Imperial College London


Faculty of EngineeringDepartment of Computing

Research Associate in Computational Optimisation Research



francesco.ceccon14 Website




Huxley BuildingSouth Kensington Campus





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Ceccon F, Siirola J, Misener R, 2019, SUSPECT: MINLP special structure detector for pyomo, Optimization Letters, Vol: 14, Pages: 801-814, ISSN: 1862-4472

We present SUSPECT, an open source toolkit that symbolically analyzesmixed-integer nonlinear optimization problems formulated using the Python alge-braic modeling library Pyomo. We present the data structures and algorithms used toimplement SUSPECT. SUSPECT works on a directed acyclic graph representation ofthe optimization problem to perform: bounds tightening, bound propagation, mono-tonicity detection, and convexity detection. We show how the tree-walking rules inSUSPECT balance the need for lightweight computation with effective special struc-ture detection. SUSPECT can be used as a standalone tool or as a Python library to beintegrated in other tools or solvers. We highlight the easy extensibility of SUSPECTwith several recent convexity detection tricks from the literature. We also report ex-perimental results on the MINLPLib 2 dataset.

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