I a struggling writing a fully parametrised fitness function for the global optimisation toolbox in matlab.
Approach:
[x fvall,exitflag,output]=ga(fitnessfcn,nvars,A,b,Aeq,beq,lb,ub)
I have a fitness function that I call with
fitnessfcn=#fitnessTest;
hence, the function is stated in a separate file.
Problem:
My issue is now that my optimisation is a simple but super long sum like
cost=f1*x1+f2x2+...fnxn
n should be parameterised (384 at the moment). In all matlab help files, the objective function is always short and neat like
y = 100 * (x(1)^2 - x(2)) ^2 + (1 - x(1))^2;
I have tried several approaches "writing" the objective function intelligently, but then, I cannot call the function correctly:
if I write the fitness function manually (for fi=1)
function y = simple_fitness(x)
y = x(1)+ x(2)+ x(3)+ x(4)+ x(5)+ x(6)+ x(7)+ x(8);
the global optimisation works
But if I use my automated approach:
n = 8; %# number of function handles
parameters = 1:1:n;
store = cell(2,3);
for i=1:n
store{1,i} = sprintf('x(%i)',parameters(i));
store{2,i} = '+'; %# operator
end
%# combine such that we get
%# sin(t)+sin(t/2)+sin(t/4)
funStr = [store{1:end-1}];%# ignore last operator
endFunction=';';
%functionHandle = str2func(funStr)
y=strcat(funStr,endFunction)
matlab does not recognise the function properly:
error:
Subscripted assignment dimension mismatch.
Error in fcnvectorizer (line 14)
y(i,:) = feval(fun,(pop(i,:)));
thanks! I cannot write the objective function by hand as I will have several hundred variables.
You can use the sum(x) directly using function handle, instead of writing all indexes, do: fitnessfcn = #(x) sum(x).
Related
I write code to do optimization to solve a problem taking the form min_x f(x,c) s.t. sum(x)=1 and g(x,c)=1, where x is a vector and c is a function of x such that c(x) is a constant when x is fixed. Indeed, g(x,c) takes the following form
n=5; N=1000000; idx = 1:1:n; df=4; theta=2; alpha=2.4;
central_Simulation_Points=mvtrnd(eyes(n),df,N);
x=sym('x_%d',[n,1]); syms c
g(x,c)=1./N.*sum((theta.*(alpha-1).*central_Simulation_Points*x).^2+c).^(1./(alpha-1)))==1;
Constraint_equ = solve(g(x,c), c);
Constraint_equ_handle = matlabFunction(Constraint_equ);
The use of solve is to express c in terms of x, while matlabFunction changes the symbolic function to function handle for input. I then define an objective function obj_fun=#(x)f(x,c(x)). The objective function for optimization is
(alpha-1)/alpha*sum_{i=1}^N[(theta*(alpha-1)*V(x,Xi)+c)^(1/(alpha-1))*V(x,Xi)]+c/(theta*alpha*(1-alpha))
V(x,Xi) is any function. I use (x^{T}Xi)^2. Xi is the i-th row of central_Simulation_Points.
When I count the the time, Equ = 1./N.*sum((theta.*(alpha-1).*central_Simulation_Points*x).^2+c).^(1./(alpha-1)))==1; takes 203 seconds to run, but it seems that Constraint_equ = solve(Equ, c); never makes the program stops. I wonder if any other methods could be used instead of using
Constraint_equ = solve(Equ, c);
Constraint_equ_handle = matlabFunction(Constraint_equ);
Are there any ways to find c in terms of x, or any ways to modify the code?
When I type help gmres in MATLAB I get the following example:
n = 21; A = gallery('wilk',n); b = sum(A,2);
tol = 1e-12; maxit = 15;
x1 = gmres(#(x)afun(x,n),b,10,tol,maxit,#(x)mfun(x,n));
where the two functions are:
function y = afun(x,n)
y = [0; x(1:n-1)] + [((n-1)/2:-1:0)'; (1:(n-1)/2)'].*x+[x(2:n); 0];
end
and
function y = mfun(r,n)
y = r ./ [((n-1)/2:-1:1)'; 1; (1:(n-1)/2)'];
end
I tested it and it works great. My question is in both those functions what is the value for x since we never give it one?
Also shouldn't the call to gmres be written like this: (y in the #handle)
x1 = gmres(#(y)afun(x,n),b,10,tol,maxit,#(y)mfun(x,n));
Function handles are one way to parametrize functions in MATLAB. From the documentation page, we find the following example:
b = 2;
c = 3.5;
cubicpoly = #(x) x^3 + b*x + c;
x = fzero(cubicpoly,0)
which results in:
x =
-1.0945
So what's happening here? fzero is a so-called function function, that takes function handles as inputs, and performs operations on them -- in this case, finds the root of the given function. Practically, this means that fzero decides which values for the input argument x to cubicpoly to try in order to find the root. This means the user just provides a function - no need to give the inputs - and fzero will query the function with different values for x to eventually find the root.
The function you ask about, gmres, operates in a similar manner. What this means is that you merely need to provide a function that takes an appropriate number of input arguments, and gmres will take care of calling it with appropriate inputs to produce its output.
Finally, let's consider your suggestion of calling gmres as follows:
x1 = gmres(#(y)afun(x,n),b,10,tol,maxit,#(y)mfun(x,n));
This might work, or then again it might not -- it depends whether you have a variable called x in the workspace of the function eventually calling either afun or mfun. Notice that now the function handles take one input, y, but its value is nowhere used in the expression of the function defined. This means it will not have any effect on the output.
Consider the following example to illustrate what happens:
f = #(y)2*x+1; % define a function handle
f(1) % error! Undefined function or variable 'x'!
% the following this works, and g will now use x from the workspace
x = 42;
g = #(y)2*x+1; % define a function handle that knows about x
g(1)
g(2)
g(3) % ...but the result will be independent of y as it's not used.
but i want to make a program in which i can generate a function of multiple variables that depend on the number of rows of a matrix.
for k = 1:sizel;
f(k)=(alpha(k,1)+(beta(k,1)*p(k))+(gamma(k,1)*p(k)^2));
end
cost=(sum(f))
this is for the purpose of optimization so i need that at the end the variables are declares as p(1),p(2),p(3)... these will be the input for my function.
Note: i dont want to assign values to the variables because this will be done be the optimization algorithm in the optimization toolbox.
here is the complete code
function cost = cost(p) ;
clc
clear
costfunctionconstantsmatrix;
sizel=size(CostFormulaconstants);
alpha=CostFormulaconstants(:,1);
beta=CostFormulaconstants(:,2);
gamma=CostFormulaconstants(:,3);
for k = 1:sizel;
f(k)=(alpha(k,1)+(beta(k,1)*p(k))+(gamma(k,1)*p(k)^2));
end
cost=(sum(f))
end
i used the symbolic approach and i got the correct answer for the cost indeed, i got something like this: (53*p(1))/10 + (11*p(2))/2 + (29*p(3))/5 + p(1)^2/250 + (3*p(2)^2)/500 + (9*p(3)^2)/1000 + 1100. But when i try to specify my function to be optimized in the optimization toolbox it tells me that the variables p are sym and cannot be converted to double. the trouble is how to convert this expression to double so that the optimization algorithm can input values for the variable p(1), p(2) and p(3)
Can you pass the matrix as an argument to the function?
function cost = fcn(my_mat)
[m,n] = size(my_mat);
f = zeros(m,1);
for k = 1:m % for example
f(k)=(alpha(k,1)+(beta(k,1)*p(k))+(gamma(k,1)*p(k)^2));
end
cost = sum(f);
end
Your problem is not entirely clear to me but I believe you wish to generate a series of functions in which the variables alpha, beta, gamma are constants with different values for each function, and the vector p is an argument.
What confuses me in your question is that you use the index k for both the constants and the arguments, which I think is not what you intended to write. Assuming I understand your goal, a solution may make use of function handles.
The notation f(k) = #(p) p(1)+p(2) for example, generates a function that adds p(1) and p(2). Abbreviating CostFormulaconstants to cf, the following would generate a series of functions, one for each row in cf.
for k = 1 : size(cf, 1)
f{k} = #(p) cf(k,1) + cf(k,2)*p(1) + cf(k,3)*p(2)^2;
end
You can supply individual function handles to callers from the optimization toolbox simply with f{3} for the third function, for example. A call to f{3} would look like
a = f{3}([3,4]);
If your functions are indeed all polynomials, polyval may be worth a look as well.
EDIT: After clarification, the problem seems a bit simpler, no need for function handles. Why not simply
function c = cost(p)
c = 0;
cf = [...]; % your coefficients here.
for k = 1 : size(cf, 1)
c = c + cf(k,1) + cf(k,2)*p(k) + cf(k,3)*p(k)^2;
end
I have a problem with symbolic functions. I am creating function of my own whose first argument is a string. Then I am converting that string to symbolic function:
f = syms(func)
Lets say my string is sin(x). So now I want to calculate it using subs.
a = subs(f, 1)
The result is sin(1) instead of number.
For 0 it works and calculates correctly. What should I do to get the actual result, not only sin(1) or sin(2), etc.?
You can use also use eval() to evaluate the function that you get by subs() function
f=sin(x);
a=eval(subs(f,1));
disp(a);
a =
0.8415
syms x
f = sin(x) ;
then if you want to assign a value to x , e.g. pi/2 you can do the following:
subs(f,x,pi/2)
ans =
1
You can evaluate functions efficiently by using matlabFunction.
syms s t
x =[ 2 - 5*t - 2*s, 9*s + 12*t - 5, 7*s + 2*t - 1];
x=matlabFunction(x);
then you can type x in the command window and make sure that the following appears:
x
x =
#(s,t)[s.*-2.0-t.*5.0+2.0,s.*9.0+t.*1.2e1-5.0,s.*7.0+t.*2.0-1.0]
you can see that your function is now defined by s and t. You can call this function by writing x(1,2) where s=1 and t=1. It should generate a value for you.
Here are some things to consider: I don't know which is more accurate between this method and subs. The precision of different methods can vary. I don't know which would run faster if you were trying to generate enormous matrices. If you are not doing serious research or coding for speed then these things probably do not matter.
I have one file with the following code:
function fx=ff(x)
fx=x;
I have another file with the following code:
function g = LaplaceTransform(s,N)
g = ff(x)*exp(-s*x);
a=0;
b=1;
If=0;
h=(b-a)/N;
If=If+g(a)*h/2+g(b)*h/2;
for i=1:(N-1)
If=If+g(a+h*i)*h;
end;
If
Whenever I run the second file, I get the following error:
Undefined function or variable 'x'.
What I am trying to do is integrate the function g between 0 and 1 using trapezoidal approximations. However, I am unsure how to deal with x and that is clearly causing problems as can be seen with the error.
Any help would be great. Thanks.
Looks like what you're trying to do is create a function in the variable g. That is, you want the first line to mean,
"Let g(x) be a function that is calculated like this: ff(x)*exp(-s*x)",
rather than
"calculate the value of ff(x)*exp(-s*x) and put the result in g".
Solution
You can create a subfunction for this
function result = g(x)
result = ff(x) * exp(-s * x);
end
Or you can create an anonymous function
g = #(x) ff(x) * exp(-s * x);
Then you can use g(a), g(b), etc to calculate what you want.
You can also use the TRAPZ function to perform trapezoidal numerical integration. Here is an example:
%# parameters
a = 0; b = 1;
N = 100; s = 1;
f = #(x) x;
%# integration
X = linspace(a,b,N);
Y = f(X).*exp(-s*X);
If = trapz(X,Y) %# value returned: 0.26423
%# plot
area(X,Y, 'FaceColor',[.5 .8 .9], 'EdgeColor','b', 'LineWidth',2)
grid on, set(gca, 'Layer','top', 'XLim',[a-0.5 b+0.5])
title('$\int_0^1 f(x) e^{-sx} \,dx$', 'Interpreter','latex', 'FontSize',14)
The error message here is about as self-explanatory as it gets. You aren't defining a variable called x, so when you reference it on the first line of your function, MATLAB doesn't know what to use. You need to either define it in the function before referencing it, pass it into the function, or define it somewhere further up the stack so that it will be accessible when you call LaplaceTransform.
Since you're trying to numerically integrate with respect to x, I'm guessing you want x to take on values evenly spaced on your domain [0,1]. You could accomplish this using e.g.
x = linspace(a,b,N);
EDIT: There are a couple of other problems here: first, when you define g, you need to use .* instead of * to multiply the elements in the arrays (by default MATLAB interprets multiplication as matrix multiplication). Second, your calls g(a) and g(b) are treating g as a function instead of as an array of function values. This is something that takes some getting used to in MATLAB; instead of g(a), you really want the first element of the vector g, which is given by g(1). Similarly, instead of g(b), you want the last element of g, which is given by g(length(g)) or g(end). If this doesn't make sense, I'd suggest looking at a basic MATLAB tutorial to get a handle on how vectors and functions are used.