sequential convex programming
时间: 2023-10-09 16:05:52 浏览: 46
Sequential convex programming (SCP) is a type of optimization algorithm that solves non-convex optimization problems by iteratively solving a sequence of convex sub-problems. SCP is a combination of two optimization techniques: convex programming and sequential programming.
In SCP, the non-convex optimization problem is first approximated by a sequence of convex sub-problems that are easier to solve. The solution of each convex sub-problem is used to update the solution of the non-convex problem. The process continues until a satisfactory solution is obtained.
SCP is particularly useful in solving non-convex optimization problems in which the objective function or constraints are non-linear or non-convex. It is commonly used in applications such as optimal control, machine learning, and signal processing.
SCP has several advantages over other optimization methods. Firstly, it is computationally efficient since it does not require solving the entire non-convex problem at each iteration. Secondly, it can handle complex constraints and non-linear objective functions. Finally, SCP can guarantee convergence to a local optimal solution, which is often sufficient for many practical applications.