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
start by `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 >= `v11.0`

.
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 >= v11.0.**

KNITRO.jl now supports MathOptInterface and JuMP 0.19.

```
using JuMP, KNITRO
model = with_optimizer(KNITRO.Optimizer, outlev=3)
```

**Note: MathProgBase works only with the old Knitro's KTR API.**

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
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 `knitro.h`

).

Extensive examples using the C wrapper can be found in `examples/`

.

The package AmplNLWriter.jl
allows to to call `knitroampl`

through Julia to solve JuMP's optimization
models.

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.

09/29/2014

6 days ago

339 commits