optimization problem with two variable maxima and minima
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can any one give me the solution or help me out in solving this equation mathamatically
Y=2x(1)^2 + 23.08x(2)^2 +4(6+x(1))^2 +24+14(x(1)^2 +x(2)^2)^0.5 +3(x(1)^2 + x(2)^2)
the other equation is 1=x(1)*x(2)
7 Comments
Matt J
on 6 Sep 2019
Which variable is the unknown? You only have 1 equation, so only one of the variables can be.
manish kumar
on 6 Sep 2019
Torsten
on 6 Sep 2019
Differentiate Y with respect to x(1) and x(2), set the derivatives to 0 and solve for x(1) and x(2).
help diff
help solve
manish kumar
on 6 Sep 2019
Torsten
on 6 Sep 2019
I don't understand what you mean.
manish kumar
on 6 Sep 2019
Torsten
on 6 Sep 2019
Please show your code.
Answers (1)
fun=@(x) [2*x(1)^2+23.08*x(2)^2+4*(6+x(1))^2+24+14*(x(1)^2 +x(2)^2)^0.5+3*(x(1)^2+x(2)^2)-Y;...
prod(x)-1];
x=fsolve(fun,initial_guess)
9 Comments
manish kumar
on 9 Sep 2019
Matt J
on 9 Sep 2019
You probably need to remove all the spaces in the expression for fun.
fun=@(x) [2*x(1)^2+23.08*x(2)^2+4*(6+x(1))^2+24+14*(x(1)^2 +x(2)^2)^0.5+3*(x(1)^2+x(2)^2)-Y;...
prod(x)-1];
x=fsolve(fun,initial_guess)
manish kumar
on 11 Sep 2019
Walter Roberson
on 11 Sep 2019
You should have used
initial_guess = [-42, pi];
manish kumar
on 11 Sep 2019
Torsten
on 11 Sep 2019
It's still not clear to me what you are trying to do.
Do you want to solve the two equations
Y=2x(1)^2 + 23.08x(2)^2 +4(6+x(1))^2 +24+14(x(1)^2 +x(2)^2)^0.5 +3(x(1)^2 + x(2)^2)
1=x(1)*x(2)
for x(1) and x(2) (for a given value of Y)
or do you want to determine maximum and minimum of
Y(x(1),x(2)) = 2x(1)^2 + 23.08x(2)^2 +4(6+x(1))^2 +24+14(x(1)^2 +x(2)^2)^0.5 +3(x(1)^2 + x(2)^2)
under the constraint
1 = x(1)*x(2)
or ...
manish kumar
on 11 Sep 2019
Torsten
on 11 Sep 2019
fun= @(x)2*x.^2+23.08*(1./x).^2+4*(6+x).^2+24+14*(x.^2+(1./x).^2).^0.5+3*(x.^2+(1./x).^2)
x0 = 1.0;
xmin = fminsearch(fun,x0)
Bruno Luong
on 11 Sep 2019
Edited: Bruno Luong
on 11 Sep 2019
Careful on local minimum
>> xmin = fminsearch(fun,1), fun(xmin) % not global minimum
xmin =
0.9418
ans =
270.4623
>> xmin = fminsearch(fun,-2), fun(xmin)
xmin =
-2.2066
ans =
142.7984
>>
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