Reputation: 21
I have a few questions about setting up NLopt with non-linear constraints:
grad[ ]
in the constraint function? Is there any (automatic) method to solve the problem without introducing Lagrangian multiplier?Using a Lagrangian multiplexer, I know we can solve the problem. However the use of Lagrangian multiplexer we have to obtain my_constraint_data
manually, which make it difficult to solve large-scale problem.
For example, suppose I want to minimize the function
f(x1,x2) = -((x1)^3)-(2*(x2)^2)+(10*(x1))-6-(2*(x2)^3)
subject to the following constraints:
Constraint 1: c1 = 10-(x1)*(x2) >= 0
Constraint 2: c2 = ((x1)*(x2)^2)-5 >= 0
Constraint 3: c3 = (x2)-(x1)*(x2)^3 >= 0
In NLopt tutorial, we know that grad[0] = d(c1)/d(x1)
and grad[1] = d(c2)/d(x2)
as the gradient of constraints. Then, we set grad
as follows:
double myconstraint(unsigned n, const double *x, double *grad, void *data) {
my_constraint_data *d = (my_constraint_data *)data;
if (grad) {
grad[0] = -x[1]; //grad[0] = d(c1)/dx[1]
grad[1] = 2*x[0]+x[1]; //grad[1] = d(c2)/dx[2]
grad[2] = ???; //grad[2] = d(c3)/dx[3] but we only have 2 variable (x1)&(x2)
}
return (10-x[0]*x[1], x[0]*x[1]*x[1]-5, x[1]-x[0]*x[1]*x[1]*x[1];
}
The problem is we do not know how to set grad[ ]
(especially for c3
) if the number of constraints are larger than the number of variables.
Of course we can solve the problem with non-automatic method like below by using Lagrangian multiplexer (l1, l2, l3) where
grad[0] = -l1*(d(c1)/d(x1))-l2*(d(c2)/d(x1))-l3*(d(c)/d(x1))
and
grad[1] = -l1*(d(c1)/d(x2))-l2*(d(c2)/d(x2))-l3*(d(c)/d(x3))
double myconstraint(unsigned n, const double *x, double *grad, void *data) {
my_constraint_data *d = (my_constraint_data *)data;
//set l1, l2, and l3 as parameter of lagrangian multiplier
double l1=d->l1,l2=d->l2,l3=d->l3;
++count;
if (grad) {
grad[0] = l1*x[1]-l2*x[1]*x[1]-l3*x[1]*x[1]*x[1];
grad[1] = l1*x[0]-2*l2*x[0]*x[1]-l3+3*l3*x[0]*x[1]*x[1];
}
return (10-x[0]*x[1], x[0]*x[1]*x[1]-5, x[1]-x[0]*x[1]*x[1]*x[1]);
}
Meanwhile, it is not easy to apply non-automatic method into large-scale problem because it will be inefficient and complicated in programming.
We appreciate for your answer. It will be really helpful to us. Thank you for all your kindness.
Upvotes: 2
Views: 1787
Reputation: 16189
I think you've got the constraints and the variables you are minimizing mixed up. If I understand your question correctly, you need to create three separate constraint functions for your three constraints. For example:
double c1(unsigned n, const double *x, double *grad, void *data)
{
/* Enforces the constraint
*
* 10 - x1*x2 >= 0
*
* Note we compute x1*x2 - 10 instead of 10 - x1*x2 since nlopt expects
* inequality constraints to be of the form h(x) <= 0. */
if (grad) {
grad[0] = x[1]; // grad[0] = d(c1)/dx1
grad[1] = x[0]; // grad[1] = d(c1)/dx2
}
return x[0]*x[1] - 10;
}
double c2(unsigned n, const double *x, double *grad, void *data)
{
/* Enforces the constraint
*
* x1*x2^2 - 5 >= 0
*
* Note we compute -x1*x2^2 - 5 instead of x1*x2^2 - 5 since nlopt expects
* inequality constraints to be of the form h(x) <= 0. */
if (grad) {
grad[0] = -x[1]*x[1];
grad[1] = -2*x[0]*x[1];
}
return -x[0]*x[1]*x[1] + 5;
}
Then, in your main
function you need to add each inequality constraint separately:
int main(int argc, char **argv)
{
// set up nlopt here
/* Add our constraints. */
nlopt_add_inequality_constraint(opt, c1, NULL, 1e-8);
nlopt_add_inequality_constraint(opt, c2, NULL, 1e-8);
// etc.
}
Upvotes: 0