Julia interface to the Artelys Knitro solver



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.

Artelys Knitro functionality is extensive, so coverage is incomplete, but most functionality for solving linear, nonlinear, and mixed-integer programs is provided. Documentation is available at https://juliaopt.github.io/KNITRO.jl/latest.

The Artelys Knitro wrapper for Julia is community driven and not officially supported by Artelys. If you are an Artelys customer interested in official support for Julia, let them know!

MathProgBase Interface

KNITRO.jl implements the solver-independent MathProgBase interface, and so can be used within modeling software like JuMP.

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[1] - 7*x[3] + 5*y[4] + 6*y[5] + 8*y[6] - 18*log(x[2]+1) - 19.2*log(x[1]-x[2]+1))
@NLconstraints(m, begin
    0.8*log(x[2] + 1) + 0.96*log(x[1] - x[2] + 1) - 0.8*x[3] >= 0
    log(x[2] + 1) + 1.2*log(x[1] - x[2] + 1) - x[3] - 2*y[6] >= -2
    x[2] - x[1] <= 0
    x[2] - 2*y[4] <= 0
    x[1] - x[2] - 2*y[5] <= 0
    y[4] + y[5] <= 1

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