CVXPY allows us to cast convex optimization tasks into OOP code.
\begin{align} \min \mid Ax - b \mid^{2}_{2} \end{align}
object to: x \geq 0 import cvxpy as cp A,b = ... x = cp.Variable(n) obj = cp.norm2(A@x - b)**2 constraints = [x >= 0] prob = cp.Problem(cp.Minimize(obj), constraints) prob.solve() How it works starts with the optimization problem P_{1} applies a series of problem transformation P_{2} … P_{N} final problem P_{N} should be one of Linear Program, Quadratic Program, SOCP, SDP calls a specialized solver on P_{N} retrieves the solution of the original problem by reversing transformations