论文标题
奖杯：使用精确层次结构的信任区域优化
TROPHY: Trust Region Optimization Using a Precision Hierarchy
论文作者
论文摘要
我们提出了一种算法，用于对非线性无约束问题进行基于信任区域的优化。该方法在不同的浮点精度下选择性地使用功能和梯度评估来降低整体能源消耗，存储和通信成本；在Exascale计算时代，这些功能越来越重要。特别是，我们是出于提高大规模气候模型计算效率的愿望而激发的。我们在两个示例中采用了我们的方法：最可爱的测试集和一个大规模的数据同化问题，以从雷达回报中恢复风场。尽管本文主要是概念证明，但我们表明，如果在适当的硬件上实施，与固定精确求解器相比，混合精液的使用可以大大减少计算负载。
We present an algorithm to perform trust-region-based optimization for nonlinear unconstrained problems. The method selectively uses function and gradient evaluations at different floating-point precisions to reduce the overall energy consumption, storage, and communication costs; these capabilities are increasingly important in the era of exascale computing. In particular, we are motivated by a desire to improve computational efficiency for massive climate models. We employ our method on two examples: the CUTEst test set and a large-scale data assimilation problem to recover wind fields from radar returns. Although this paper is primarily a proof of concept, we show that if implemented on appropriate hardware, the use of mixed-precision can significantly reduce the computational load compared with fixed-precision solvers.
