SUSPECT: MINLP special structure detector for Pyomo

Journal Article · 15 February 2019 · Optimization Letters

DOI: https://doi.org/10.1007/s11590-019-01396-y · OSTI ID:1496978

Ceccon, Francesco ^[1]; Siirola, John D. ^[2]; Misener, Ruth  ^[1]

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1. Imperial College London, London (United Kingdom). Dept. of Computing
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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. Here, 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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1496978
• Report Number(s): SAND--2019-1376J; 672405
• Journal Information: Optimization Letters, Journal Name: Optimization Letters Vol. 14; ISSN 1862-4472
• Publisher: Springer NatureCopyright Statement
• Country of Publication: United States
• Language: English