论文标题
昂贵的无衍生化非平滑复合材料优化的结构感知方法
Structure-Aware Methods for Expensive Derivative-Free Nonsmooth Composite Optimization
论文作者
论文摘要
我们提出了解决一系列广泛的约束非平滑复合最小化问题的新方法。这些方法是专门为目标设计而设计的，这些目标是来自计算量函数的输出的一些已知映射。我们提供了这些方法的随附实现：特别是，一种新型的歧管抽样算法（\ MSPShorref），其子问题在某种意义上是由以前的流形样品方法和方法（\ goombahref）解决的双重问题的原始版本（\ goombahre）来解决的。对于这两种方法，我们提供了严格的合并分析和保证。我们证明了这些方法的广泛测试。可以在\ url {github.com/poptus/ibcdfo/}中找到本手稿中开发的方法的开源实现。
We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (\mspshortref) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (\goombahref) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at \url{github.com/POptUS/IBCDFO/}.
