- Function to compute derivative. Mine is called "derivative."
- Function to compute objective function. Mine is called "objectiveFunction."
How to solve differential equations with parameters using fmincon to find out optimized parameters
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Hello, I need to find out the optimized two parameter(a and b) to make minimum of (f2+f1-0.576). What I have is the differential equations of f1,f2,and f3.
for example, f1'=3*a/b*f2*f1+16*(f3-f1) f2'=-3*a/b*f2*f1+(f3-f1) f3'= 5*a/b*f1+(f2+f3) f1(0)=f2(0)=f3(0)=1 and f2(1)+f1(1)=0.576 for 0<-x<-1. The ranges of parameters are 0<-a<-10 and 100<-b<-1000.
And I want to use fmincon to optimize a and b to make minimum of (f2(1)+f1(1)-0.576). I can solve the differential equations with the fixed a and b. But I don't know how to find out the optimized a and b. My fmincon equation is, [x,f]=fmincon (@myfun,...)
F=myfun (x,a,b) F=f2(1)+f1(1)-0.576
In F equation, there is no a or b, so I can not set the initial,Ub, or Lb for a and b in fmincon. Actually, I don't know how to solve the differential equations with ode45 without a and b now. Can anyone help me to solve the equation? Thank you in advance.
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Jason Nicholson
2014-6-19
编辑:Jason Nicholson
2014-6-19
You need two functions:
Once you have the objective function, call fmincon. I do this in runOptimization. Note that
a = 3.2737
b = 3.2530
runOptimization.m
ab0 = [1; 1]; % initial guess
A = [ 1 0; % a<100
-1 0; % a>0
0 1; % b<22
0 -1];% b>0
b = [100*(1-eps); % a<100
0-eps; % a>0
22*(1-eps); % b<22
0-eps]; % b>0
ab = fmincon(@objectiveFunction, ab0, A, b);
a = ab(1);
b = ab(2);
derivative.m
function df = derivative(x, f, ab)
a = ab(1);
b = ab(2);
df = zeros(3,1);
df(1)=3*a/b*f(2)*f(1)+16*(f(3)-f(1));
df(2)=-3*a/b*f(2)*f(1)+(f(3)-f(1));
df(3)= 5*a/b*f(1)+(f(2)+f(3));
end % end function, derivative
objectiveFunction.m
function cost = objectiveFunction(ab)
f0 = [1;1;1];
[~, f] = ode113(@(t,f) derivative(t, f, ab), [0 1], f0);
cost = f(2, end) + f(1, end) - 0.576;
end % end function, objectiveFunction
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Jason Nicholson
2014-6-19
To be honest, I don't know. What I do know is you can check to see if it gives you the lowest "cost" in the objective function. You can also read the help on the different algorithms available. Some of the algorithms do not use a gradient. Some do. Some can handle discontinuous cost functions. Good luck.
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