Copied **Finite-Difference Methods** Consider the initial boundary value problem \[ v_t=\beta v_{xx},\qquad 0 Raw LaTeX {Finite-Difference Methods} Consider the initial boundary value problem \[ v_t=\beta v_{xx},\qquad 0<x<1,\qquad 0<t,\qquad v(x,0)=f(x) \] with Neumann boundary conditions \[ v_x(0,t)=0,\qquad v_x(1,t)=0. \] Suppose that the interval \([0,1]\) is partitioned by meshpoints \(x_j=jh\) for \(j=0,1,\ldots,N\) and \(h=1/N\). A possible discretization of the above problem uses the Crank-Nicolson scheme for the differential equation: \[ \left(I-\frac{\beta k}{2}D_+D_-\right)u_j^{n+1} = \left(I+\frac{\beta k}{2}D_+D_-\right)u_j^n \] for \(j=0,1,\ldots,N\), combined with the boundary condition discretizations: \[ \frac{u_1^n-u_{-1}^n}{h}=0 \qquad \text{and}\qquad \frac{u_{N+1}^n-u_{N-1}^n}{h}=0 \] for each time level \(n\). Initial data is obtained from by setting \(u_j^0=f_j\) for \(j=0,1,\ldots,N\). (Note that \(D_+\) and \(D_-\) are the usual forward and backward difference operators.) Set up the matrix system that must be solved in order to advance the numerical solution from time level \(n\) to time level \(n+1\). Show that the suggested discretization is stable in the \(l_2\) norm \[ \lVert u\rVert_2= \left(\sum_{j=0}^N h\lvert u_j\rvert^2\right)^{1/2} \] for \textit{all} choices of timestep \(k\) and spacestep \(h\). What other issues should be considered in deciding whether the suggested scheme is a good choice for the problem? \fact{1} A real symmetric \(n\times n\) matrix \(A\) can be diagonalized by an orthogonal similarity transformation, and \(A\)'s eigenvalues are real. \fact{2} The \((N-1)\times(N-1)\) matrix \(M\) defined by \[ m_{ij}= \begin{cases} 2, & \text{if } i=j,\\ -1, & \text{if } \lvert i-j\rvert=1,\\ 0, & \text{otherwise}, \end{cases} \] has eigenvalues \[ \mu_l=4\sin^2\left(\frac{\pi l}{2N}\right),\qquad l=1,2,\ldots,N-1. \] \fact{3} The \((N+1)\times(N+1)\) matrix: \[ \left[ \begin{array}{rrrrrrrrrrrr} -1 & 1 & 0 & 0 & 0 & \cdot & \cdot & \cdot & 0 & 0 & 0 & 0\\ 1 & -2 & 1 & 0 & 0 & \cdot & \cdot & \cdot & 0 & 0 & 0 & 0\\ 0 & 1 & -2 & 1 & 0 & \cdot & \cdot & \cdot & 0 & 0 & 0 & 0\\ 0 & 0 & 1 & -2 & 1 & \cdot & \cdot & \cdot & 0 & 0 & 0 & 0\\ \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot\\ \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot\\ \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot\\ \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot\\ \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot & \cdot\\ 0 & 0 & 0 & 0 & 0 & \cdot & \cdot & \cdot & 1 & -2 & 1 & 0\\ 0 & 0 & 0 & 0 & 0 & \cdot & \cdot & \cdot & 0 & 1 & -2 & 1\\ 0 & 0 & 0 & 0 & 0 & \cdot & \cdot & \cdot & 0 & 0 & 1 & -1 \end{array} \right] \] has eigenvalues \[ \mu_l=-4\sin^2\left(\frac{\pi l}{2(N+1)}\right),\qquad l=0,1,\ldots,N. \] Topic Tags Eigenvalues, Spectral Radius, and Gershgorin Theorem Finite Difference Boundary Value Problems Finite Difference Methods Numerical Differentiation and Finite Difference Operators