I know how to minimize a linear function $f : \mathbb{R}^{n} \rightarrow \mathbb{R}$ with CVXPY but in my problem the function $f$ is quadratic and hence the problem is now in the form : $$\lVert AW-b\rVert_{2}$$ where A is a $N\times N$ matrix which depends on the vector W.
More specifically, my full problem is the following : $$ \underset{w}{\operatorname{argmin}} \sum_{i=1}^{n}[w_{i}\times (\frac{Vw}{\sigma})_{i} - b_{i}]^{2}$$
where $V$ is the $N \times N$ covariance matrix, $\sigma = \sqrt{w^{T}Vw}$ is the standard deviation of the portfolio with weights $w$, and $b=(b_{i})_{i=1,...,N}$ is the (risk contribution) target vector.
Note that the left term in the sum $ w_{i}\times (\frac{Vw}{\sigma})_{i}$ is a real scalar product (in the $\mathbb{R}$ space) between $w_{i}$ and $(\frac{Vw}{\sigma})_{i}$ (the i-th element of the vector in parentheses).
Is it possible to minimize this quantity using cvxopt/cvxpy ? I managed to do it with scipy but the algorithm took a lot of time to find the solution.
