论文标题
在衍生式免费优化中利用问题结构
Exploiting problem structure in derivative free optimization
论文作者
论文摘要
引入了一种结构化的无衍生物随机模式搜索优化算法，该算法能够利用在不受限制的和界限受约束的优化问题中通常存在的坐标部分可分开的结构（通常与稀疏性相关联）。该技术通过数量级提高了性能，并可以解决大型问题，否则其他无衍生方法可以完全棘手。还描述了基于插值的建模工具的库，可以与初始模式搜索算法的结构化或非结构化版本相关联。图书馆的使用进一步增强了性能，尤其是与结构相关联时。首先在[Porcelli，Toint，Acm Toms，2017年]中引入了与这两种技术相关的性能的显着增长。提出的数值结果的一个有趣的结论是，与尝试构建本地泰勒型模型相比，提供有关问题的全局结构信息可能会导致对目标函数的评估明显少得多。
A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constrained optimization problems. This technique improves performance by orders of magnitude and makes it possible to solve large problems that otherwise are totally intractable by other derivative-free methods. A library of interpolation-based modelling tools is also described, which can be associated to the structured or unstructured versions of the initial pattern search algorithm. The use of the library further enhances performance, especially when associated with structure. The significant gains in performance associated with these two techniques are illustrated using a new freely-available release of the BFO (Brute Force Optimizer) package firstly introduced in [Porcelli,Toint, ACM TOMS, 2017], which incorporates them. An interesting conclusion of the numerical results presented is that providing global structural information on a problem can result in significantly less evaluations of the objective function than attempting to building local Taylor-like models.
