Thank you for your answer. In the Sequential Convex Programming (SCP) approach, the original nonlinear optimization problem is approximated as a series of convex optimization subproblems, and iteratively solving these subproblems can obtain feasible or optimal solutions to the original problem. In nonlinear optimization, the idea of sequential linear programming or quadratic programming is exactly the same. Although the SCP method can retain more non-convex terms, it cannot be distinguished from nonlinear optimization based on this feature alone.