I have a function that is convex, but when I run, I get the error:
Disciplined convex programming error:
Invalid quadratic form(s): not a square.
I understand what it means, all of that. Am asking for a way to get my convex problem to run… whether that be to reformulate things, up to using a different solver or writing a new atom function, or whatever… My optimization problem:
min over X (sum over ii(2*h1,ii *X - XT*h1,iiT*h2,ii*X + 0.5*XT*h1,iiT*h1,ii*X)) Subject to: 0<= X <= 1, 1T*X = a
where X is a column vector, all h’s are row vectors of positive numbers, a is a positive constant integer.
So, we can see in the second term of my sum, h1,iiT*h2,ii is not positive definite, since it is not transpose symmetric. However, when taking the Hessian of my objective function, all numbers are positive. Since X is constrained to non-negative values, my function is convex over the range of optimization.
advice for how to use this with CVX or some other solver ?