We present SUSPECT, an open source toolkit that symbolically analyzes mixed-integer nonlinear optimization problems formulated using the Python algebraic modeling library Pyomo. We present the data structures and algorithms used to implement SUSPECT. SUSPECT works on a directed acyclic graph representation of the optimization problem to perform: bounds tightening, bound propagation, monotonicity detection, and convexity detection. We show how the tree-walking rules in SUSPECT balance the need for lightweight computation with effective special structure detection. SUSPECT can be used as a standalone tool or as a Python library to be integrated in other tools or solvers. We highlight the easy extensibility of SUSPECT with several recent convexity detection tricks from the literature. We also report experimental results on the MINLPLib 2 dataset.

中文翻译：

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怀疑：用于pyomo的MINLP特殊结构检测器

我们介绍了SUSPECT，这是一个开放源代码工具包，可对使用Python代数建模库Pyomo制定的混合整数非线性优化问题进行符号分析。我们介绍了用于实现SUSPECT的数据结构和算法。SUSPECT对有问题的优化问题进行有向无环图表示，以执行以下任务：边界收紧，边界传播，单调性检测和凸度检测。我们展示了SUSPECT中的树遍历规则如何通过有效的特殊结构检测来平衡对轻量级计算的需求。SUSPECT可以用作独立工具，也可以用作要集成到其他工具或求解器中的Python库。我们通过文献中的几种最新的凸度检测技巧，突出了SUSPECT的易扩展性。我们还将在MINLPLib 2数据集上报告实验结果。