I wonder if there are any experts in optimization and linear/nonconvex/semidefinite programming that can help me with a peculiar linear-programming problem. The problem is this:
maximize (or minimize) a.x
given these constraints:
b_k . x ≥ r_k (k = 1, 2, ...)
c_l . x = s_l (l = 1, 2, ...)
x . A x = 0
x_i ≥ 0
where x, b_k, c_l are column vectors, "." the dot-product, and A a symmetric matrix that is not necessarily positive definite.
The problem is peculiar because it would just be a linear one, if it weren't for the quadratic constraint. But the quadratic constraint is homogeneous and a strict equality.
Is there an algorithm to find the exact, global solution? I'd be thankful for pointers to relevant literature at least. I'm studying from several texts but the literature in this field is huge, with so many different cases and approaches.
https://math.stackexchange.com/q/5100707/455507
#mathematics #optimization #linearprogramming #semidefiniteprogramming #nonconvexprogramming