I'm trying to solve an equation using maxima 13.04.2 but the answer isn't what I expect.
Example:
y2=A2*cos(2*pi*f2*t+phase2) we know A2=.4,f2=6.4951,t=1, trying to find **phase2**
y2=.4*cos(2*pi*6.4951+phase2)
I tried to solve the y2 equation for phase2 in maxima but it got rid of the cos function
kill(all);
A:A; phase:phase; solve(A*cos(2*pi*f*t+phase)=0,phase);
The answer that came back was
I thought something like this was suppose to come back
y2 = A2×cos(2πf2t + φ2) ⇒
y2/A2 = cos(2πf2t + φ2) ⇒
arccos(y2/A2) = 2πf2t + φ2 ⇒
arccos(y2/A2) - 2πf2t = φ2
so I could then plug in the vales
A2 = 0.4, f2 = 6.4951, t = 1 and get the phase
Any ideas how to get maxima to get the correct format?
PS: Yes I know I can do it by hand but I have thousands of equations like this and I plan on using octave arrays to call maxima to solve them and bring the answers back into octave.
Well, it seems straightforward.
(%i1) e:A*cos(2*%pi*f*t + phi) = y;
(%o1) cos(2 %pi f t + phi) A = y
(%i2) solve (e, phi);
solve: using arc-trig functions to get a solution.
Some solutions will be lost.
y
(%o2) [phi = acos(-) - 2 %pi f t]
A
(%i3) subst ([A = 0.4, f = 6.4951, t = 1], %o2);
(%o3) [phi = acos(2.5 y) - 12.9902 %pi]
Couple of notes. (1) solve can solve this equation, which is good, but bear in mind that it is fairly limited in its capability, and you can probably make up other equations that it can't solve. There are some other options for solving equations in Maxima, but it general that is a weak area. (2) Maybe instead of switching back and forth between Maxima and Octave, you can just code equation %o2 in Octave and evaluate that for different values of the parameters.
Related
Firstly, I'm quite new to Matlab.
I am currently trying to do a definite integral with respect to y of a particular function. The function that I want to integrate is
(note that the big parenthesis is multiplying with the first factor - I can't get the latex to not make it look like power)
I have tried plugging the above integral into Desmos and it worked as intended. My plan was to vary the value of x and y and will be using for loop via matlab.
However, after trying to use the int function to calculate the definite integral with the code as follow:
h = 5;
a = 2;
syms y
x = 3.8;
p = 2.*x.^2+2.*a.*y;
q = x.^2+y.^2;
r = x.^2+a.^2;
f = (-1./sqrt(1-(p.^2./(4.*q.*r)))).*(2.*sqrt(q).*sqrt(r).*2.*a-p.*2.*y.*sqrt(r)./sqrt(q))./(4.*q.*r);
theta = int(f,y,a+0.01,h) %the integral is undefined at y=2, hence the +0.01
the result is not quite as expected
theta =
int(-((8*461^(1/2)*(y^2 + 361/25)^(1/2))/5 - (461^(1/2)*y*(8*y + 1444/25))/(5*(y^2 + 361/25)^(1/2)))/((1 - (4*y + 722/25)^2/((1844*y^2)/25 + 665684/625))^(1/2)*((1844*y^2)/25 + 665684/625)), y, 21/10, 5)
After browsing through various posts, the common mistake is the undefined interval but the +0.01 should have fixed it. Any guidance on what went wrong is much appreciated.
The Definite Integrals example in the docs shows exactly this type of output when a closed form cannot be computed. You can approximate it numerically using vpa, i.e.
F = int(f,y,a,h);
theta = vpa(F);
Or you can do a numerical computation directly
theta = vpaintegral(f,y,a,h);
From the docs:
The vpaintegral function is faster and provides control over integration tolerances.
I have tried solving 4 equations of 4 unknowns in MATLAB and Mathematica.
I used vpasolve for finding the unknowns in MATLAB. Here is the MATLAB code.
Y1 = 0.02;
l1 = 0.0172;
syms Y2 Y3 l2 l3;
lambda = [0.0713 0.0688 0.0665];
b1 = 0.1170;
b3 = 0.1252;
t2_1 = (2*pi/lambda(1))*l2;
t3_1 = (2*pi/lambda(1))*l3;
t2_3 = (2*pi/lambda(3))*l2;
t3_3 = (2*pi/lambda(3))*l3;
t1_1 = (2*pi/lambda(1))*l1;
t1_3 = (2*pi/lambda(3))*l1;
eq1 = 2*Y1*tan(t1_1)+Y2*tan(t2_1)+Y3*tan(t3_1)==0;
eq2 = 2*Y1*tan(t1_3)+Y2*tan(t2_3)+Y3*tan(t3_3)==0;
eq3 = b1== (t1_1*Y1)+(t2_1*(Y2/2)*((sec(t2_1)^2)/(sec(t1_1)^2)))+(t3_1*(Y3/2)*((sec(t3_1)^2)/(sec(t1_1)^2)));
eq4 = b3== (t1_3*Y1)+(t2_3*(Y2/2)*((sec(t2_3)^2)/(sec(t1_3)^2)))+(t3_3*(Y3/2)*((sec(t3_3)^2)/(sec(t1_3)^2)));
E=[eq1 eq2 eq3 eq4];
S=vpasolve(E,[Y2,Y3,l2,l3]);
For the same equations, I wrote the below code in Mathematica.
eqns = {2*Y1*Tan[t11]+Y2*Tan[t21]+Y3*Tan[t31]==0,
2*Y1*Tan[t13]+Y2*Tan[t23]+Y3*Tan[t33]==0,
t11*Y1 + t21*(Y2/2)*(Sec[t21]^2/Sec[t11]^2) + t31*(Y3/2)*(Sec[t31]^2/Sec[t11]^2)-b1==0,
t13*Y1 + t23*(Y2/2)*(Sec[t23]^2/Sec[t13]^2) + t33*(Y3/2)*(Sec[t33]^2/Sec[t13]^2)-b3==0};
NMinimize[Norm[Map[First, eqns]], {Y2,Y3,l2,l3}]
But both are giving me different solutions and those are not the required solutions. I suppose I should use some other function for solving the equations. Can anyone help me finding out how to solve these equations? Your help is highly appreciated. Thank you.
EDIT
If I use the below code for finding the roots, I'm able to find the solutions but I just want positive roots. I tried the code which you have mentioned for getting positive roots but its not working for this and I don't know why. Can you please check this once?
Y1=0.0125;l1=0.010563;lambda={0.0426,0.0401,0.0403,0.0423,0.0413}; b1 = 0.0804;
b3 = 0.0258;
t2_1 = (2*Pi/lambda[[1]])*l2;
t3_1 = (2*Pi/lambda[[1]])*l3;
t2_3 = (2*Pi/lambda[[5]])*l2;
t3_3 = (2*Pi/lambda[[5]])*l3;
t1_1 = (2*Pi/lambda[[1]])*l1;
t1_3 = (2*Pi/lambda[[5]])*l1;
eqns = {2*Y1*Tan[t11]+Y2*Tan[t21]+Y3*Tan[t31]==0,
2*Y1*Tan[t13]+Y2*Tan[t23]+Y3*Tan[t33]==0,
t11*Y1 + t21*(Y2/2)*(Sec[t21]^2/Sec[t11]^2) + t31*(Y3/2)*(Sec[t31]^2/Sec[t11]^2)-b1==0,
t13*Y1 + t23*(Y2/2)*(Sec[t23]^2/Sec[t13]^2) + t33*(Y3/2)*(Sec[t33]^2/Sec[t13]^2)-b3==0};
Try
Y1=0.02;l1=0.0172;lambda={0.0713,0.0688,0.0665};b1=0.1170;b3=0.1252;
t21=(2*Pi/lambda[[1]])*l2;t31=(2*Pi/lambda[[1]])*l3;t23=(2*Pi/lambda[[3]])*l2;
t33=(2*Pi/lambda[[3]])*l3;t11=(2*Pi/lambda[[1]])*l1;t13=(2*Pi/lambda[[3]])*l1;
eqns={2*Y1*Tan[t11]+Y2*Tan[t21]+Y3*Tan[t31]==0,
2*Y1*Tan[t13]+Y2*Tan[t23]+Y3*Tan[t33]==0,
t11*Y1+t21*(Y2/2)*(Sec[t21]^2/Sec[t11]^2)+t31*(Y3/2)*(Sec[t31]^2/Sec[t11]^2)-b1==0,
t13*Y1+t23*(Y2/2)*(Sec[t23]^2/Sec[t13]^2)+t33*(Y3/2)*(Sec[t33]^2/Sec[t13]^2)-b3==0};
tbl=Table[
{y2i,y3i,l2i,l3i}=RandomReal[{0,.2},4];
Quiet[root=Check[FindRoot[eqns,{{Y2,y2i,0,.2},{Y3,y3i,0,.2},{l2,l2i,0,.2},{l3,l3i,0,.2}}],False]];
If[root===False,Nothing,root]
,{256}];
roots=Sort[Map[{Y2,Y3,l2,l3}/.#&,tbl],Norm[#1]<Norm[#2]&]
If[roots=={},"It found no roots in that range using those coefficients",
Map[First,eqns]/.{Y2->roots[[1,1]],Y3->roots[[1,2]],l2->roots[[1,3]],l3->roots[[1,4]]}]
That will look for roots starting at 256 different random locations greater than, but near 0. The way I have written FindRoot this time it will stop if the search is outside the range 0,.2 You can change that range if needed.
Next your functions have many local minima that trap FindRoot. So it should discard all local minima found inside that range.
And I have hidden the warning messages, usually not a good idea to do.
Then it will Sort the roots to show those nearest 0 first.
If you want it to sort to show the results nearest some given value then I can modify the Sort to show those first, I just need to know where you want the roots nearest to.
And finally it substitutes the Y2,Y3,l2,l3 of the smallest root it found back into the four equations to demonstrate that the results are very very near zero and this is a real root instead of a local minima.
After trying that a few times I found one root at {0.0203704,0.0225972,0.0163842,0.0181147} and that looks very close to your required values if I swap Y2 and Y3 and swap l2 and l3. Is that perhaps the root that you are looking for?
If you need more then please tell me exactly what is needed.
Please check ALL this VERY carefully to make certain I have made no mistakes.
I'm trying to calculate the phase2 angle/value in the y2 equation of a signal given at a specific frequency if I know the other values. Is this possible? Example below: along with picture example:
y1=A1*cos*(2*pi*f1*t+phase1) we know A1,f1,t=1,phase1
y1=0.00720858*cos*(2*pi*6+6.33)
y2=A2*cos*(2*pi*f2*t+phase2) we know A2,f2,t=1, trying to find **phase2**
y2=.4*cos*(2*pi*6.4951+phase2)
y3=A3*cos*(2*pi*f3*t+phase3) we know A3,f3,t=1,phase3
y3=0.0135274*cos(2*pi*7+.786473)
I'm using maxima 13.04.2, octave 3.8.1.
I tried to solve the y2 equation for phase2 in maxima but it got rid of the cos function
kill(all);
A:A; phase:phase; solve(A*cos*(2*pi*t+phase)=0,phase);
the answer came back as phase=-2pi*t
Is this possible? or should I go about this another way?
Thanks
The weird result might stem from the fact that you multiply the cos function with what is supposed to be its argument (by the way, this is mathematically unsound). What you might want is to apply the cos function to the argument. To illustrate what I mean, compare:
A*cos*(2*pi*t+phase)
with:
A*cos(2*pi*t+phase)
On another hand, why not solve the equation pen-on-paper style?
y2 = A2×cos(2πf2t + φ2) ⇒
y2/A2 = cos(2πf2t + φ2) ⇒
arccos(y2/A2) = 2πf2t + φ2 ⇒
arccos(y2/A2) - 2πf2t = φ2
With the values that you provided:
A2 = 0.4, f2 = 6.4951, t = 1.
you can calculate the phase φ2 as function of your level y2 (left as exercise to you).
I have a large underdetermined equation system for which I search an unique solution in respect of any given constraints. I simplified my problem into the following one:
x²-4=0,
y²-9=0,
x*y=myMin,
x+y=myMin.
What is the best way to implement this in Matlab symbolically, so that it returns
x=2
y=-3
I'm searching something like
syms x y
S=solve(...
x²-4==0,...
y²-9==0,...
x*y==myMin,...
x+y==myMin);
I do not know how specify the min as a function command to solve. But here's an approach that solves the equations and then post-processes the result according to your constraints:
syms x y
S=solve(x^2-4==0,y^2-9==0);
[~,idx] = min(double(S.x .* S.y)+double(S.x + S.y));
X = double(S.x(idx))
Y = double(S.y(idx))
This gives:
X =
2
Y =
-3
The symbolic results have to be converted using the double command to allow processing with the min function.
The problem you seem to run into is that there is no solution, not even matlab can deal with that.
Try it like this:
myMin = -6;
syms x y
S=solve(...
x²-4==0,...
y²-9==0,...
x*y==myMin,...
x+y==myMin + 5); %Note the +5 to make it feasible
Cannot try myself, but a quick calculation tells me that this one is at least solvable.
I have all the data and an ODE system of three equations which has 9 unknown coefficients (a1, a2,..., a9).
dS/dt = a1*S+a2*D+a3*F
dD/dt = a4*S+a5*D+a6*F
dF/dt = a7*S+a8*D+a9*F
t = [1 2 3 4 5]
S = [17710 18445 20298 22369 24221]
D = [1357.33 1431.92 1448.94 1388.33 1468.95]
F = [104188 104792 112097 123492 140051]
How to find these coefficients (a1,..., a9) of an ODE using Matlab?
I can't spend too much time on this, but basically you need to use math to reduce the equation to something more meaningful:
your equation is of the order
dx/dt = A*x
ergo the solution is
x(t-t0) = exp(A*(t-t0)) * x(t0)
Thus
exp(A*(t-t0)) = x(t-t0) * Pseudo(x(t0))
Pseudo is the Moore-Penrose Pseudo-Inverse.
EDIT: Had a second look at my solution, and I didn't calculate the pseudo-inverse properly.
Basically, Pseudo(x(t0)) = x(t0)'*inv(x(t0)*x(t0)'), as x(t0) * Pseudo(x(t0)) equals the identity matrix
Now what you need to do is assume each time step (1 to 2, 2 to 3, 3 to 4) is an experiment (therefore t-t0=1), so the solution would be to:
1- Build your pseudo inverse:
xt = [S;D;F];
xt0 = xt(:,1:4);
xInv = xt0'*inv(xt0*xt0');
2- Get exponential result
xt1 = xt(:,2:5);
expA = xt1 * xInv;
3- Get the logarithm of the matrix:
A = logm(expA);
And since t-t0= 1, A is our solution.
And a simple proof to check
[t, y] = ode45(#(t,x) A*x,[1 5], xt(1:3,1));
plot (t,y,1:5, xt,'x')
You have a linear, coupled system of ordinary differential equations,
y' = Ay with y = [S(t); D(t); F(t)]
and you're trying to solve the inverse problem,
A = unknown
Interesting!
First line of attack
For given A, it is possible to solve such systems analytically (read the wiki for example).
The general solution for 3x3 design matrices A take the form
[S(t) D(t) T(t)].' = c1*V1*exp(r1*t) + c2*V2*exp(r2*t) + c3*V3*exp(r3*t)
with V and r the eigenvectors and eigenvalues of A, respectively, and c scalars that are usually determined by the problem's initial values.
Therefore, there would seem to be two steps to solve this problem:
Find vectors c*V and scalars r that best-fit your data
reconstruct A from the eigenvalues and eigenvectors.
However, going down this road is treaturous. You'd have to solve the non-linear least-squares problem for the sum-of-exponentials equation you have (using lsqcurvefit, for example). That would give you vectors c*V and scalars r. You'd then have to unravel the constants c somehow, and reconstruct the matrix A with V and r.
So, you'd have to solve for c (3 values), V (9 values), and r (3 values) to build the 3x3 matrix A (9 values) -- that seems too complicated to me.
Simpler method
There is a simpler way; use brute-force:
function test
% find
[A, fval] = fminsearch(#objFcn, 10*randn(3))
end
function objVal = objFcn(A)
% time span to be integrated over
tspan = [1 2 3 4 5];
% your desired data
S = [17710 18445 20298 22369 24221 ];
D = [1357.33 1431.92 1448.94 1388.33 1468.95 ];
F = [104188 104792 112097 123492 140051 ];
y_desired = [S; D; F].';
% solve the ODE
y0 = y_desired(1,:);
[~,y_real] = ode45(#(~,y) A*y, tspan, y0);
% objective function value: sum of squared quotients
objVal = sum((1 - y_real(:)./y_desired(:)).^2);
end
So far so good.
However, I tried both the complicated way and the brute-force approach above, but I found it very difficult to get the squared error anywhere near satisfyingly small.
The best solution I could find, after numerous attempts:
A =
1.216731997197118e+000 2.298119167536851e-001 -2.050312097914556e-001
-1.357306715497143e-001 -1.395572220988427e-001 2.607184719979916e-002
5.837808840775175e+000 -2.885686207763313e+001 -6.048741083713445e-001
fval =
3.868360951628554e-004
Which isn't bad at all :) But I would've liked a solution that was less difficult to find...