I have an optimization problem which should be solvable with Quadratic Programming:
There are $n$ multiplication coefficients $c_i$ for which optimized values are searched.
The coefficients are multiplied by an individual vector and summed up to a new vector:
$\pmb v^{sum} = \sum_{i =0}^n c_i \cdot \pmb v_i$
Finally, the mean squared error between $\pmb v^{sum}$ and a vector $\pmb u$ is formed:
$mse = \frac{1}{m}\sum_{i = 0}^m (u_i - v^{sum}_i)^2$
The restrictions for values in the $\pmb c$ vector are:
They are not allowed to be negative:
$c_i \ge 0.0$
They must have a sum of $1.0$:
$\sum_{i =0}^n c_i = 1.0$
So I want to find optimized $c_i$ values for which the $mse$ is minimized while considering the restrictions.
The minimization should be done with Quadratic Programming:
$minimize\ \ \ \frac{1}{2}\pmb x^T \pmb P\pmb x+\pmb q^T\pmb x$
$subject\ to\ \ \ \pmb{Gx} \leq \pmb h$
$\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \pmb{Ax} = \pmb b$
Can someone help me translate the problem into the Quadratic Programming form so I can put it into a solver?