In this article, we propose an interval constraint programming method for globally solving catalog-based categorical optimization problems. It supports catalogs of arbitrary size and properties of arbitrary dimension, and does not require any modeling effort from the user. A novel catalog-based contractor (or filtering operator) guarantees consistency between the categorical properties and the existing catalog items. This results in an intuitive and generic approach that is exact, rigorous (robust to roundoff errors) and can be easily implemented in an off-the-shelf interval-based continuous solver that interleaves branching and constraint propagation. We demonstrate the validity of the approach on a numerical problem in which a categorical variable is described by a two-dimensional property space. A Julia prototype is available as open-source software under the MIT license at https://github.com/cvanaret/CateGOrical.jl

翻译：本文提出一种基于区间约束规划的全局求解方法，用于处理基于编目的分类优化问题。该方法支持任意规模的编目及任意维度的属性空间，且无需用户进行任何建模工作。通过设计一种新型的基于编目的约束传播算子（过滤算子），保证了分类属性与现有编目条目之间的一致性。由此形成的通用方法直观且具有严格性（对舍入误差具有鲁棒性），可轻松集成至商业化的基于区间的连续求解器中——该类求解器通过交替执行分支定界与约束传播机制实现优化。我们通过一个数值算例验证了该方法的有效性，该算例中分类变量由二维属性空间描述。基于Julia语言开发的开源原型系统（MIT许可证）已发布于 https://github.com/cvanaret/CateGOrical.jl。