Hey Sergei,

Is it possible to constrain one variable subject to the value of another  variable to be optimised in HyPhy? For example, suppose I wish to maximise the function f(x,y) = exp{-(x-2)^2-(y-3)^2} w.r.t (x, y) s.t. x>y.

In the code below, HyPhy uses the initial value of a, rather than the optmised value in the constraint b :< a and hence gives x = 2, y=3.

Code:

function userFunction (x,y)
{
return Exp(-(x-2)^2-(y-3)^2);
}
a = 5;
b = 5;
b :< a;
Optimize (res, userFunction (a,b));
fprintf (stdout, " ", res, " ");
 

Is there a way around this?

Thanks,
Miguel

Thanks for this!

My actual problem is slightly more complicated though, and I am not sure how to resolve it using your trick above. I have two variables to be optimised, say x and y. The variable x is constrained to [0,1], while both the left and right limits of y depend on x; y is defined on [x, min(x/f,1)] for some fraction f. So...

y=a*x for a>1 and
y=b*min(x/f,1) for b<1
Hence a=b*min(x/f,1)/x > 1 while b < 1

I thought about optimising b :< 1 and setting a := Max(b*Min(x/f,1)/x, 1) but I'm sure this isn't correct since I am not actually optimising b such that a is appropriately constrained too (the Max command is just a fix for when the optimised value of b fails to satisfy the constraint on a).

Code:

	  global x  = 0.5;    x :>  0;  x :< 1;
	  global b = 0.5; b:<1;

	  global y := Max((b/x)*Min(x/f__,1),1)*x;
 

Any suggestions?

Thanks in advance!
Miguel

Thanks! The constraints are enforced, although I'm actually optimising the built-in LikelihoodFunction(), rather than a user function - hope the optimisation routine still behaves!

Thanks again for the help!
Miguel