sequential convex programming

Sequential Convex Programming (SCP) is a numerical optimization technique used to solve non-linear optimization problems. SCP is a variant of the Sequential Quadratic Programming (SQP) algorithm that deals with non-convex, non-linear optimization problems.

SCP works by iteratively solving a sequence of convex optimization sub-problems, where each sub-problem is obtained by approximating the original problem with a convex function. The solution of each sub-problem is then used to update the current solution of the original problem.

SCP has several advantages over other optimization techniques, such as guaranteed convergence to a local minimum, the ability to handle non-convex functions, and the ability to handle constraints.

SCP is commonly used in various fields, including engineering, economics, and finance, where it is used to optimize complex systems and models.