sequential convex programming
时间: 2023-10-09 21:03:57 浏览: 37
Sequential convex programming (SCP) is a numerical optimization technique used to solve non-convex optimization problems by iteratively solving a sequence of convex subproblems. The idea behind SCP is to approximate the original non-convex problem with a sequence of convex subproblems that can be solved efficiently using standard optimization techniques.
The basic idea of SCP is to divide the original non-convex problem into a sequence of convex subproblems, and then solve each subproblem using a convex optimization algorithm. The solution to one subproblem is then used as the starting point for the next subproblem. This process is repeated until convergence is achieved.
SCP is often used in applications where the objective function is non-convex but can be decomposed into a sum of convex functions. In this case, SCP can be used to iteratively solve each convex subproblem and obtain a solution to the original non-convex problem.
SCP has been applied to a wide range of optimization problems, including machine learning, control, and signal processing. It is particularly useful in applications where the objective function is non-smooth or has non-convex constraints.