SUSPECT: MINLP special structure detector for Pyomo
- Imperial College London, London (United Kingdom). Dept. of Computing
- 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 Lab. (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, Vol. 14; ISSN 1862-4472
- Publisher:
- Springer NatureCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure
An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs