The GMRES algorithm and its matlab implementation are supposed to solve linear equations system, such as
%Ax = b
A = rand(4);
b = rand(4,1);
x = gmres(A,b);
One can also use a function handle
foo = #(x) A*x + conj(A)*5*x;
y = gmres(foo,b);
What I want is to solve the following
B = rand(4);
H = rand(4);
foo2 = H*B + B*H;
X = gmres(foo2, B) %Will not run!
--Error using gmres (line 94)
--Right hand side must be a column vector of length 30 to match the coefficient matrix.
Mathematically speaking I don't see why gmres couldn't apply to this problem as well.
Note: What I'm really trying to solve is an implicit euler method for a PDE dB/dt = B_xx + B_yy, so H is in fact a second derivative matrix using finite difference.
Thank you
Amir
If I've understood right you want to use GMRES to solve an a sylvester equation
A*X + X*A = C
for n-by-n matrices A, X and C.
(I asked a related question yesterday over at SciComp and got this great answer.)
To use GMRES you can express this matrix-matrix equation as a size n^2 matrix-vector equation. For convenience we can use the Kronecker product, implemented in MATLAB with kron:
A = randn(5);
X = randi(3,[5 5]);
C = A*X + X*A;
% Use the Kronecker product to form an n^2-by-n^2 matrix
% A*X + X*A
bigA = (kron(eye(5),A) + kron(A.',eye(5)));
% Quick check that we're getting the same answer
norm(bigA*X(:) - C(:))
% Use GMRES to calculate X from A and C.
vec_X_gmres = gmres(bigA,C(:));
X_gmres = reshape(vec_X_gmres,5,5);
Related
I'm trying to numerically find the solution to A*cos x +B*sin x = C where A and B are two known square matrices of the same size (for example 100x100), and C is a known vector (100x1).
Without the second term (i.e. with a single matrix), I will use Jacobi or Gauss-Seidel to solve this problem and get x but here, I don't see how to proceed to solve this problem in Matlab.
May be, it would be useful to solve the problem as : A*X + B*sqrt(1-X^2) = C.
I would greatly appreciate any help, ideas or advices
Thanks in advance
If I understood you correctly, you could use fsolve like this (c and X are vectors):
A = ones(2,2);
B = ones(2,2);
c = ones(2,1);
% initial point
x0 = ones(length(A), 1);
% call to fsolve
sol = fsolve(#(x) A * cos(x) + B*sin(x) - c, x0);
Here, we solve the nonlinear equation system F(x) = 0 with F: R^N -> R^N and F(x) = A * cos(x) + B*sin(x) - c.
Only for the sake of completeness, here's my previous answer, i.e. how one could do it in case C and X are matrices instead of vectors:
A = ones(2,2);
B = ones(2,2);
C = ones(2,2);
% initial point
x0 = ones(numel(A), 1);
% call to fsolve
fsolve(#(x) fun(x, A, B, C), x0)
function [y] = fun(x, A, B, C)
% Transform the input vector x into a matrix
X = reshape(x, size(A));
% Evaluate the matrix equation
Y = A * cos(X) + B*sin(X) - C;
% flatten the matrix Y to a row vector y
y = reshape(Y, [], 1);
end
Here, the idea is to transform the matrix equation system F: R^(N x N) -> R^(N x N) into a equivalent nonlinear system F: R^(N*N) -> R^(N*N).
I have an equation like this:
dy/dx = a(x)*y + b
where a(x) is a non-constant (a=1/x) and b is a vector (10000 rows).
How can I solve this equation?
Let me assume you would like to write a generic numerical solver for dy/dx = a(x)*y + b. Then you can pass the function a(x) as an argument to the right-hand side function of one of the ODE solvers. e.g.
a = #(x) 1/x;
xdomain = [1 10];
b = rand(10000,1);
y0 = ones(10000,1);
[x,y] = ode45(#(x,y,a,b)a(x)*y + b,xdomain,y0,[],a,b);
plot(x,y)
Here, I've specified the domain of x as xdomain, and the value of y at the bottom limit of x as y0.
From my comments, you can solve this without MATLAB. Assuming non-zero x, you can use an integrating factor to get a 10000-by-1 solution y(x)
y_i(x) = b_i*x*ln(x) + c_i*x
with 10000-by-1 vector of constants c, where y_i(x), b_i and c_i are the i-th entries of y(x), b and c respectively. The constant vector c can be determined at some point x0 as
c_i = y_i(x0)/x_0 - b_i*ln(x0)
After finding equations of motion using the Symbolic Toolbox (R2016b, Windows), I have the following form:
M(q)*qddot = b(q,qdot) + u
M and b were found using equationsToMatrix.
Now, I need to separate b into Coriolis and Potential terms such that
M(q)*qddot + C(q,qdot)*qdot + G(q) = u
It would be extremely convenient if I could apply
[C,G] = equationsToMatrix(b,qdot)
but unfortunately it will not factor out qdot when b is nonlinear. I do not care (and in fact it is necessary) that C is a function of q and qdot, even after factoring out the vector qdot. I have tried coeffs and factor without results.
Thanks.
Posting my own solution so there can be at least one answer...
This function works, but it is not heavily tested. It works exactly as I suggested in my original question. Feel free to rename it so it doesn't conflict with the MATLAB builtin.
function [A,b] = equationsToMatrix( eq, x )
%EQUATIONSTOMATRIX equationsToMatrix for nonlinear equations
% factors out the vector x from eq such that eq = Ax + b
% eq does not need to be linear in x
% eq must be a vector of equations, and x must be a vector of symbols
assert(isa(eq,'sym'), 'Equations must be symbolic')
assert(isa(x,'sym'), 'Vector x must be symbolic')
n = numel(eq);
m = numel(x);
A = repmat(sym(0),n,m);
for i = 1:n % loop through equations
[c,p] = coeffs(eq(i),x); % separate equation into coefficients and powers of x(1)...x(n)
for k = 1:numel(p) % loop through found powers/coefficients
for j = 1:m % loop through x(1)...x(n)
if has(p(k),x(j))
% transfer term c(k)*p(k) into A, factoring out x(j)
A(i,j) = A(i,j) + c(k)*p(k)/x(j);
break % move on to next term c(k+1), p(k+1)
end
end
end
end
b = simplify(eq - A*x,'ignoreanalyticconstraints',true); % makes sure to fully cancel terms
end
I'm trying to plot the boundaries of Arnold tongues (the regions were periodic solutions exist) for the circle map, f(x) = 2x + a + b*sin(2*pi*x)/pi mod 1. These are defined when f^n(x)=x and d/dx(f^n(x)) = 1, where f^n(x) represents iterating the function n times, i.e. f^2(x) = f(f(x)), n is the period of the periodic point.
I would like to able to take the two equations and write an equation for the boundary of the Arnold tongues in terms of b, so I will get x = g(b) and a = h(b) which satisfies the equation. I then want to plot a against b.
Analytically I can solve this in this way for the period 1 boundary by rearrange d/dx(f(x)) = 1 for x which gives x in terms of b then substituting this value into f(x) = x to give a in terms of b. I've also managed to do this in MATLAB using symbolic equations in the following way.
clear;
syms x a b
f = 2*x + a + (b/pi)*sin(2*pi*x);
g = diff(f,x);
solx = solve(g==1,x);
fnox = subs(f,x,solx);
solb(1) = solve(fnox(1)==solx(1), a);
solb(2) = solve(fnox(2)==solx(2),a);
[xval1,yval1] = fplot(matlabFunction(solb(1)),[0 1]);
[xval2,yval2] = fplot(matlabFunction(solb(2)),[0 1]);
A1 = [xval1,yval1];
A2 = [xval2,yval2];
A1 = A1(imag(A1(:,2))==0,:);
A2 = A2(imag(A2(:,2))==0,:);
figure(1)
hold on;
plot(A1(:,2),A1(:,1),'b')
plot(A2(:,2),A2(:,1),'b')
hold off;
The question is this, is there a way for me to solve for period 2 or higher boundary? I've tried the following,
f2 = subs(f,x,f)
g2 = diff(f2,x)
solx2 = solve(g2==1,x);
However I get a 'cannot find explicit solution' warning. I think perhaps the equation is too complicated for MATLAB to solve symbolically. Is there a way I can get it to work using symbolic equations? If not is there a suitable numeric method to perform the above?
Any help is much appreciated, thanks in advance.
We were asked to define our own differential operators on MATLAB, and I did it following a series of steps, and then we should use the differential operators to solve a boundary value problem:
-y'' + 2y' - y = x, y(0) = y(1) =0
my code was as follows, it was used to compute this (first and second derivative)
h = 2;
x = 2:h:50;
y = x.^2 ;
n=length(x);
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
% the code above creates the upper and lower shift matrix
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2); %second differential operator
d1= D*y.'
d2= ((D2)*y.')
then I changed it to this after posting it here and getting one response that encouraged the usage of Identity Matrix, however I still seem to be getting no where.
h = 2;
n=10;
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2); %second differential operator
I= eye(n);
eqn=(-D2 + 2*D - I)*y == x
solve(eqn,y)
I am not sure how to proceed with this, like should I define y and x, or what exactly? I am clueless!
Because this is a numerical approximation to the solution of the ODE, you are seeking to find a numerical vector that is representative of the solution to this ODE from time x=0 to x=1. This means that your boundary conditions make it so that the solution is only valid between 0 and 1.
Also this is now the reverse problem. In the previous post we did together, you know what the input vector was, and doing a matrix-vector multiplication produced the output derivative operation on that input vector. Now, you are given the output of the derivative and you are now seeking what the original input was. This now involves solving a linear system of equations.
Essentially, your problem is now this:
YX = F
Y are the coefficients from the matrix derivative operators that you derived, which is a n x n matrix, X would be the solution to the ODE, which is a n x 1 vector and F would be the function you are associating the ODE with, also a n x 1 vector. In our case, that would be x. To find Y, you've pretty much done that already in your code. You simply take each matrix operator (first and second derivative) and you add them together with the proper signs and scales to respect the left-hand side of the ODE. BTW, your first derivative and second derivative matrices are correct. What's left is adding the -y term to the mix, and that is accomplished by -eye(n) as you have found out in your code.
Once you formulate your Y and F, you can use the mldivide or \ operator and solve for X and get the solution to this linear system via:
X = Y \ F;
The above essentially solves the linear system of equations formed by Y and F and will be stored in X.
The first thing you need to do is define a vector of points going from x=0 to x=1. linspace is probably the most suitable where you can specify how many points we want. Let's assume 100 points for now:
x = linspace(0,1,100);
Therefore, h in our case is just 1/100. In general, if you want to solve from the starting point x = a up to the end point x = b, the step size h is defined as h = (b - a)/n where n is the total number of points you want to solve for in the ODE.
Now, we have to include the boundary conditions. This simply means that we know the beginning and ending of the solution of the ODE. This means that y(0) = y(1) = 0. As such, we make sure that the first row of Y has only the first column set to 1 and the last row of Y has only the last column set to 1, and we'll set the output position in F to both be 0. This symbolizes that we already know the solution at these points.
Therefore, your final code to solve is just:
%// Setup
a = 0; b = 1; n = 100;
x = linspace(a,b,n);
h = (b-a)/n;
%// Your code
uppershift = 1;
U = diag(ones(n-abs(uppershift),1),uppershift);
lowershift = -1;
L= diag(ones(n-abs(lowershift),1),lowershift);
D = ((U-L))/(2*h); %first differential operator
D2 = (full (gallery('tridiag',n)))/ -(h^2);
%// New code - Create differential equation matrix
Y = (-D2 + 2*D - eye(n));
%// Set boundary conditions on system
Y(1,:) = 0; Y(1,1) = 1;
Y(end,:) = 0; Y(end,end) = 1;
%// New code - Create F vector and set boundary conditions
F = x.';
F(1) = 0; F(end) = 0;
%// Solve system
X = Y \ F;
X should now contain your numerical approximation to the ODE in steps of h = 1/100 starting from x=0 up to x=1.
Now let's see what this looks like:
figure;
plot(x, X);
title('Solution to ODE');
xlabel('x'); ylabel('y');
You can see that y(0) = y(1) = 0 as per the boundary conditions.
Hope this helps, and good luck!