论文标题
部分可观测时空混沌系统的无模型预测
Exact Penalties for Decomposable Optimization Problems
论文作者
论文摘要
We consider a general decomposable convex optimization problem. By using right-hand side allocation technique, it can be transformed into a collection of small dimensional optimization problems.主问题是凸的非平滑优化问题。我们建议采用确切的非平滑惩罚方法，该方法在某些固定的惩罚参数下提供了初始问题的解决方案，并提供了较低级别问题的一致性。 The master problem is suggested to be solved by a two-speed subgradient projection method, which enhances the step-size selection. Preliminary results of computational experiments confirm its efficiency.
We consider a general decomposable convex optimization problem. By using right-hand side allocation technique, it can be transformed into a collection of small dimensional optimization problems. The master problem is a convex non-smooth optimization problem. We propose to apply the exact non-smooth penalty method, which gives a solution of the initial problem under some fixed penalty parameter and provides the consistency of lower level problems. The master problem is suggested to be solved by a two-speed subgradient projection method, which enhances the step-size selection. Preliminary results of computational experiments confirm its efficiency.
