Repository containing experiments and tutorials related to various optimization problems and packages.
The easiest way is to use a conda environment. For instructions on getting started with the conda package manager, refer to the conda documentation.
Setup a conda environment by using the environment.yml file in this repository.
$ conda env create -f environment.yml
The environment created will be named cvxpy by default. The the command below to see the list of conda enviroments.
$ conda info -e
You may also need to setup/configure the individual solvers. See instructions below.
Description
CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.
Important! CVXPY is NOT a solver, it is a modelling language. CVXPY relies on the open source solvers ECOS, OSQP, and SCS. It also supports other solvers such as CVXOPT and Gurobi which must be installed separately.
Note If you need to solve a large mixed-integer problem quickly, or if you have a nonlinear mixed-integer model, then you will need to use an external commercial solver such as Gurobi.
Installation
CVXPY should come with external CVXOPT and Gurobi solvers installed when using the conda environment.
- Run
cvxpy_example.pyto test default cvxpy installation. - Run
cvxpy_solvers.pyto test cvxpy configuration with CVXOPT and Gurobi external solvers.
Description
PuLP is an LP modeler written in Python. PuLP can generate MPS or LP files and call GLPK, COIN-OR CLP/CBC, CPLEX, GUROBI, MOSEK, XPRESS, CHOCO, MIPCL, SCIP to solve linear problems.
Installation
Should be installed by default in the conda environment.
Run pulp_shortest_path.py example to test if PuLP has been configured correctly.
Description
Gurobi is a commercial optimization solver that can handle the following problem types - Linear programming (LP), Mixed-integer linear programming (MILP), Quadratic programming (QP) (Convex and Non-Convex), Mixed-integer quadratic programming (MIQP), (Convex and Non-Convex), Quadratically-constrained programming (QCP) (Convex and Non-Convex), Mixed-integer quadratically-constrained programming (MIQCP) (Convex and Non-Convex).
Installation
Download and install the Gurobi software. If you are using the conda environment as stated above, Gurobi will be automatically installed using conda. Else, go to the Gurobi website, download and install it.
Note gurobipy is the python API for the Gurobi software, which is automatically installed when using conda. Although the Gurobi solver can be used with CVXPY or PuLP as modelling languages, gurobipy provides a better interface to all of Gurobi's functionality.
Important! You need to have a Gurobi license to solve optimization problem with any reasonable number of variables. Luckily, Gurobi provides a free academic license. You need to sign up for a free account, and register for an academic license.
Important! Put the license key in the home directory, so that Gurobi can find it easily.
Run the gurobi_python_api.py file to test if Gurobi has been configured properly.
Description
GEKKO is a Python package for machine learning and optimization of mixed-integer and differential algebraic equations. It is coupled with large-scale solvers for linear, quadratic, nonlinear, and mixed integer programming (LP, QP, NLP, MILP, MINLP).
Capable of solving mixed integer non-linear problems. Solvers include APOPT, BPOPT, IPOPT. Refer to the APMonitor documentation for details.
Installation
Should be installed by default if using the conda environment. Else refer to the GEKKO documentation for installation process.
Run the gekko_example.py file to check if GEKKO has been configured properly.
GEKKO is capable of handling MINLPs. See the example in MINLP/gekko_nonlinear_constraint.py.
Gurobi is capable of piecewise linear approximation of non-linear functions using SOS2 variables. See the example in MINLP/gurobi_nonlinear_constraint.py.