-\frac{\frac{5}{6}+\frac{8}{3}\cdot\frac{7}{16}}{\frac{8}{3}\cdot\frac{15}{8}}(x-4)\right).\frac{1}{17}
    +\frac{-\frac{10}{629}}{\frac{8}{3}\cdot\frac{15}{8}}(x-4)\right]L_2(x)M_2(x), \quad x\in \mathbb{T},
    \end{aligned}\notag\]
    which after simplifying becomes
    \[\begin{aligned}
    p_5(x)&=&\left[\frac{1}{17}+\frac{8}{17}(x+4)\right]\frac{1}{32}(x^2-4x)\frac{1}{60}(x^2-8x+12)\\
    &+& \left[1-\frac{2}{5}x \right] \frac{1}{16}(x^2-16)\frac{1}{12}(x^2-4x-12)\\
    &+&\left[\frac{1}{17}-\frac{84}{3145}(x-4)\right]\frac{1}{32}(x^2+4x)\frac{1}{12}(x^2-4), \quad x\in \mathbb{T}.
    \end{aligned}\notag\]


    This page titled Chapter 3: Hermite Interpolation is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Svetlin G. Georgiev.

    • Was this article helpful?