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：本文提出了一种区间约束规划方法，用于全局求解基于编目的类别优化问题。该方法支持任意大小的编目和任意维度的属性，且无需用户进行任何建模工作。一种新颖的基于编目的收缩算子（或过滤算子）确保了类别属性与现有编目条目之间的一致性。由此产生了一种直观且通用的方法，该方法精确、严格（对舍入误差鲁棒），并且可轻松实现在现成的基于区间的连续求解器中，该求解器通过交替分支与约束传播进行工作。我们通过一个数值问题验证了该方法的有效性，该问题中类别变量由二维属性空间描述。基于Julia的原型在MIT许可下作为开源软件发布，地址为：https://github.com/cvanaret/CateGOrical.jl