sequential convex programming

Sequential Convex Programming (SCP) is a mathematical optimization technique used to solve non-convex optimization problems. It is an iterative algorithm that breaks down a non-convex optimization problem into a sequence of convex optimization sub-problems, which are then solved in a sequential manner.

SCP is particularly useful in problems where the objective function is non-convex, but the constraints are convex. The algorithm starts with an initial feasible solution and then iteratively improves the solution by solving a sequence of convex sub-problems that are obtained by linearizing the objective function around the current solution.

At each iteration, SCP solves a convex optimization problem that is obtained by approximating the non-convex objective function with a convex function. The solution of the convex problem is then used to update the current solution, and the process is repeated until convergence is achieved.

SCP has been successfully applied to a wide range of optimization problems, including machine learning, signal processing, control systems, and finance. It is a powerful tool for solving complex optimization problems that are difficult to solve using other techniques.