The time complexity of algorithms, i.e., the scaling of the time required for solving a problem instance as a function of instance size, is of key interest in theoretical computer science and practical applications. In this work, we present a fully automated tool – Empirical Scaling Analyzer (ESA)– for performing sophisticated and detailed empirical scaling analyses. The methodological approach underlying ESA is based on a combination of automatic function fitting and bootstrap sampling; previous versions of the methodology have been used in prior work to characterize the empirical scaling behaviour of several prominent, high-performance SAT and TSP solvers. ESA is applicable to any algorithm or system, as long as running time data can be collected on sets of problem instances of various sizes. We present results from rigorous stress-testing to critically assess ESA on scenarios with challenging characteristics. We also give an overview of empirical scaling results obtained using ESA.

中文翻译：

---

经验标度分析器：用于对绩效标度进行经验分析的自动化系统

算法的时间复杂性，即解决问题实例所需时间的比例随实例大小的变化，在理论计算机科学和实际应用中至关重要。在这项工作中，我们提供了一个全自动工具–经验标度分析器（ESA）–用于执行复杂而详细的经验标度分析。ESA的方法学方法基于自动功能拟合和自举抽样的结合；在先前的工作中已使用该方法的早期版本来表征几个突出的高性能SAT和TSP求解器的经验缩放行为。ESA适用于任何算法或系统，只要可以在各种大小的问题实例集上收集运行时间数据即可。我们提出严格的压力测试结果，以严格评估具有挑战性特征的方案的ESA。我们还概述了使用ESA获得的经验定标结果。