I'm very new to Matlab and have problem plotting this nonlinear 2D function graph using Matlab.
a lot of errors generated after the below is run.
fun1 = 20 + 10 + 15;
fun2 = 20 + (x * 0.00125 ) + 15;
fun3 = (x * 0.0025) + 15;
fplot(fun1,[0 8000])
fplot(fun2,[8000 16000])
fplot(fun2,[16000 positive infinity])
I appreciate a lot to your efforts and kindness for replying my question
Best Regards
Your first three expressions do not define functions. Please read the documentation about the correct syntax.
fun1 = #(x)(20 + 10 + 15);
First create a file fun.m which contains your function definition
function y = fun(x)
if x < 8000
y = 20 + 10 + 15;
elseif x < 16000
y = 20 + (x * 0.00125) + 15;
else
y = x * 0.0025 + 15;
end
end
Then you can plot it with
fplot(#fun, [0 24000])
which results in
If you do some reading in fplot you will find out that
for fplot(fun,limits)
fun must be
The name of a function
A string with variable x that may be passed to eval, such as 'sin(x)', 'diric(x,10)', or '[sin(x),cos(x)]'
A function handle
so in your case you need to change all of you fun to strings just add ' before and after the expression
as for the last line change it to be
fplot(fun2,[16000 inf])
although i don't think this would give you any good results
Related
Good afternoon!
First things first, I looked for similar questions for a while, but (probably because of my inexperience) I've found nothing similar to what I'm going to ask.
I'm using matlab for the first time to solve this kind of problems, so I'm not sure of what to do. A brief explenation:
I'm doing a project for my Optimal Control course: I have to replicate the results of a paper about employment, and I'm stuck with the plots. I have the following data:
five variable functions (U(t), T(t), R(t), V1(t) and V2(t))
four control functions(u1(t), u2(t), u3(t), u4(t))
constraints on the control variables (each u must be between 0 and 1)
initial values for U, T, R, V1 and V2 (in t=0, in particular V1 and V2 are constant over time)
final values for the λ coefficients in the hamiltonian
(note: for the controls, I've already found the optimal expression, which is in this form: ui = min{1, max{0,"expression"}}. If needed, I can give also the four expressions, neglected to
synthesize a little)
Under professor's suggestions, I've tried to use fmincon, that theoretically should give me directly the information that I need to plot some result using only the cost function of the problem. But in this case I have some issues involving time in the calculations. Below, the code that I used for fmincon:
syms u
%note: u(5) corresponds to U(t), but this is the only way I've found to get
%a result, the other u(i) are in ascending order (u(1) = u1 and so on...)
g = #(u) 30*u(5) + (20/2)*(u(1))^2 + (20/2)*(u(2))^2 + (10/2)*(u(3))^2 + (40/2)*(u(4))^2;
%initial guesses
u0 = [0 0 0 0 100000]; %
A = [];
b = [];
Aeq = [];
beq = [];
lb = 0.0 * ones(1,2,3,4);
ub = 1.0 * ones(1,2,3,4);
[x,fval,output,lambda] = fmincon(g, u0, A, b, Aeq, beq, lb, ub);
Whit this code, i get (obviously) only one value for each variable as result, and since I've not found any method to involve time, as I said before, I start looking for other solving strategies.
I found that ode45 is a differential equation solver that has the "time iteration" already included in the algorithm, so I tried to write the code to get it work with my problem.
I took all the equations from the paper and put them in a vector as shown in the mathworks examples, and this is my matlab file:
syms u1(t) u2(t) u3(t) u4(t)
syms U(t) T(t) R(t) V1(t) V2(t)
syms lambda_u lambda_t lambda_r lambda_v1 lambda_v2
%all the parameters provided by the paper
delta = 500;
alpha1 = 0.004;
alpha2 = 0.005;
alpha3 = 0.006;
gamma1 = 0.001;
gamma2 = 0.002;
phi1 = 0.22;
phi2 = 0.20;
delta1 = 0.09;
delta2 = 0.05;
k1 = 0.000003;
k2 = 0.000002;
k3 = 0.0000045;
%these two variable are set constant
V1 = 200;
V2 = 100;
%weight values for the cost function (only A1 is used in this case, but I left them all since the unused ones are irrelevant)
A1 = 30;
A2 = 20;
A3 = 20;
A4 = 10;
A5 = 40;
%ordering the unknowns in an array
x = [U T R u1 u2 u3 u4];
%initial conditions, ordered as the x vector (for the ui are guesses)
y0 = [100000 2000 1000 0 0 0 0];
%system set up
f = #(t,x) [delta - (1 + x(4))*k1*x(1)*V1 - (1 + x(5))*k2*x(1)*V2 - alpha1*x(1) + gamma1*x(2) + gamma2*x(3);...
(1 + x(4))*k1*x(1)*V1 - k3*x(2)*V2 - alpha2*x(2) - gamma1*x(2);...
(1 + x(5))*k2*x(1)*V2 - alpha3*x(3) - gamma2*x(3) + k3*x(2)*V2;...
alpha2*x(2) + gamma1*x(2) + (1 + x(6))*phi1*x(1) + k3*x(2)*V2 - delta1*V1;...
alpha3*x(3) + gamma2*x(3) + (1 + x(7))*phi2*x(1) - delta2*V2;...
-A1 + (1 + x(4))*k1*V1*(lambda_u - lambda_t) + (1 + x(5))*k2*V2*(lambda_u - lambda_r) + lambda_u*alpha1 - lambda_v1*(1 + x(6))*phi1 - lambda_v2*(1 + x(7))*phi2;...
-lambda_u*gamma1 + (alpha2 + gamma1)*(lambda_t - lambda_v1) + k3*V2*(lambda_t - lambda_r - lambda_v1);...
-lambda_u*gamma2 + (alpha3 + gamma2)*(lambda_r - lambda_v2);...
(1 + x(4))*k1*x(1)*(lambda_u - lambda_t) + lambda_v1*delta1;...
(1 + x(5))*k2*x(1)*(lambda_u -lambda_r) + k3*x(2)*(lambda_t - lambda_r - lambda_v1) + lambda_v2*delta2];
%using ode45 to solve over the chosen time interval
[t,xa] = ode45(f,[0 10],y0);
With this code, I get the following error:
Error using odearguments (line 95)
#(T,X)[DELTA-(1+X(4))*K1*X(1)*V1-(1+X(5))*K2*X(1)*V2-ALPHA1*X(1)+GAMMA1*X(2)+GAMMA2*X(3);(1+X(4))*K1*X(1)*V1-K3*X(2)*V2-ALPHA2*X(2)-GAMMA1*X(2);(1+X(5))*K2*X(1)*V2-ALPHA3*X(3)-GAMMA2*X(3)+K3*X(2)*V2;ALPHA2*X(2)+GAMMA1*X(2)+(1+X(6))*PHI1*X(1)+K3*X(2)*V2-DELTA1*V1;ALPHA3*X(3)+GAMMA2*X(3)+(1+X(7))*PHI2*X(1)-DELTA2*V2;-A1+(1+X(4))*K1*V1*(LAMBDA_U-LAMBDA_T)+(1+X(5))*K2*V2*(LAMBDA_U-LAMBDA_R)+LAMBDA_U*ALPHA1-LAMBDA_V1*(1+X(6))*PHI1-LAMBDA_V2*(1+X(7))*PHI2;-LAMBDA_U*GAMMA1+(ALPHA2+GAMMA1)*(LAMBDA_T-LAMBDA_V1)+K3*V2*(LAMBDA_T-LAMBDA_R-LAMBDA_V1);-LAMBDA_U*GAMMA2+(ALPHA3+GAMMA2)*(LAMBDA_R-LAMBDA_V2);(1+X(4))*K1*X(1)*(LAMBDA_U-LAMBDA_T)+LAMBDA_V1*DELTA1;(1+X(5))*K2*X(1)*(LAMBDA_U-LAMBDA_R)+K3*X(2)*(LAMBDA_T-LAMBDA_R-LAMBDA_V1)+LAMBDA_V2*DELTA2]
returns a vector of length 10, but the length of initial conditions vector is 7. The vector returned by
#(T,X)[DELTA-(1+X(4))*K1*X(1)*V1-(1+X(5))*K2*X(1)*V2-ALPHA1*X(1)+GAMMA1*X(2)+GAMMA2*X(3);(1+X(4))*K1*X(1)*V1-K3*X(2)*V2-ALPHA2*X(2)-GAMMA1*X(2);(1+X(5))*K2*X(1)*V2-ALPHA3*X(3)-GAMMA2*X(3)+K3*X(2)*V2;ALPHA2*X(2)+GAMMA1*X(2)+(1+X(6))*PHI1*X(1)+K3*X(2)*V2-DELTA1*V1;ALPHA3*X(3)+GAMMA2*X(3)+(1+X(7))*PHI2*X(1)-DELTA2*V2;-A1+(1+X(4))*K1*V1*(LAMBDA_U-LAMBDA_T)+(1+X(5))*K2*V2*(LAMBDA_U-LAMBDA_R)+LAMBDA_U*ALPHA1-LAMBDA_V1*(1+X(6))*PHI1-LAMBDA_V2*(1+X(7))*PHI2;-LAMBDA_U*GAMMA1+(ALPHA2+GAMMA1)*(LAMBDA_T-LAMBDA_V1)+K3*V2*(LAMBDA_T-LAMBDA_R-LAMBDA_V1);-LAMBDA_U*GAMMA2+(ALPHA3+GAMMA2)*(LAMBDA_R-LAMBDA_V2);(1+X(4))*K1*X(1)*(LAMBDA_U-LAMBDA_T)+LAMBDA_V1*DELTA1;(1+X(5))*K2*X(1)*(LAMBDA_U-LAMBDA_R)+K3*X(2)*(LAMBDA_T-LAMBDA_R-LAMBDA_V1)+LAMBDA_V2*DELTA2]
and the initial conditions vector must have the same number of elements.
Error in ode45 (line 115)
odearguments(FcnHandlesUsed, solver_name, ode, tspan, y0, options, varargin);
Error in test (line 62)
[t,xa] = ode45(f,[0 10],y0);
For which I can't find a solution, since I have used all the initial values given in the paper. The only values that I have left are the final values for the lambda coefficients, since they are final values, and I am not sure if they can be used.
In this case, I can't also understand where I should put the bounds on the control variable.
For completeness, I will provide also the link to the paper in question:
https://www.ripublication.com/ijss17/ijssv12n3_13.pdf
Can you help me figure out what I can do to solve my problems?
P.S: I know this is a pretty bad code, but I'm basing on the basics tutorials on mathworks; for sure this should need to be refactored and ordered in various file (one for the cost function and one for the constraints for example) but firstly I would like to understand where the problem is and then I will put all in a pretty form.
Thank you so much!
Generally you confused something with Vectors. In initial conditions you declared 7 values:
%initial conditions, ordered as the x vector (for the ui are guesses)
y0 = [100000 2000 1000 0 0 0 0];
But you declared 10 ODE's:
%system set up
f = #(t,x) [delta - (1 + x(4))*k1*x(1)*V1 - (1 + x(5))*k2*x(1)*V2 - alpha1*x(1) + gamma1*x(2) + gamma2*x(3);...
(1 + x(4))*k1*x(1)*V1 - k3*x(2)*V2 - alpha2*x(2) - gamma1*x(2);...
(1 + x(5))*k2*x(1)*V2 - alpha3*x(3) - gamma2*x(3) + k3*x(2)*V2;...
alpha2*x(2) + gamma1*x(2) + (1 + x(6))*phi1*x(1) + k3*x(2)*V2 - delta1*V1;...
alpha3*x(3) + gamma2*x(3) + (1 + x(7))*phi2*x(1) - delta2*V2;...
-A1 + (1 + x(4))*k1*V1*(lambda_u - lambda_t) + (1 + x(5))*k2*V2*(lambda_u - lambda_r) + lambda_u*alpha1 - lambda_v1*(1 + x(6))*phi1 - lambda_v2*(1 + x(7))*phi2;...
-lambda_u*gamma1 + (alpha2 + gamma1)*(lambda_t - lambda_v1) + k3*V2*(lambda_t - lambda_r - lambda_v1);...
-lambda_u*gamma2 + (alpha3 + gamma2)*(lambda_r - lambda_v2);...
(1 + x(4))*k1*x(1)*(lambda_u - lambda_t) + lambda_v1*delta1;...
(1 + x(5))*k2*x(1)*(lambda_u -lambda_r) + k3*x(2)*(lambda_t - lambda_r - lambda_v1) + lambda_v2*delta2];
Every line in above code is recognized as one ODE.
But that's not all. The second problem is with your construction. You mixed symbolic math (lambda declared as syms) with numerical solving, which will be tricky. I'm not familiar with the exact scientific problem you are trying to solve, but if you can't avoid symbolic math, maybe you should try dsolve from Symbolic Math Toolbox?
I am trying to solve a differential equation with the ode solver ode45 with MATLAB. I have tried using it with other simpler functions and let it plot the function. They all look correct, but when I plug in the function that I need to solve, it fails. The plot starts off at y(0) = 1 but starts decreasing at some point when it should have been an increasing function all the way up to its critical point.
function [xpts,soln] = diffsolver(p1x,p2x,p3x,p1rr,y0)
syms x y
yp = matlabFunction((p3x/p1x) - (p2x/p1x) * y);
[xpts,soln] = ode45(yp,[0 p1rr],y0);
p1x, p2x, and p3x are polynomials and they are passed into this diffsolver function as parameters.
p1rr here is the critical point. The function should diverge after the critical point, so i want to integrate it up to that point.
EDIT: Here is the code that I have before using diffsolver, the above function. I do pade approximation to find the polynomials p1, p2, and p3. Then i find the critical point, which is the root of p1 that is closest to the target (target is specified by user).
I check if the critical point is empty (sometimes there might not be a critical point in some functions). If its not empty, then it uses the above function to solve the differential equation. Then it plots the x- and y- points returned from the above function basically.
function error = padeapprox(m,n,j)
global f df p1 p2 p3 N target
error = 0;
size = m + n + j + 2;
A = zeros(size,size);
for i = 1:m
A((i + 1):size,i) = df(1:(size - i));
end
for i = (m + 1):(m + n + 1)
A((i - m):size,i) = f(1:(size + 1 - i + m));
end
for i = (m + n + 2):size
A(i - (m + n + 1),i) = -1;
end
if det(A) == 0
error = 1;
fprintf('Warning: Matrix is singular.\n');
end
V = -A\df(1:size);
p1 = [1];
for i = 1:m
p1 = [p1; V(i)];
end
p2 = [];
for i = (m + 1):(m + n + 1)
p2 = [p2; V(i)];
end
p3 = [];
for i = (m + n + 2):size
p3 = [p3; V(i)];
end
fx = poly2sym(f(end:-1:1));
dfx = poly2sym(df(end:-1:1));
p1x = poly2sym(p1(end:-1:1));
p2x = poly2sym(p2(end:-1:1));
p3x = poly2sym(p3(end:-1:1));
p3fullx = p1x * dfx + p2x * fx;
p3full = sym2poly(p3fullx); p3full = p3full(end:-1:1);
p1r = roots(p1(end:-1:1));
p1rr = findroots(p1r,target); % findroots eliminates unreal roots and chooses the one closest to the target
if ~isempty(p1rr)
[xpts,soln] = diffsolver(p1x,p2x,p3fullx,p1rr,f(1));
if rcond(A) >= 1e-10
plot(xpts,soln); axis([0 p1rr 0 5]); hold all
end
end
I saw some examples using another function to generate the differential equation but i've tried using the matlabFunction() method with other simpler functions and it seems like it works. Its just that when I try to solve this function, it fails. The solved values start becoming negative when they should all be positive.
I also tried using another solver, dsolve(). But it gives me an implicit solution all the time...
Does anyone have an idea why this is happening? Any advice is appreciated. Thank you!
Since your code seems to work for simpler functions, you could try to increase the accuracy options of the ode45 solver.
This can be achieved by using odeset:
options = odeset('RelTol',1e-10,'AbsTol',1e-10);
[T,Y] = ode45(#function,[tspan],[y0],options);
I'm trying build a matlab function that will evaluate a function and vector that are sent in as parameters. I'm having a hard time trying to figure out how to send in the function so that it can be evaluated in the matlab function. I figured out how to do it without the function but I'm a little lost trying to evaluate it within a matlab function. Below is my code...
This is what I'm trying to do...
x = [x1 x2]';
f = x(x1)^2 + 2 * (x2)^2
x = [5 10];
f = (5)^2 + 2 * (10)^2 % which I would like to return 225, not a column vector
This is what I have and what I have tried...
x = [5 10]';
% without using a matlab function
% k = 1
% f = x(k)^2 + 2 * x(k + 1)^2; % returns the correct answer of 225
f = x^2 + 2 * x^2 % complains about the scalar 2
f = x.^2 + 2 * x.^2 % returns a column vector [75; 300]
function [value] = evalFunction(f,x)
value = f(x);
I've tried...
f = #(x) x.^2 + 2 * (x+1).^2;
value = evalFunction(#f,x) %Error: "f" was previously used as a variable
So I tried...
f = #(x) x.^2 + 2 * (x+1).^2;
value = evalFunction(f,x) %value = [97;342]
I'm new to matlab so any help is appreciated. I've been doing some research and found some stuff here on stackoverflow but can't seem to get it to work. I've seen there are other ways to do this, but I will eventually be adding more code to the matlab evalFunction function so I'd like to do it this way. Thanks!
Anonymous functions and function handles plus array indexing. Taking x as a 2-element vector, define and use your function like:
f = #(x) x(1).^2 + 2 * x(2).^2;
value = evalFunction(f,x) % but you can just do f(x) if that is all you need
However, if evalFunction does nothing other than evaluate f at x, then you don't need it at all. Just do f(x).
Alternately,
f = #(x1,x2) x1.^2 + 2*x2.^2;
value = evalFunction(f,x1,x2); % here your function will call it by f(x1,x2)
You are probably coming at this from a C background - in Matlab, x+1 is the entire vector x with 1 added - not the element offset by 1.
The function you need is
f = #(x)x(1).^2 + 2 * (x(2)).^2;
or, to be a little more "matlab-like":
f = #(x) [1 2] * x(1:2)'.^2;
Which performs the element-wise square of the first two elements of x as a column vector, and then does the matrix multiplication with [1 2], resulting in
1 * x(1) .^2 + 2 * x(2) .^2;
Which seems to be what you were asking for.
caveat: did not have opportunity to test this...
I have a question regarding Matlab's option to publish in PDF. Say I have the following code:
%1D functions and plotting
%1. We calculate y given the following function: y(x) = 2x^2 +
%3x + 1, for x = 10.
x = 10;
y = 2*x.^2 + 3*x + 1
%2. We calculate y given the following function: y(x) = ax^2 + bx + c, with a
%=2, b=3, c=0 and x = 100.
a=2;
b=3;
c=0;
x=100;
y = a*x.^2 + b*x + c
. . . more code follows here
When I choose to publish this as PDF the answers to problems 1 and 2 (where I calculate two different values for the variable 'y') do not appear at the line where I calculate the value (where I write y = 2*x.^2 + 3*x + 1 for instance). Instead, the values of the 'y' variables appear at the end of the document where it says 'y = 231' and 'y = 20300'. Is there any way I can get this to be included right after I define the variable without separating the document into cells? Or is this a default thing that I can not do anything about? I would really appreciate any input!
The general approach (indipendent of the output format) is to restart the paragraph with line break and %%:
%% 1. We calculate y given the following function: y(x) = 2x^2 +
% 3x + 1, for x = 10.
x = 10;
y = 2*x.^2 + 3*x + 1
%% 2. We calculate y given the following function: y(x) = ax^2 + bx + c, with a
% =2, b=3, c=0 and x = 100.
a=2;
b=3;
c=0;
x=100;
y = a*x.^2 + b*x + c
I would like to create a function that finds the parameters p and q of Bass diffusion model, given the data of two time periods.
The model (equation) is the following:
n(T) = p*m + (q-p)*n(T-1) + q/m*n(T-1)^2
where
n(T) = number of addoptions occuring in period T
n(T-1) = number of cumulative adoptions that occured before T
p = coefficient of innovation
q = coefficient of imitation
m = number of eventual adopters
for example if m = 3.000.000
and the data for the years below is the following:
2000: n(T) = 820, n(T-1) = 0
2005: n(T) = 25000, n(T-1) = 18000
then the following equation system has to be solved (in order to determine the values of p and q):
p*m + (q-p)*0 + q/3.000.000 * 0^2 == 820
p*m + (q-p)*18000 + q/3.000.000 * 18000^2 == 25000
By following Matlab documentation I tried to create a function Bass:
function F = Bass(m, p, q, cummulativeAdoptersBefore)
F = [p*m + (q-p)*cummulativeAdoptersBefore(1) + q/m*cummulativeAdoptersBefore(1).^2;
p*m + (q-p)*cummulativeAdoptersBefore(2) + q/m*cummulativeAdoptersBefore(2).^2];
end
Which should be used in fsolve(#Bass,x0,options) but in this case m, p, q, cummulativeAdoptersBefore(1), and cummulativeAdoptersBefore(2) should be given in x0 and all variables would be considered as unknown instead of just the latter two.
Does anyone know how to solve the system of equations such as above?
Thank you!
fsolve() seeks to minimize the function you supply as argument. Thus, you have to change your equations to
p*m + (q-p)*0 + q/3.000.000 * 0^2 - 820 == 0
p*m + (q-p)*18000 + q/3.000.000 * 18000^2 - 25000 == 0
and in Matlab syntax
function F = Bass(m, p, q, cumulativeAdoptersBefore, cumulativeAdoptersAfter)
F = [p*m + (q-p)*cumulativeAdoptersBefore(1) ...
+ q/m *cumulativeAdoptersBefore(1).^2
- cumulativeAdoptersAfter(1);
p*m + (q-p)*cumulativeAdoptersBefore(2) ...
+ q/m *cumulativeAdoptersBefore(2).^2
- cumulativeAdoptersAfter(2)];
end
Note: There is a typo in your Bass function (multiplication instead of sum).
Now you have a function, which takes more parameters than there are unkowns.
One option is to create an anonymous function, which only takes the unknowns as arguments and to fix the other parameters via a closure.
To fit the unkowns p and q, you could use something like
cumulativeAdoptersBefore = [0, 1800];
cumulativeAdoptersAfter = [820, 25000];
m = 3e6;
x = [0, 0]; %# Probably, this is no good starting guess.
xopt = fsolve(#(x) Bass(m, x(1), x(2), cumulativeAdoptersBefore, cumulativeAdoptersAfter), x0);
So fsolve() sees a function taking only a single argument (a vector with two elements) and it also returns a vector value.