The KNITRO.jl package provides an interface for using the Artelys Knitro solver from Julia. You cannot use KNITRO.jl without having purchased and installed a copy of Knitro from Artelys. This package is available free of charge and in no way replaces or alters any functionality of Artelys Knitro solver.
Documentation is available at https://juliaopt.github.io/KNITRO.jl/latest.
Note that the current package provides a wrapper both for the new Knitro's API
(whose functions start by
KN_) and the deprecated Knitro's API (whose functions
KTR_). We recommend using the latest version of Knitro available and
the new API to get access to all of the new functionalities from the solver.
Using the new
KN_ API requires Knitro >=
Refer to Knitro documentation
for a full specification of the Knitro's API.
The Artelys Knitro wrapper for Julia is supported by the JuliaOpt community (which originates the development of this package) and Artelys. Feel free to contact Artelys support if you encounter any problem with this interface or the solver.
Note: MathOptInterface works only with the new Knitro's
KN API which requires Knitro >=
using JuMP, KNITRO model = with_optimizer(KNITRO.Optimizer, outlev=3)
Note: MathProgBase works only with the old Knitro's
The solver object is called
KnitroSolver. All options listed in the
Artelys Knitro documentation
may be passed directly. For example, you can run all algorithms by saying
KnitroSolver(KTR_PARAM_ALG=KTR_ALG_MULTI), and here is a formulation
modelled using JuMP.jl that specifies
some non-default option settings:
using KNITRO, JuMP ## Solve test problem 1 (Synthesis of processing system) in # M. Duran & I.E. Grossmann, "An outer approximation algorithm for # a class of mixed integer nonlinear programs", Mathematical # Programming 36, pp. 307-339, 1986. The problem also appears as # problem synthes1 in the MacMINLP test set. m = Model(solver=KnitroSolver(mip_method = KTR_MIP_METHOD_BB, algorithm = KTR_ALG_ACT_CG, outmode = KTR_OUTMODE_SCREEN, KTR_PARAM_OUTLEV = KTR_OUTLEV_ALL, KTR_PARAM_MIP_OUTINTERVAL = 1, KTR_PARAM_MIP_MAXNODES = 10000, KTR_PARAM_HESSIAN_NO_F = KTR_HESSIAN_NO_F_ALLOW)) x_U = [2,2,1] @variable(m, x_U[i] >= x[i=1:3] >= 0) @variable(m, y[4:6], Bin) @NLobjective(m, Min, 10 + 10*x - 7*x + 5*y + 6*y + 8*y - 18*log(x+1) - 19.2*log(x-x+1)) @NLconstraints(m, begin 0.8*log(x + 1) + 0.96*log(x - x + 1) - 0.8*x >= 0 log(x + 1) + 1.2*log(x - x + 1) - x - 2*y >= -2 x - x <= 0 x - 2*y <= 0 x - x - 2*y <= 0 y + y <= 1 end) solve(m)
NB: The MathProgBase interface is bound to be deprecated. Please use MathOptInterface instead.
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 directly Knitro's C API (whose templates
are specified in the file
Extensive examples using the C wrapper can be found in
The package AmplNLWriter.jl
allows to to call
knitroampl through Julia to solve JuMP's optimization
The usage is as follow:
using JuMP, KNITRO, AmplNLWriter model = with_optimizer(AmplNLWriter.Optimizer, KNITRO.amplexe, ["outlev=3"])
Note that supports is still experimental for JuMP 0.19.
about 1 month ago