Python solver for a linear objective function with quadratic constraints

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I need to optimize a problem of the following form within Python

$ minimize$

$\qquad abs(\Delta w) \;\epsilon^T$

$s.t.$

$\qquad(w+\Delta w)C(w+\Delta w)^T \; \le 9500 (some\; finite\; number) \\\\[2ex] \qquad\frac{\Delta w_i}{w_i} \gt -1 \; \forall\; i$

$\text{w = Vector of weights}$

$\Delta\text{w = Vector of change in weights}$

$\epsilon\text{ = Vector of costs}$

$\text{C = Variance Covariance matrix}$