KNITRO.jl is a wrapper for the Artelys Knitro solver.
It has two components:
- a thin wrapper around the C API
- an interface to MathOptInterface.
This wrapper is maintained by the JuMP community with help from Artelys.
Contact Artelys support if you encounter any problem with this interface or the solver.
KNITRO.jl is licensed under the MIT License.
The underlying solver is a closed-source commercial product for which you must purchase a license.
First, obtain a license and install a copy of KNITRO from Artelys.
KNITRO.jl using the Julia package manager:
import Pkg Pkg.add("KNITRO")
If you are having trouble installing KNITRO.jl, here are several things to try:
- Make sure that you have defined your global variables correctly, for example with
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$KNITRODIR/lib". You can check that
KNITRO.jlsees your library with
using KNITRO; KNITRO.has_knitro().
falsebut you are confident that your paths are correct, try running
Pkg.build("KNITRO")and restarting Julia. In at least one user's experience, installing and using KNITRO in a temporary Julia environment (activated with
] activate --temp) does not work and the need to manually build is likely the reason why.
Use with JuMP
To use KNITRO with JuMP, use
using JuMP, KNITRO model = Model(KNITRO.Optimizer) set_attribute(model, "outlev", 1) set_attribute(model, "algorithm", 4)
Use with AMPL
To use KNITRO with AmplNLWriter.jl, use
using JuMP import AmplNLWriter import KNITRO model = Model(() -> AmplNLWriter.Optimizer(KNITRO.amplexe, ["outlev=3"]))
Use with other packages
A variety of packages extend KNITRO.jl to support other optimization modeling systems. These include:
The Knitro optimizer supports the following constraints and attributes.
List of supported objective functions:
List of supported variable types:
List of supported constraint types:
List of supported model attributes:
A list of available options is provided in the KNITRO reference manual.
KNITRO.jl implements most of Knitro's functionalities. If you aim at using part of Knitro's API that are not implemented in the MathOptInterface/JuMP ecosystem, you can refer to the low-level API, which wraps Knitro's C API (whose templates are specified in the file
Extensive examples using the C wrapper can be found in
Due to limitations in the interaction between Julia and C, KNITRO.jl disables multi-threading if the problem is nonlinear. This will override any options such as
par_numthreads that you may have set. Read GitHub issue #93 for more details.