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
Related
I have N functions in MATLAB and I can define them using strcat, num2str and eval in a for loop. So without defining by hand I am able to define N functions. Let N=4 and let them be given as follows:
f1=#(x) a1*x+1;
f2=#(x) a2*x+1;
f3=#(x) a3*x+1;
f4=#(x) a4*x+1;
Now I add these four functions and I can do this by hand as follows:
f=#(x)(f1(x)+f2(x)+f3(x)+f4(x));
Here I can do it by hand because I know that N=4. However, in general I never know how many functions I will have. For all cases I cannot write a new function.
Is there any way to do this automatically? I mean if I give N=6 I am expecting to see MATLAB giving me this:
f=#(x)(f1(x)+f2(x)+f3(x)+f4(x)+f5(x)+f6(x));
Whenever I give N=2 then I must have the function f, defined as follows:
f=#(x)(f1(x)+f2(x));
How can we do this?
First of all, you should read this answer that gives a series of reasons to avoid the use of eval. There are very few occasions where eval is necessary, in all other cases it just complicates things. In this case, you use to dynamically generate variable names, which is considered a very bad practice. As detailed in the linked answer and in further writings linked in that answer, dynamic variable names make the code harder to read, harder to maintain, and slower to execute in MATLAB.
So, instead of defining functions f1, f2, f3, ... fN, what you do is define functions f{1}, f{2}, f{3}, ... f{N}. That is, f is a cell array where each element is an anonymous function (or any other function handle).
For example, instead of
f1=#(x) a1*x+1;
f2=#(x) a2*x+1;
f3=#(x) a3*x+1;
f4=#(x) a4*x+1;
you do
N = 4;
a = [4.5, 3.4, 7.1, 2.1];
f = cell(N,1);
for ii=1:N
f{ii} = #(x) a(ii) * x + 1;
end
With these changes, we can easily answer the question. We can now write a function that outputs the sum of the functions in f:
function y = sum_of_functions(f,x)
y = 0;
for ii=1:numel(f)
y = y + f{ii}(x);
end
end
You can put this in a file called sum_of_functions.m, or you can put it at the end of your function file or script file, it doesn't matter. Now, in your code, when you want to evaluate y = f1(x) + f2(x) + f3(x)..., what you write is y = sum_of_functions(f,x).
I have 100 equations with 5 variables. Is there a function in Matlab which I can use to find the optimal solution of these equations?
My problem is to find argmin ||(a-ic)^2 + (b-jd)^2 + e - h(i,j)|| over all i, j from -10 to 10. ie.
%% Note: not Matlab code. Just showing the Math.
for i = -10:10
for j = -10:10
(a-ic)^2 + (b-jd)^2 + e = h(i,j)
known: h(i,j) is a 10*10 matrix,and i,j are indexes
expected: the optimal result of a,b,c,d,e
You can try using lsqnonlin as follows.
%% define a helper function in your .m file
function f = fun(x)
a=x(1); b=x(2); c=x(3); d=x(4); e=x(5); % Using variable names from your question. In other situations, be careful when overwriting e.
f=zeros(21*21,0); % size(f) is taken from your question. You should make this a variable for good practice.
for i = -10:10
for j = -10:10
f(10*(i+10+1)+(j+10+1)) = (a-i*c)^2 + (b-j*d)^2 + e - h(i,j); % 10 is taken from your question.
end
end
end
(Aside, why is your h(i,j) taking negative indices??)
In your main function you can simply write
function out=myproblem(x0)
out=lsqnonlin(#fun,x0);
end
In your cmd, you can call with specific initial try such as
myproblem([0,0,0,0,0])
Helper function over anonymous because in my experience helpers get sped up by JIT while anonymous do not. I also opted to reshape in the loops as an opposed to actually call reshape after because I expect reshape to cost significant extra time. Remember that O(1) in fun is not O(1) in lsqnonlin.
(As always, a solution to a nonlinear problem is not guaranteed.)
I have a function F which takes as an input a vector a. Both the output of the function and a are vectors of length N, where N is arbitrary. Each component Fn is of the form g(a(n),a(n-k)), where g is the same for each component.
I want to implement this function in matlab using its symbolic functionality and calculate its Jacobian (and then store both the function and its jacobian as a regular .m file using matlabFunction). I know how to do this for a function where each input is a scalar that can be handled manually. But here I want a script that is capable of producing these files for any N. Is there a nice way to do this?
One solution I came up with is to generate an array of strings "a0","a1", ..., "aN" and define each component of the output using eval. But this is messy and I was wondering if there is a better way.
Thank you!
[EDIT]
Here is a minimal working example of my current solution:
function F = F_symbolically(N)
%generate symbols
for n = 1:N
syms(['a',num2str(n)]);
end
%define output
F(1) = a1;
for n = 2:N
F(n) = eval(sprintf('a%i + a%i',n,n-1));
end
Try this:
function F = F_symbolically(N)
a = sym('a',[1 N]);
F = a(1);
for i=2:N
F(i) = a(i) + a(i-1);
end
end
Note the use of sym function (not syms) to create an array of symbolic variables.
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).
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).