matlab代写代考

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml Stability Analysis In this livescript, you will learn how To apply stability analysis to determine the maximal time step that can be used to solve an initial value problem. Consider the linear initial value problem \frac{dx}{dt}=-100x with the initial condition x(0)=1 . Since the equation is linear, […]

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml Iterative Methods: Gauss-Seidel Method In this livescript, you will learn how To use the Gauss-Seidel method to solve systems of linear equations One issue with the Jacobi method is that because [N] is the matrix of all the off-diagonal elements, the updated values of x_{i} are not

ENGR20005 Numerical Methods in Engineering Workshop 10 Part A: MATLAB Livescripts 10.1 The livescript ENGR20005 Workshop10p1.mlx runs through the solution of initial value problems in MATLAB. (a) Read through the livescript and make sure you understand what each line of code does. (b) Modify the livescript to solve the the initial value problem dx =

clear all Npoints=50; alpha = 1; %BC1 y(-1)=1 beta = 0; %BC2 y(1)=0 %xpoints=linspace(-1,1,Npoints)’; [xpoints,w,P]=lglnodes(Npoints); xpoints=-xpoints; y = @(x) (exp(4*x)-sinh(4)*x-cosh(4))./16+1-0.5*(1+x); yexact = y(xpoints); n=length(xpoints)-1; %order of polynomial D=DerivMatrix(xpoints,n); D2 = D*D; r = exp(4*xpoints); Amatrix=D2(2:end-1,2:end-1); Cvector=r(2:end-1)-alpha*D2(2:end-1,1)-beta*D2(2:end-1,end); sol = Amatrix\Cvector; yapprox = [alpha;sol;beta]; hold off plot(xpoints,yapprox,’bo’) hold on plot(xpoints,yexact,’k-‘) xlabel(‘x’);ylabel(‘y’); function D=DerivMatrix(x,n) D=zeros(n+1,n+1); for i=1:n+1 num(i)=1.0;

close all clear all tmin=0.0; tmax=8.0; x0=[1 1]; Delta_t=0.2; [t,x]=MyEulerLinearSysODEs([tmin tmax],x0,Delta_t); [tmat,xmat]=ode23(@f,[0 8],[1 1]); hold off plot(t,x(:,2),’bo-‘,’linewidth’,2,’markersize’,10) hold on plot(tmat,xmat(:,2),’k-‘,’linewidth’,2) xlabel(‘t’); ylabel(‘x_1(t)’); legend(‘Implicit Euler’,’MATLAB ode23()’) function [t,x]=MyEulerLinearSysODEs(tspan,x0,Delta_t) a=tspan(1); b=tspan(2); t=a:Delta_t:b; x=zeros(length(t),numel(x0)); x(1,:)=x0; %setting initial conditions K=[-6 -3;5 2]; temp=inv(eye(2)-K*Delta_t); for n=1:length(t)-1 x(n+1,:)=(temp*x(n,:)’)’; end end function dxdt=f(t,x) dxdt=[-6*x(1)-3*x(2) ;5*x(1)+2*x(2)]; end

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml MATLAB Integration In this livescript, you will learn how To compute integrals using both the numeric and symbolic packages in MATLAB. Numeric Integration We’ll consider the integral of the function f(x)=\frac{1}{x} over the interval x\in[1,2] . (a) Compute the integral \int^{2}_{1}{f(x)dx} analytically. The simpliest way of doing

clear all xmatlab1 = fsolve(@NonlinearExample,[1;1]); options = optimoptions(@fsolve,’Display’,’iter’,’SpecifyObjectiveGradient’,true); [xmatlab2,F,exitflag,output,JAC] = fsolve(@NonlinearExampleWithJacobian,[1;1],options); x=[1;1] [f,J]=NonlinearExampleWithJacobian(x) while max(abs(f)) >1.0e-6 det=J(1,1)*J(2,2)-J(1,2)*J(2,1); x(1)=x(1)+(f(2)*J(1,2)-f(1)*J(2,2))/det; x(2)=x(2)+(f(1)*J(2,1)-f(2)*J(1,1))/det; [f,J]=NonlinearExampleWithJacobian(x) end function [f,J] = NonlinearExampleWithJacobian(x) f(1) = x(1)*x(1)+1-x(2); f(2) = 3*cos(x(1))-x(2); J(1,1)=2*x(1);J(1,2)=-1; J(2,1)=-3*sin(x(1));J(2,2)=-1 end function f = NonlinearExample(x) f(1) = x(1)*x(1)+1-x(2); f(2) = 3*cos(x(1))-x(2); end