Solving for a variable in matlab - matlab

I have a system of two equations and I need Matlab to solve for a certain variable. The problem is the variable I need is inside an expression, and trig functions. I wrote the following code:
function [ V1, V2 ] = find_voltages( w1, l1, d, w2, G1, G2, m, v, e, h, a, x)
k1 = sqrt((2*V1*e)/(G1^2*m*v^2));
k2 = sqrt((2*V2*e)/(G2^2*m*v^2));
A = h + l1*a;
b = -A*k1*sin(k1*w1) + a*cos(k1*w1);
B = A*cos(k1*w1) + (a/k1)*sin(k1*w1);
C = B + a*b;
c = C*k2*sinh(k2*w2) + b*cosh(k2*w2);
D = C*cosh(k2*w2) + (b/k2)*sinh(k2*w2);
bd = A*k1*sinh(k1*w1) + a*cosh(k1*w1);
Bd = A*cosh(k1*w1) + (a/k1)*sinh(k1*w1);
Cd = Bd + a*bd;
cd = -Cd*k2*sin(k2*w2) + bd*cos(k2*w2);
Dd = Cd*cos(k2*w2) + (bd/k2)*sin(k2*w2);
fsolve([c*(x-(l1+w1+d+w2)) + D == 0, cd*(x-(l1+w1+d+w2)) + Dd == 0], [V1,V2])
end
and got an error because V1 and V2 are not defined. They are part of an expression, and need to be solved for. Is there a way to do this? Also, is it a problem that the functions I put as arguments to solve are conglomerates of the smaller equations above them?
Valid values:
Drift space 1 (l1): 0.11
Quad 1 length (w1): 0.11
Quad 2 length (w2): 0.048
Separation (d): 0.014
Radius of Separation 1 (G1): 0.016
Radius of Separation 2 (G2): 0.01
Voltage 1 (V1): -588.5
Voltage 2 (V2): 418
Kinetic Energy in eV: 15000
Mass (m) 9.109E-31
Kinetic Energy in Joules (K): 2.4E-15
Velocity (v): 72591415.94
Charge on an Electron (e): 1.602E-19
k1^2=(2*V1*e)/(G1^2*m*v^2): 153.4467773
k2^2=(2*V2*e)/(G2^2*m*v^2): 279.015

First start by re-writing your function as an expression which returns the extent to which your function(s) fail to hold for some valid guess for [V1,V2]. E.g.,
function gap = voltage_eqn(V, w1, l1, d, w2, G1, G2, m, v, e, h, a, x)
V1 = V(1) ;
V2 = V(2) ;
k1 = sqrt((2*V1*e)/(G1^2*m*v^2));
k2 = sqrt((2*V2*e)/(G2^2*m*v^2));
A = h + l1*a;
b = -A*k1*sin(k1*w1) + a*cos(k1*w1);
B = A*cos(k1*w1) + (a/k1)*sin(k1*w1);
C = B + a*b;
c = C*k2*sinh(k2*w2) + b*cosh(k2*w2);
D = C*cosh(k2*w2) + (b/k2)*sinh(k2*w2);
bd = A*k1*sinh(k1*w1) + a*cosh(k1*w1);
Bd = A*cosh(k1*w1) + (a/k1)*sinh(k1*w1);
Cd = Bd + a*bd;
cd = -Cd*k2*sin(k2*w2) + bd*cos(k2*w2);
Dd = Cd*cos(k2*w2) + (bd/k2)*sin(k2*w2);
gap(2) = c*(x-(l1+w1+d+w2)) + D ;
gap(1) = cd*(x-(l1+w1+d+w2)) + Dd ;
end
Then call fsolve from some initial V0:
Vf = fsolve(#(V) voltage_eqn(V, w1, l1, d, w2, G1, G2, q, m, v, e, h, a, x), V0) ;

Related

"Solve" command with a vector in matlab

I have a problem with the equation shown below. I want to enter a vector in t2 and find the roots of the equation from different values in t2.
t2=[10:10:100]
syms x
p = x^3 + 3*x - t2;
R = solve(p,x)
R1 = vpa(R)
Easy! Don't use syms and use the general formula:
t2 = [10:10:100];
%p = x^3 + 3*x - t2;
a = 1;
b = 0;
c = 3;
d = -t2;
D0 = b*b - 3*a*c;
D1 = 2*b^3 - 9*a*b*c + 27*a^2*d;
C = ((D1+sqrt(D1.^2 - 4*D0.^3))/2).^(1/3);
C1 = C*1;
C2 = C*(-1-sqrt(3)*1i)/2;
C3 = C*(-1+sqrt(3)*1i)/2;
f = -1/(3*a);
x1 = f*(b + C1 + D0./C1);
x2 = f*(b + C2 + D0./C2);
x3 = f*(b + C3 + D0./C3);
Since b = 0, you can simplify this a bit:
% ... polynomial is the same
D0 = -3*a*c;
D1 = 27*a^2*d;
% ... the different C's are the same
f = -1/(3*a);
x1 = f*(C1 + D0./C1);
x2 = f*(C2 + D0./C2);
x3 = f*(C3 + D0./C3);
Trial>> syms x p
Trial>> EQUS = p == x^3 + 3*x - t2
It is unknown that you want to solve an equation or a system.
Suppose that you want to solve a system.
Trial>> solx = solve(Eqns,x)
But, I do not think you can find roots.
You can solve one equation.
Trial>> solx = solve(Eqns(3),x)
As far as I know, Maple can do this batter.
In general, loops should be avoided, but this is the only solution that hit my brain right now.
t2=[10:10:100];
pp=repmat([1,0,3,0],[10,1]);
pp(:,4)=-t2;
for i=1:10
R(i,:) =roots(pp(i,:))';
end

I can't determine the polynomial interpolation with the minumum grade

I have to restrict it with the folowings:
P(-1) = f(-1), P(0)=f(0), P(1)=f(1), P'(1)=f'(1)
Let the polynomial be
ax³ + bx² + cx + d
By the given equations,
- a + b - c + d = f(-1)
d = f(0)
a + b + c + d = f(1)
3a +2b + c = f'(1)
You should be able to solve.

matlab: minimization/optimization algorithm

I use function with multiple outputs farina4 that computes coefficients a, b, e, f and a vector out_p5tads_final (1 x n array) through a minimization of a system of equations using the data input set p5tads (1 x n array):
function [a b e f fval out_p5tads_final] = farina4(p5tads)
f = #(coeff)calculs_farina4(coeff,p5tads);
[ans,fval] = fminsearchcon(f,coeff0,[0 0 0 0],[1 1 1 1]);% fminsearch with constrains
a = ans(1);
b = ans(2);
e = ans(3);
f = ans(4);
out_p5tads_final = p5tads_farina4(a,b,e,f);
function out_coeff = calculs_farina4(coeff0,p5tads)
%bla-bla-bla
end
function out_p5tads = p5tads_farina4(a,b,e,f)
%bla-bla-bla
end
end
After calculating a, b, e, f and out_p5tads_final I need to calculate/minimize the RMS function with respect to out_p5tads_f4.
RMS = sqrt(mean((p5tads(:) - out_p5tads_f4(:)).^2))*100
and to repeat function farina4 in order to find the optimal set of the parameters a, b, e, f and out_p5tads_final.
I am trying to build up an algorithm of such optimization and do not see a way so far.
For instance, it seems to be not possible to introduce a function with multiple output inside the above RMS equation unless there is a way to index somehow the output of this function farina4.
If there can be an alternative optimization algorithm for RMS without fminsearch (or similar) ?
a b e and f are values between 0 and 1
out_p5tads_final is an (1 x 10) array
%
function out_coeff = calculs_farina4(coeff0,p5tads)
%
mmmm = p5tads(1);
mmmr = p5tads(2);
rmmr = p5tads(3);
mmrm = p5tads(4);
mmrr = p5tads(5);
rmrm = p5tads(6);
rmrr = p5tads(7);
mrrm = p5tads(8);
mrrr = p5tads(9);
rrrr = p5tads(10);
%
a = coeff0(1);
b = coeff0(2);
e = coeff0(3);
f = coeff0(4);
%
f_mmmm = mmmm - ((a^2*b^2*(a + b) + e^2*f^2*(e + f))/2);
f_mmmr = mmmr - (a^2*b^2*(e + f) + e^2*f^2*(a + b));
f_rmmr = rmmr - ((a^2*f^2*(b + e) + b^2*e^2*(a + f))/2);
f_mmrm = mmrm - 2*a*b*e*f;
f_mmrr = mmrr - b*f*(a^3 + e^3) + a*e*(a^3 + f^3);
f_rmrm = rmrm - 2*a*b*e*f;
f_rmrr = rmrr - 2*a*b*e*f;
f_mrrm = mrrm - ((a^2*b^2*(e + f) + e^2*f^2*(a + b))/2);
f_mrrr = mrrr - (a^2*f^2*(b + e) + b^2*e^2*(a + f));
f_rrrr = rrrr - ((a^2*f^2*(a + f) + b^2*e^2*(b + e))/2);
%
out_coeff = f_mmmm^2 + f_mmmr^2 + f_rmmr^2 + f_mmrm^2 + f_mmrr^2 + f_rmrm^2 + f_rmrr^2 + f_mrrm^2 + f_mrrr^2 + f_rrrr^2;
end
%
function out_p5tads = p5tads_farina4(a,b,e,f)
%
p_mmmm = ((a^2*b^2*(a + b) + e^2*f^2*(e + f))/2);
p_mmmr = (a^2*b^2*(e + f) + e^2*f^2*(a + b));
p_rmmr = ((a^2*f^2*(b + e) + b^2*e^2*(a + f))/2);
p_mmrm = 2*a*b*e*f;
p_mmrr = b*f*(a^3 + e^3) + a*e*(a^3 + f^3);
p_rmrm = 2*a*b*e*f;
p_rmrr = 2*a*b*e*f;
p_mrrm = ((a^2*b^2*(e + f) + e^2*f^2*(a + b))/2);
p_mrrr = (a^2*f^2*(b + e) + b^2*e^2*(a + f));
p_rrrr = ((a^2*f^2*(a + f) + b^2*e^2*(b + e))/2);
%
out_p5tads = [p_mmmm,p_mmmr,p_rmmr,p_mmrm,p_mmrr,p_rmrm,p_rmrr,p_mrrm,p_mrrr,p_rrrr];
end
end
Thanks much in advance !
19/08/2014 3:35 pm
I need to get an optimal set of coefficients a b e f that the RMS value , which is calculated from
RMS = sqrt(mean((p5tads(:) - out_p5tads_f4(:)).^2))*100
is minimal. Here, the vector p5tads is used to calculate/optimize the set of a b e f coefficients, which are in turn used to calculate the vector out_p5tads_f4. The code should run a desired number of optimizations cycles (e.g. by default 100) and then select the series of a b e f and out_p5tads_f4 afforded the minimal RMS error value (with respect to out_p5tads_f4).

Manually changing RGB to HSI matlab

I would like to preface this by saying, I know some functions, including RGB2HSI could do this for me, but I would like to do it manually for a deeper understanding.
So my goal here is to change my RGB image to HSI color scheme. The image is in .raw format, and i am using the following formulas on the binary code to try and convert it.
theta = arccos((.5*(R-G) + (R-B))/((R-G).^2 + (R-B).*(G-B)).^.5);
S = 1 - 3./(R + G + B)
I = 1/3 * (R + G + B)
if B <= G H = theta if B > G H = 360 - theta
So far I have tried two different things, that have resulted in two different errors. The first attempted was the following,
for iii = 1:196608
C(iii) = acosd((.5*(R-G) + (R-B))/((R-G).^2 + (R-B).*(G-B)).^.5);
S(iii) = 1 - 3./(R + G + B);
I(iii) = 1/3 * (R + G + B);
end
Now in attempting this I knew it was grossly inefficent, but I wanted to see if it was a viable option. It was not, and the computer ran out of memory and refused to even run it.
My second attempt was this
fid = fopen('color.raw');
R = fread(fid,512*384*3,'uint8', 2);
fseek(fid, 1, 'bof');
G = fread(fid, 512*384*3, 'uint8', 2);
fseek(fid, 2, 'bof');
B = fread(fid, 512*384*3, 'uint8', 2);
fclose(fid);
R = reshape(R, [512 384]);
G = reshape(G, [512 384]);
B = reshape(B, [512 384]);
C = acosd((.5*(R-G) + (R-B))/((R-G).^2 + (R-B).*(G-B)).^.5);
S = 1 - 3./(R + G + B);
I = 1/3 * (R + G + B);
if B <= G
H = B;
if B > G
H = 360 - B;
end
end
H = H/360;
figure(1);
imagesc(H * S * I)
There were several issues with this that I need help with. First of all, the matrix 'C' has different dimensions than S and I so multiplication is impossible, so my first question is, how would I call up each pixel so I could perform the operations on them individually to avoid this dilemma.
Secondly the if loops refused to work, if I put them after "imagesc" nothing would happen, and if i put them before "imagesc" then the computer would not recognize what variable H was. Where is the correct placement of the ends?
Normally, the matrix 'C' have same dimensions as S and I because:
C = acosd((.5*(R-G) + (R-B))/((R-G).^2 + (R-B).*(G-B)).^.5);
should be
C = acosd((.5*(R-G) + (R-B))./((R-G).^2 + (R-B).*(G-B)).^.5);
elementwise division in the middle was missing . Another point is:
if B <= G
H = B;
if B > G
H = 360 - B;
end
end
should be
H = zeros(size(B));
H(find(B <= G)) = B(find(B <= G));
H(find(B > G)) = 360 - B(find(B > G));

matlab: large system of coupled nonlinear equations

I'm trying to solve the a very large system of coupled nonlinear equations. Following this thread and the related help by Matalb (first example) I tried to wrote the following code:
%% FSOLVE TEST #2
clc; clear; close all
%%
global a0 a1 a2 a3 a4 h0 TM JA JB
a0 = 2.0377638272727268;
a1 = -7.105521894545453;
a2 = 9.234000147272726;
a3 = -5.302489919999999;
a4 = 1.1362478399999998;
h0 = 45.5;
TM = 0.00592256;
JA = 1.0253896074561006;
JB = 1.3079437258774012;
%%
global N
N = 5;
XA = 0;
XB = 15;
dX = (XB-XA)/(N-1);
XX = XA:dX:XB;
y0 = JA:(JB-JA)/(N-1):JB;
plot(XX,y0,'o')
[x,fval] = fsolve(#nlsys,y0);
where the function nlsys is as follows:
function S = nlsys(x)
global a1 a2 a3 a4 N TM h0 dX JA JB
H = h0^2/12;
e = cell(N,1);
for i = 2:N-1
D1 = (x(i+1) - x(i-1))./2./dX;
D2 = (x(i+1) + x(i-1) - 2.*x(i))./(dX^2);
f = a1 + 2*a2.*x(i) + 3*a3.*x(i).^2 + 4*a4.*x(i).^3;
g = - H.* (a1 + 2*a2.*x(i) + 3*a3.*x(i).^2 + 4*a4.*x(i).^3)./(x(i).^5);
b = (H/2) .* (5*a1 + 8*a2.*x(i) + 9*a3.*x(i).^2 + 8*a4.*x(i).^3)./(x(i).^6);
e{i} = #(x) f + b.*(D1.^2) + g.*D2 - TM;
end
e{1} = #(x) x(1) - JA;
e{N} = #(x) x(N) - JB;
S = #(x) cellfun(#(E) E(x), e);
When I run the program, Matlab gives the following errors:
Error using fsolve (line 280)
FSOLVE requires all values returned by user functions to be of data type double.
Error in fsolve_test2 (line 32)
[x,fval] = fsolve(#nlsys,y0);
Where are my mistakes?
Thanks in advance.
Petrus