My objective function is y’Ky + y’*L, which is quadratic. I know that in order this to be convex, K must be positive semidefinite.
In my case K is a sparse 25x25 matrix and its eigen values are roughly:
1st eigen value: 1000
2nd eigen value: 1
3rd eigen value: 0.5
4th eigen value: 0.2
and the rest is all zero.
I get an an error from function quad_form(), when i run cvx:
“The second argument must be positive or negative semidefinite”.
Even though all eigen values are non negative, how do i not have semidefinite matrix of K.
My code is
minimize y’Ky + y’*L
Ps: There are some constraint equalities as well, but they dont affect whether I get this error or not.