Need help a bout this problem

Hello friends!

I am new to optimization.
My objective junction is a weighted sum of elements of my input vector which i want to optimize them;

i mean:

Objective function: minimize (sum (pi * (1 - pci)))

in which pci is a known vector weights the pi vector elements.

i have some other constrains that one of them is not linear and is like this:

(sum( sum( pi(i) * pi(j) * Aij) ) ) * sum(pi*pci) * (sum (pi))^k <= Constant value.

pi(i) is the ith element of pi and pi(j) is the jth element of pi and Aij is a factor that depends on i and j.
can i simulate this problem by CVX???

is it a quasi-convex optimization problem??

The objective function is linear, but the constraint is non-convex, so can not be handled by CVX. The first multiplicative term of the constraint though is p’Ap, which if <= Constant by itself without the other multiplicative terms would be convex and expressible in CVX if A were psd. However, the constraint as written is not convex.

Read Why isn't CVX accepting my model? READ THIS FIRST! and .