Utilitarian algorithm configuration is a general-purpose technique for automatically searching the parameter space of a given algorithm to optimize its performance, as measured by a given utility function, on a given set of inputs. Recently introduced utilitarian configuration procedures offer optimality guarantees about the returned parameterization while provably adapting to the hardness of the underlying problem. However, the applicability of these approaches is severely limited by the fact that they only search a finite, relatively small set of parameters. They cannot effectively search the configuration space of algorithms with continuous or uncountable parameters. In this paper we introduce a new procedure, which we dub COUP (Continuous, Optimistic Utilitarian Procrastination). COUP is designed to search infinite parameter spaces efficiently to find good configurations quickly. Furthermore, COUP maintains the theoretical benefits of previous utilitarian configuration procedures when applied to finite parameter spaces but is significantly faster, both provably and experimentally.

翻译：实用算法配置是一种通用技术，用于自动搜索给定算法的参数空间，以优化其在给定输入集上的性能（通过给定的效用函数衡量）。近期提出的实用配置方法在保证返回参数化方案最优性的同时，能够被证明适应于底层问题的难度。然而，这些方法的适用性受到严重限制，因为它们仅能搜索有限且相对较小的参数集合，无法有效处理具有连续或不可数参数的算法配置空间。本文提出一种新方法，我们称之为COUP（连续乐观实用延迟配置法）。COUP旨在高效搜索无限参数空间以快速发现优质配置。此外，当应用于有限参数空间时，COUP保留了先前实用配置方法的理论优势，同时在可证明性和实验验证中均表现出显著更快的速度。