Recently, several authors have suggested the use of first order methods, such as fast dual ascent and the alternating direction method of multipliers, for embedded model predictive control. The main reason is that they can be implemented using simple arithmetic operations only. However, a known limitation of gradient-based methods is that they are sensitive to ill-conditioning of the problem data. In this paper, we present a fast dual gradient method for which the sensitivity to ill-conditioning is greatly reduced. This is achieved by approximating the negative dual function with a quadratic upper bound with different curvature in different directions in the algorithm, as opposed to having the same curvature in all directions as in standard fast gradient methods. The main contribution of this paper is a characterization of the set of matrices that can be used to form such a quadratic upper bound to the negative dual function. We also describe how to choose a matrix from this set to get an improved approximation of the dual function, especially if it is ill-conditioned, compared to the approximation used in standard fast dual gradient methods. This can give a significantly improved performance as illustrated by a numerical evaluation on an ill-conditioned AFTI-16 aircraft model.
Robust and Efficient Embedded Convex Optimization through First-Order Adaptive Caching
School of Engineering and Applied Science, Columbia University（哥伦比亚大学工程与应用科学学院）；Barnard College, Columbia University（哥伦比亚大学巴纳德学院）；Dartmouth College（达特茅斯学院）
