An efficient arc-search interior-point algorithm for convex quadratic programming with box constraints

This paper proposes an arc-search interior-point algorithm for convex quadratic programming with box constraints. The problem has many applications, such as optimal control with actuator saturation. It is shown that an explicit feasible starting point exists for this problem. Therefore, an efficient feasible interior-point algorithm is proposed to tackle the problem. It is proved that the proposed algorithm is polynomial and has the best known complexity bound O (root nlog(1/is an element of). Moreover, the search neighborhood for this special problem is wider than an algorithm for general convex quadratic programming problems, which implies that longer steps and faster convergence are expected. Finally, an engineering design problem is considered and the algorithm is applied to solve the engineering problem.

Automated summary

This paper proposes an arc-search interior-point algorithm for convex quadratic programming with box constraints and demonstrates its complexity bound and advantages. Furthermore, an engineering design problem is used to validate the effectiveness of the algorithm.