$$ \newcommand{\pmi}{\operatorname{pmi}} \newcommand{\inner}[2]{\langle{#1}, {#2}\rangle} \newcommand{\Pb}{\operatorname{Pr}} \newcommand{\E}{\mathbb{E}} \newcommand{\RR}{\mathbf{R}} \newcommand{\script}[1]{\mathcal{#1}} \newcommand{\Set}[2]{\{{#1} : {#2}\}} \newcommand{\argmin}[2]{\underset{#1}{\operatorname{argmin}} {#2}} \newcommand{\optmin}[3]{ \begin{align*} & \underset{#1}{\text{minimize}} & & #2 \\ & \text{subject to} & & #3 \end{align*} } \newcommand{\optmax}[3]{ \begin{align*} & \underset{#1}{\text{maximize}} & & #2 \\ & \text{subject to} & & #3 \end{align*} } \newcommand{\optfind}[2]{ \begin{align*} & {\text{find}} & & #1 \\ & \text{subject to} & & #2 \end{align*} } $$
CVXPY converts problems into conic form by using Grant and Boyd’s graph implementation methodology (note that this will not be true for all problems in CVXPY 1.0). CVXPY therefore interfaces only with cone solvers. Convexity is verified via disciplined convex programming.