List of Optimization Packages in Python: Update 2024

This article serves as a comprehensive reference for academics and experts in the following fields to get familiar with what is available for optimization in Python:

• Industrial engineering (IE)
• Supply chain management (SCM)
• Operations research (OR)
• Computer science (CS)
• Machine learning (ML)
• Simulation (SM)
• Data science (DS)

Guide

Package capability	Description
MINLP	Mixed integer nonlinear programming
MIQP	Mixed integer quadratic programming
MILP	Mixed integer linear programming
NLP	Nonlinear programming
IP	Integer programming
LP	Linear programming
CP	Constraint programming
GPP	General purpose programming
MOP	Multi-criteria/objective programming

MINLP+MIQP+MILP+NLP+IP+LP Packages

Note: Some packages might not accept a mixed group of discrete and continuous variables.

Package	Link
amplpy	Official
casadi	Official
feloopy	Official
gekko	Official
gamspy	Official
insideopt-seeker	Official
knitro	Official
lindo	Official
midaco	Official
naginterfaces	Official
octeract	Official
omlt	Official
optalg	Official
optmod	Official
pydrake	Official
pyepo	Official
pyomo	Official
pyscipopt	Official
xpress	Official

MIQP+MILP+IP+LP Packages

Note: Some packages might not accept a mixed group of discrete and continuous variables.

Package	Link
copt	Official
cplex	Official
docplex	Official
gurobipy	Official
hexaly	Official
highs	Official
mosek	Official
optlang	Official
qpsolvers	Official
rsome	Official
sasoptpy	Official

MILP+IP+LP Packages

Note: Some packages might not accept a mixed group of discrete and continuous variables.

Package	Link
cvxopt	Official
cvxpy	Official
cylp	Official
flowty	Official
linopy	Official
lpsolve55	Official
mindoptpy	Official
mip	Official
ortools	Official
picos	Official
pulp	Official
pymprog	Official
swiglpk	Official

NLP+LP Packages

Package	Link
acadopy	Official
acados	Official
cyipopt	Official
dymos	Official
gpkit	Official
iminuit	Official
nlopt	Official
nlpy	Official
openmdao	Official
openopt	Official
polyopt	Official
pyipopt	Official
pyopt	Official
scipy	Official
trustregion	Official
worhp	Official

CP Packages

Package	Link
choco	Ofiicial
cplex	Official
cpmpy	Official
feloopy	Official
gecode-python	Official
kalis	Official
minizinc	Official
optapy	Official
ortools	Official
python-constraint	Official
timefold	Official
z3-solver	Official

GPP Packages

Package	Link
arm-mango	Official
ax	Official
bayesian-optimization	Official
bayesianevolution	Official
bayeso	Official
bayesopt	Official
black-box	Official
bolib	Official
cma	Official
cmaes	Official
cobyqa	Github
cuopt	Official
deap	Official
dfoalgos	Official
dfogn	Official
dlib	Official
evolopy	Official
evoopt	Official
evostra	Official
feloopy	Official
freelunch	Official
gaft	Official
geneticalgorithm	Official
goptpy	Github
gradient-free-optimizers	Github
gyopt	Official
hebo	Official
heuristic_optimization	Official
hpbandster	Official
hyperopt	Official
inspyred	Official
mealpy	Official
mipego	Official
moptipy	Official
mystic	Official
nevergrad	Official
niapy	Official
oasis	Official
optuna	Official
optuner	Official
opytimizer	Official
pagmo	Official
pdfo	Official
platypus	Official
prodyn	Official
proxmin	Official
psopt	Official
psopy	Official
py-bobyqa	Official
pydogs	Official
pygmo	Official
pygpgo	Official
pymetaheuristic	Official
pymoo	Official
pyopus	Official
pypesto	Official
pyriad	Official
pysmac	Official
pysot	Official
pyswarms	Official
qiskit-optimization	Official
rapids-NeurIPS	Official
ray	Official
rbfopt	Official
scikit-opt	Official
scikit-optimize	Official
simanneal	Official
simple	Official
solidpy	Official
spearmint	Official
spotpy	Official
ssb-optimize	Official
swarm-cg	Github
swarmlib	Official
swarmpackagepy	Official
tgo	Official
turbo-NeurIPS	Official
turbo	Official
ultraopt	Official
yabox	Official
zoofs	Official
zoopt	Official

MOP Packages

Package	Link
feloopy	Official
parmoo	Official
pydecision	Official
pymoo	Official
pymultiobjective	Official

Notes

1- If you are having trouble while installing via !pip install <PACKAGE> (in-line code) or pip install <PACKAGE> (terminal code), you may use the following piece of code. Also, please be aware that some of the introduced packages require installing software or downloading and compiling a copy of their binary files to be imported into Python. Therefore, some of them are not easily pip installable.

import pip

#Function:
def install(package):
    if hasattr(pip, 'main'):
        pip.main(['install', package])
    else:
        pip._internal.main(['install', package])

#Example:
install('pyomo')

2- There are some benchmarkig tools and websites, which are introduced as follows:

Benchmark	Link
humpday	Official
pycutest	Official
Mittelmann	Official