This library implements methods to:
- Detect convex and concave expressions
- Detect increasing and decreasing expressions
- Detect linear, quadratic and polynomial expressions
- Tighten expression bounds
Please reference this software as
@Article{Suspect2019,
author={Ceccon, Francesco and Siirola, John D. and Misener, Ruth},
title={{SUSPECT}: {MINLP} special structure detector for Pyomo},
journal={Optimization Letters},
year={2019},
month={Feb},
issn="1862-4480",
doi="10.1007/s11590-019-01396-y",
url="https://doi.org/10.1007/s11590-019-01396-y"
}Documentation is available at https://cog-imperial.github.io/suspect/
SUSPECT requires Python 3.5 or later. We recommend installing SUSPECT in a virtual environment
To create the virtual environment run:
$ python3 -m venv myenv $ source myenv/bin/activate
Then you are ready to clone and install SUSPECT:
$ git clone https://github.com/cog-imperial/suspect.git $ cd suspect $ pip install -r requirements.txt $ pip install .
The package contains an utility to display structure information about a single problem.
You can run the utility as:
model_summary.py -p /path/to/problem.osil
or, if you want to check variables bounds include the solution:
model_summary.py -p /path/to/problem.osil -s /path/to/problem.sol
The repository also includes a Dockerfile to simplify running the utility in batch mode in a cloud environment. Refer to the batch folder for more information.
from suspect import detect_special_structure, create_connected_model
import pyomo.environ as aml
model = aml.ConcreteModel()
model.x = aml.Var()
model.y = aml.Var()
model.obj = aml.Objective(expr=(model.y - model.x)**3)
model.c1 = aml.Constraint(expr=model.y - model.x >= 0)
connected, _ = create_connected_model(model)
info = detect_special_structure(connected)
# try info.variables, info.objectives, and info.constraints
print(info.objectives['obj'])Copyright 2020 Francesco Ceccon
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
This work was funded by an Engineering & Physical Sciences Research Council Research Fellowship to RM [Grant Number EP/P016871/1].