Getting Started

Installation

PyOptInterface is available on PyPI. You can install it via pip:

pip install pyoptinterface

After installation, you can import the package in Python console:

import pyoptinterface as poi

PyOptInterface has no dependencies other than Python itself. However, to use it with a specific optimizer, you need to install the corresponding optimizer manually. The details can be found on the configurations of optimizers.

In order to provide out-of-the-box support for open source optimizers (currently we support HiGHS), PyOptInterface can also be installed with pre-built optimizers. You can install them via pip:

pip install pyoptinterface[highs]

It will install a full-featured binary version of HiGHS optimizer via highsbox, which can be used with PyOptInterface.

In order to use nonlinear programming solvers (currently we only support IPOPT), you should install extra dependencies like:

pip install pyoptinterface[nlp]

It will install the llvmlite and tccbox package as the JIT compilers required by nonlinear programming.

We will introduce how to set up the optimizers to use with PyOptInterface in this page.

Setup of optimizers

PyOptInterface uses the Dynamic Loading technique to load the dynamic library of optimizers at runtime, so the optimizer it uses can be changed manually without recompiling the code.

The set up of optimizers includes two approaches:

1. Automatic detection of the installation directory of the optimizers.

2. Manually specifying the path of the dynamic library of optimizer.

We will introduce the automatic detection and manual configuration in details

Automatic detection of the installation directory of the optimizers

The automatic detection includes three steps:

1. Environment variable set by the installer of optimizer

2. The official Python binding of the optimizer (if available)

3. Search directly in the system loadable path (e.g. /usr/lib, /usr/local/lib on Linux or PATH on Windows)

For the 3rd step, we want to explain more for novices.

For example, in order to make the dynamic library of HiGHS highs.dll/libhighs.so/libhighs.dylib loadable, we need to put it in a loadable path recognized by the operating system. The typical loadable path on Linux is /usr/lib, /usr/local/lib, and the typical loadable path on Windows is PATH.

On Linux, we use the LD_LIBRARY_PATH environment variable to specify the dynamic library path. On macOS, we use the DYLD_LIBRARY_PATH environment variable to specify the dynamic library path.

If the dynamic library is in path C:\highs\lib//opt/highs/lib:

• Windows: set PATH=%PATH%;C:\highs\lib

• Linux: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/highs/lib

• macOS: export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/highs/lib

The typical paths where the dynamic library of optimizers are located are as follows:

Optimizer	Windows	Linux	macOS(ARM)	macOS(Intel)
Gurobi	C:\gurobi1101\win64\bin	/opt/gurobi1100/linux64/lib	/opt/gurobi1100/macos_universal2/lib	/opt/gurobi1100/macos_universal2/lib
COPT	C:\Program Files\copt71\bin	/opt/copt71/lib	/opt/copt71/lib	/opt/copt71/lib
Mosek	C:\Program Files\Mosek\10.2\tools\platform\win64x86\bin	/opt/mosek/10.2/tools/platform/linux64x86/bin	/opt/mosek/10.2/tools/platform/osxaarch64/bin	/opt/mosek/10.2/tools/platform/osx64x86/bin
HiGHS	D:\highs\bin	/opt/highs/lib	/opt/highs/lib	/opt/highs/lib

Gurobi

The currently supported version is 11.0.x. Other versions may work but are not tested.

For Gurobi, the automatic detection looks for the following things in order:

1. The environment variable GUROBI_HOME set by the installer of Gurobi

2. The installation of gurobipy

3. gurobi110.dll/libgurobi110.so/libgurobi110.dylib in the system loadable path

COPT

The currently supported version is 7.1.4 and newer. Other versions may work but are not tested.

For COPT, the automatic detection looks for the following things in order:

1. The environment variable COPT_HOME set by the installer of COPT

2. copt.dll/libcopt.so/libcopt.dylib in the system loadable path

Mosek

The currently supported version is 10.2.x. Other versions may work but are not tested.

For Mosek, the automatic detection looks for the following things in order:

1. The environment variable MOSEK_10_2_BINDIR set by the installer of Mosek

2. The installation of mosek PyPI package

3. mosek64_10_2.dll/libmosek64.so/libmosek64.dylib in the system loadable path

HiGHS

The currently supported version is 1.8.x. Other versions may work but are not tested.

For HiGHS, the automatic detection looks for the following things in order:

1. The environment variable HiGHS_HOME set by the user

2. The installation of highsbox PyPI package (recommended)

3. highs.dll/libhighs.so/libhighs.dylib in the system

For HiGHS, we recommend installing the highsbox PyPI package, which provides a full-featured binary version of HiGHS optimizer for you.

Ipopt

The currently supported version is 3.14.x. Other versions may work but are not tested.

For Ipopt, the automatic detection looks for the following things in order:

1. ipopt.dll/libipopt.so/libipopt.dylib in the system, we also look for ipopt-3.dll/libipopt.dll/libipopt-3.dll on Windows.

We recommend using the official binary from GitHub if you work on Windows, since compiling Ipopt on Windows from source is not an easy task.

Manually specifying the path of the dynamic library of optimizer

If the automatic detection fails or you want to use the optimizer in a customized location, you can manually specify the path of the dynamic library of the optimizer.

We take HiGHS as an example. Whether the optimizer has been successfully loaded can be checked via the following code:

from pyoptinterface import highs

print(highs.is_library_loaded())

If the optimizer has not been successfully loaded, you can manually specify the path of the dynamic library of the optimizer via the following code:

ret = highs.load_library("path/to/libhighs.so")

print(f"Loading from custom path manually: {ret}")

The load_library function returns True if the library is successfully loaded, otherwise it returns False.

If you want to revert to use the automatic detection, you can call the autoload_library function:

ret = highs.autoload_library()

print(f"Loading from automatically detected location: {ret}")

For other optimizers, just replace highs with the corresponding optimizer name like gurobi, copt, mosek.

The typical paths where the dynamic library of optimizers are located are as follows:

Optimizer	Windows	Linux	macOS(ARM)	macOS(Intel)
Gurobi	C:\gurobi1101\win64\bin\gurobi110.dll	/opt/gurobi1100/linux64/lib/libgurobi110.so	/opt/gurobi1100/macos_universal2/lib/libgurobi110.dylib	/opt/gurobi1100/macos_universal2/lib/libgurobi110.dylib
COPT	C:\Program Files\copt71\bin\copt.dll	/opt/copt71/lib/libcopt.so	/opt/copt71/lib/libcopt.dylib	/opt/copt71/lib/libcopt.dylib
Mosek	C:\Program Files\Mosek\10.2\tools\platform\win64x86\bin\mosek64_10_1.dll	/opt/mosek/10.2/tools/platform/linux64x86/bin/libmosek64.so	/opt/mosek/10.2/tools/platform/osxaarch64/bin/libmosek64.dylib	/opt/mosek/10.2/tools/platform/osx64x86/bin/libmosek64.dylib
HiGHS	D:\highs\bin\highs.dll	/opt/highs/lib/libhighs.so	/opt/highs/lib/libhighs.dylib	/opt/highs/lib/libhighs.dylib

Let’s build a simple model and solve it

After setting up the optimizers, we can build a simple model and solve it. As the first step, we will solve the following simple Quadratic Programming (QP) problem:

\[\begin{split}\min \quad & x^{2}_{1} + 2x^{2}_{2} \\ \textrm{s.t.} \quad & x_{1} + x_{2} = 1 \\ \quad & x_{1}, x_{2} \geq 0\end{split}\]

First, we need to create a model object:

import pyoptinterface as poi
from pyoptinterface import highs
# from pyoptinterface import copt, gurobi, mosek (if you want to use other optimizers)

model = highs.Model()

Then, we need to add variables to the model:

x1 = model.add_variable(lb=0, name="x1")
x2 = model.add_variable(lb=0, name="x2")

Running HiGHS 1.8.0 (git hash: fcfb534): Copyright (c) 2024 HiGHS under MIT licence terms

The lb argument specifies the lower bound of the variable. It is optional and defaults to \(-\infty\).

The name argument is optional and can be used to specify the name of the variable.

Then, we need to add constraints to the model:

con = model.add_linear_constraint(x1+x2, poi.ConstraintSense.Equal, 1, name="con")

model.add_linear_constraint adds a linear constraint to the model.

• The first argument x1+x2 is the left-hand side of the constraint.

• The second argument is the sense of the constraint. It can be poi.ConstraintSense.Equal, poi.ConstraintSense.LessEqual or poi.ConstraintSense.GreaterEqual.

• The third argument is the right-hand side of the constraint. It must be a constant.

• The fourth argument is optional and can be used to specify the name of the constraint.

Finally, we need to set the objective function and solve the model:

obj = x1*x1 + 2*x2*x2
model.set_objective(obj, poi.ObjectiveSense.Minimize)

The model can be solved via:

model.optimize()

Coefficient ranges:
  Matrix [1e+00, 1e+00]
  Cost   [0e+00, 0e+00]
  Bound  [0e+00, 0e+00]
  RHS    [1e+00, 1e+00]
  Iteration        Objective     NullspaceDim
          0        2.0000001                0      0.00s
          2       0.66666669                1      0.00s
Model   status      : Optimal
QP ASM    iterations: 2
Objective value     :  6.6666666667e-01
HiGHS run time      :          0.00

The HiGHS optimizer will be invoked to solve the model and writes the log to the console.

We can query the status of the model via:

model.get_model_attribute(poi.ModelAttribute.TerminationStatus)

<TerminationStatusCode.OPTIMAL: 2>

The solution of the model can be queried via:

print("x1 = ", model.get_value(x1))
print("x2 = ", model.get_value(x2))
print("obj = ", model.get_value(obj))

x1 =  0.6666666611111114
x2 =  0.3333333388888886
obj =  0.6666666666666667