I am relatively new to Matlab. At the moment I am solving an equation using vpasolve. Since that needs to loop over 80 variables, it is slow.
% input variables
% ffsample = Fourier transformed reference waveform
% ffref = Fourier transformed reference waveform
len=80;
n1=1;
n2=1.95;
alpha_grad=30;
beta_grad=14.858;
% constants
tp12=2.*n1.*cos(alpha)./(n2.*cos(alpha)+n1.*cos(beta));
tp21=2.*n2.*cos(beta)./(n1.*cos(beta)+n2.*cos(alpha));
%functions with unknown n3
syms n3
gamma= asin(n2.*sin(beta)./n3);
rp23 = (n3 .* cos(beta) - n2.*cos(gamma)) ./ (n3 .* cos(beta) + n2 .* cos(gamma));
t = cputime;
res_para=zeros(len,1);
%loop to solve for n3
digits(5):
for i=1:len
res_para(i)=vpasolve(ffsample(i)./ffref(i) == (tp12*tp21*rp23) ./ (tp12*tp21), n3);
end
e = cputime-t
%convert results to complex numbers
res_para=double(res_para);
res_para=squeeze(res_para);
res_para=res_para';
Now, I am trying to convert this into an equation which uses solve before entering the loop. As far as I understand it, I should use solve in conjunction with vpa? At the moment I am trying like the following code, but it wouldn't start:
res_p=solve(x == (tp12*tp21*rp23) ./ (tp12*tp21), n3);
t = cputime;
res_para=zeros(len,1);
for i=1:len
x=ffsample(i)./ffref(i);
res_para=vpa(res_p(x));
end
Any help would be greatly appreciated.
Update
Thank you for your replies. I changed the digits(5) position.
And yes, digits(5) is a very low value indeed however, it speeds teh above code up by approximately 3 times.
As I said, I am relatively new to matlab and thought that solving the equation symbolically first and then just insert the variables would greatly reduce the time, but maybe I am wrong here.
Considering I use vpasolve to solve a very simple equation numerically:
function [ xval ] = example( )
syms x
for y=1:10
xval(y) = vpasolve(y == 5.*x+6, x);
end
Wouldn't it be faster if I solve the function y=5x+6 symbolically for x first and then just include the msising values in the loop? Something like this:
function [ xval ] = example2( )
syms x
% symbolic solve for x (???)
% y=5x+6 ==> x=(y-6)/5
sol=solve( y == 5.*x+6, x)
for y=1:10
xval(y) = sol(y);
end
Obviously example2 does not work.
Related
We have an equation similar to the Fredholm integral equation of second kind.
To solve this equation we have been given an iterative solution that is guaranteed to converge for our specific equation. Now our only problem consists in implementing this iterative prodedure in MATLAB.
For now, the problematic part of our code looks like this:
function delta = delta(x,a,P,H,E,c,c0,w)
delt = #(x)delta_a(x,a,P,H,E,c0,w);
for i=1:500
delt = #(x)delt(x) - 1/E.*integral(#(xi)((c(1)-c(2)*delt(xi))*ms(xi,x,a,P,H,w)),0,a-0.001);
end
delta=delt;
end
delta_a is a function of x, and represent the initial value of the iteration. ms is a function of x and xi.
As you might see we want delt to depend on both x (before the integral) and xi (inside of the integral) in the iteration. Unfortunately this way of writing the code (with the function handle) does not give us a numerical value, as we wish. We can't either write delt as two different functions, one of x and one of xi, since xi is not defined (until integral defines it). So, how can we make sure that delt depends on xi inside of the integral, and still get a numerical value out of the iteration?
Do any of you have any suggestions to how we might solve this?
Using numerical integration
Explanation of the input parameters: x is a vector of numerical values, all the rest are constants. A problem with my code is that the input parameter x is not being used (I guess this means that x is being treated as a symbol).
It looks like you can do a nesting of anonymous functions in MATLAB:
f =
#(x)2*x
>> ff = #(x) f(f(x))
ff =
#(x)f(f(x))
>> ff(2)
ans =
8
>> f = ff;
>> f(2)
ans =
8
Also it is possible to rebind the pointers to the functions.
Thus, you can set up your iteration like
delta_old = #(x) delta_a(x)
for i=1:500
delta_new = #(x) delta_old(x) - integral(#(xi),delta_old(xi))
delta_old = delta_new
end
plus the inclusion of your parameters...
You may want to consider to solve a discretized version of your problem.
Let K be the matrix which discretizes your Fredholm kernel k(t,s), e.g.
K(i,j) = int_a^b K(x_i, s) l_j(s) ds
where l_j(s) is, for instance, the j-th lagrange interpolant associated to the interpolation nodes (x_i) = x_1,x_2,...,x_n.
Then, solving your Picard iterations is as simple as doing
phi_n+1 = f + K*phi_n
i.e.
for i = 1:N
phi = f + K*phi
end
where phi_n and f are the nodal values of phi and f on the (x_i).
I have an equation in Matlab according to X parameter . I want to find the amount of X for the random amounts of F(x) .
and I tried the code below . but It gives me two different results while my equation should have just one result .
even I tried the roots(f) instead of solve(f) but it gave me an error :
??? Undefined function or method 'isfinite' for input arguments of
type 'sym'.
anybody can help me in this ?
what should I do ?
even if I have a wrong idea about solving this problem please tell me .
Thank you
function betaDistribution_2(a,b)
syms x ;
y=inline((x^(a-1))*((1-x)^(b-1)));
beta=quad(y,0,1);
g=(1/beta)*(x^(a-1))*((1-x)^(b-1));
% I have this equation and I want to find the amount of x for the random
%amounts of p
p=int(g,x,0,x);
for i=0:50
fxi=rand(1);
f=p-fxi;
xi=solve(f);
result=eval(xi);
disp(result)
end
end
Try to filter your solutions.
a=1;
b=2;
% a threshold for imagery part
SMALL=1e-9;
syms x real;
y=inline((x^(a-1))*((1-x)^(b-1)));
beta=quad(y,0,1);
g=(1/beta)*(x^(a-1))*((1-x)^(b-1));
p=int(g,x,0,x);
% return true for physically meaningfull results
filter = #(xc) abs(imag(xc))<SMALL && real(xc)>0 && subs(g, x, xc) > 0 && subs(p, x, xc)>0;
for m=0:50
fxi=rand(1);
f=p-fxi;
xi=solve(f, x);
result=eval(xi);
idx = arrayfun (filter, result);
result = result(idx);
% make sure it is OK
assert(length(result)==1);
disp(result)
end
I've been trying to use MATLAB to solve equations like this:
B = alpha*Y0*sqrt(epsilon)/(pi*ln(b/a)*sqrt(epsilon_t))*integral from
0 to pi of
(2*sinint(k0*sqrt(epsilon*(a^2+b^2-2abcos(theta))-sinint(2*k0*sqrt(epsilon)*a*sin(theta/2))-sinint(2*k0*sqrt(epsilon)*b*sin(theta/2)))
with regard to theta
Where epsilon is the unknown.
I know how to symbolically solve equations with unknown embedded in an integral by using int() and solve(), but using the symbolic integrator int() takes too long for equations this complicated. When I try to use quad(), quadl() and quadgk(), I have trouble dealing with how the unknown is embedded in the integral.
This sort of thing gets complicated real fast. Although it is possible to do it all in a single inline equation, I would advise you to split it up into multiple nested functions, if only for readability.
The best example of why readability is important: you have a bracketing problem in the eqution you posted; there's not enough closing brackets, so I can't be entirely sure what the equation looks like in mathematical notation :)
Anyway, here's one way to do it with the version I --think-- you meant:
function test
% some random values for testing
Y0 = rand;
b = rand;
a = rand;
k0 = rand;
alpha = rand;
epsilon_t = rand;
% D is your B
D = -0.015;
% define SIMPLE anonymous function
Bb = #(ep) F(ep).*main_integral(ep) - D;
% aaaand...solve it!
sol = fsolve(Bb, 1)
% The anonymous function above is only simple, because of these:
% the main integral
function val = main_integral(epsilon)
% we need for loop through epsilon, due to how quad(gk) solves things
val = zeros(size(epsilon));
for ii = 1:numel(epsilon)
ep = epsilon(ii);
% NOTE how the sinint's all have a different function as argument:
val(ii) = quadgk(#(th)...
2*sinint(A(ep,th)) - sinint(B(ep,th)) - sinint(C(ep,th)), ...
0, pi);
end
end
% factor in front of integral
function f = F(epsilon)
f = alpha*Y0*sqrt(epsilon)./(pi*log(b/a)*sqrt(epsilon_t)); end
% first sinint argument
function val = A(epsilon, theta)
val = k0*sqrt(epsilon*(a^2+b^2-2*a*b*cos(theta))); end
% second sinint argument
function val = B(epsilon, theta)
val = 2*k0*sqrt(epsilon)*a*sin(theta/2); end
% third sinint argument
function val = C(epsilon, theta)
val = 2*k0*sqrt(epsilon)*b*sin(theta/2); end
end
The solution above will still be quite slow, but I think that's pretty normal for integrals this complicated.
I don't think implementing your own sinint will help much, as most of the speed loss is due to the for loops with non-builtin functions...If it's speed you want, I'd go for a MEX implementation with your own Gauss-Kronrod adaptive quadrature routine.
The problem says:
Three tensile tests were carried out on an aluminum bar. In each test the strain was measured at the same values of stress. The results were
where the units of strain are mm/m.Use linear regression to estimate the modulus of elasticity of the bar (modulus of elasticity = stress/strain).
I used this program for this problem:
function coeff = polynFit(xData,yData,m)
% Returns the coefficients of the polynomial
% a(1)*x^(m-1) + a(2)*x^(m-2) + ... + a(m)
% that fits the data points in the least squares sense.
% USAGE: coeff = polynFit(xData,yData,m)
% xData = x-coordinates of data points.
% yData = y-coordinates of data points.
A = zeros(m); b = zeros(m,1); s = zeros(2*m-1,1);
for i = 1:length(xData)
temp = yData(i);
for j = 1:m
b(j) = b(j) + temp;
temp = temp*xData(i);
end
temp = 1;
for j = 1:2*m-1
s(j) = s(j) + temp;
temp = temp*xData(i);
end
end
for i = 1:m
for j = 1:m
A(i,j) = s(i+j-1);
end
end
% Rearrange coefficients so that coefficient
% of x^(m-1) is first
coeff = flipdim(gaussPiv(A,b),1);
The problem is solved without a program as follows
MY ATTEMPT
T=[34.5,69,103.5,138];
D1=[.46,.95,1.48,1.93];
D2=[.34,1.02,1.51,2.09];
D3=[.73,1.1,1.62,2.12];
Mod1=T./D1;
Mod2=T./D2;
Mod3=T./D3;
xData=T;
yData1=Mod1;
yData2=Mod2;
yData3=Mod3;
coeff1 = polynFit(xData,yData1,2);
coeff2 = polynFit(xData,yData2,2);
coeff3 = polynFit(xData,yData3,2);
x1=(0:.5:190);
y1=coeff1(2)+coeff1(1)*x1;
subplot(1,3,1);
plot(x1,y1,xData,yData1,'o');
y2=coeff2(2)+coeff2(1)*x1;
subplot(1,3,2);
plot(x1,y2,xData,yData2,'o');
y3=coeff3(2)+coeff3(1)*x1;
subplot(1,3,3);
plot(x1,y3,xData,yData3,'o');
What do I have to do to get this result?
As a general advice:
avoid for loops wherever possible.
avoid using i and j as variable names, as they are Matlab built-in names for the imaginary unit (I really hope that disappears in a future release...)
Due to m being an interpreted language, for-loops can be very slow compared to their compiled alternatives. Matlab is named MATtrix LABoratory, meaning it is highly optimized for matrix/array operations. Usually, when there is an operation that cannot be done without a loop, Matlab has a built-in function for it that runs way way faster than a for-loop in Matlab ever will. For example: computing the mean of elements in an array: mean(x). The sum of all elements in an array: sum(x). The standard deviation of elements in an array: std(x). etc. Matlab's power comes from these built-in functions.
So, your problem. You have a linear regression problem. The easiest way in Matlab to solve this problem is this:
%# your data
stress = [ %# in Pa
34.5 69 103.5 138] * 1e6;
strain = [ %# in m/m
0.46 0.95 1.48 1.93
0.34 1.02 1.51 2.09
0.73 1.10 1.62 2.12]' * 1e-3;
%# make linear array for the data
yy = strain(:);
xx = repmat(stress(:), size(strain,2),1);
%# re-formulate the problem into linear system Ax = b
A = [xx ones(size(xx))];
b = yy;
%# solve the linear system
x = A\b;
%# modulus of elasticity is coefficient
%# NOTE: y-offset is relatively small and can be ignored)
E = 1/x(1)
What you did in the function polynFit is done by A\b, but the \-operator is capable of doing it way faster, way more robust and way more flexible than what you tried to do yourself. I'm not saying you shouldn't try to make these thing yourself (please keep on doing that, you learn a lot from it!), I'm saying that for the "real" results, always use the \-operator (and check your own results against it as well).
The backslash operator (type help \ on the command prompt) is extremely useful in many situations, and I advise you learn it and learn it well.
I leave you with this: here's how I would write your polynFit function:
function coeff = polynFit(X,Y,m)
if numel(X) ~= numel(X)
error('polynFit:size_mismathc',...
'number of elements in matrices X and Y must be equal.');
end
%# bad condition number, rank errors, etc. taken care of by \
coeff = bsxfun(#power, X(:), m:-1:0) \ Y(:);
end
I leave it up to you to figure out how this works.
I'd like to create a Matlab plot of propeller angular velocity in terms of applied current. The point is, this requires combining two interdependent sets of data.
Firstly, drag coefficient c_d depends on angular velocity omega (I have no formula, just data) as seen on the plot below - the characteristics c_d(omega) could be easily linearised as c_d(omega) = p*omega + p_0.
Secondly, omega depends not only on applied current i, but also on the drag coefficient c_d(omega).
A script that solves the case, where c_d is constant below. It must be somehow possible to join those two using Matlab commands. Thanks for any help.
%%Lookup table for drag coefficient c_d
c_d_lookup = [248.9188579 0.036688351; %[\omega c_d]
280.2300647 0.037199094;
308.6091183 0.037199094;
338.6636881 0.03779496;
365.8908244 0.038305703;
393.9557188 0.039156941;
421.9158934 0.039667683;
452.2846224 0.040348674;
480.663676 0.041199911;
511.032405 0.042051149;
538.9925796 0.042561892;
567.2669135 0.043242882;
598.4734005 0.043668501;
624.1297405 0.044264368;
651.9851954 0.044604863;
683.6105614 0.045200729];
subplot(2,1,1)
plot(c_d_lookup(:,1), c_d_lookup(:,2))
title('This is how c_d depends on \omega')
ylabel('c_d')
xlabel('\omega [rad/s]')
%%Calculate propeller angular speed in terms of applied current. omega
%%depends on c_d, which in turn depends on omega. The formula is:
% omega(i) = sqrt(a*i / (b * c_d(omega)))
% Where:
% i - applied current
% omega - propeller angular velocity
% a,b - coefficients
i = [1:15];
a = 0.0718;
b = 3.8589e-005;
%If c_d was constant, I'd do:
omega_i = sqrt(a .* i / (b * 0.042));
subplot(2,1,2)
plot(i, omega_i)
ylabel({'Propeller ang. vel.', '\omega [rad/s]'})
xlabel('Applied current i[A]')
title('Propeller angular velocity in terms of applied current')
EDIT:
Trying to follow bdecaf's solution. So I created a function c_d_find, like so:
function c_d = c_d_find(omega, c_d_lookup)
c_d = interp1(c_d_lookup(:,1), c_d_lookup(:,2), omega, 'linear', 'extrap');
end
I don't know anything about Matlab function handles, but seem to understand the idea... In Matlab command window I typed:
f = #(omega) omega - sqrt(a .* i / (b * c_d_find(omega, c_d_lookup)))
which I hope created the correct function handle. What do I do next? Executing the below doesn't work:
>> omega_consistent = fzero(f,0)
??? Operands to the || and && operators must be convertible to logical scalar
values.
Error in ==> fzero at 333
elseif ~isfinite(fx) || ~isreal(fx)
hmmm...
Wonder if I understand correctly - but looks like you are looking for a consistent solution.
Your equations don't look to complicated I would outline the solution like this:
Write a function function c_d = c_d_find(omega) that does some interpolation or so
make a function handle like f = #(omega) omega - sqrt(a .* i / (b * c_d_find(omega))) - this is zero for consistent omega
calculate a consistent omega with omega_consistent =fzero(f,omega_0)