MINLPLib.jl is an extensive collection of Mixed-Integer and Continuous Nonlinear Programming Instances in the native `JuMP.jl`

format. Numerous instances in this library are based on the standard MINLPLib.org instances in addition to hundreds of global optimization instances which appear in the litearature. This library can be useful for:

Benchmarking your Julia-based MINLP solvers by performing rigorous numerical experiments

Viewing meta information of each instance to assist your analyses and experiments

For more details of the libraries included in this package, please see documentation.

To install, at your `Julia`

command prompt,

```
Pkg.clone("https://github.com/lanl-ansi/MINLPLib.jl.git")
```

Fetch an instance by its `libname/name`

just like the following,

```
using MINLPLib
m = fetch_model("minlp2/blend029")
```

or

```
using MINLPLib
m = fetch_model("minlp2", "blend029")
```

For special built-in model that requires additional inputs,

```
using MINLPLib
m = fetch_model("special", "multiKND", options=Dict(:K=>3, :N=>3, :D=>1))
```

The above scrip initializes an optimization JuMP model `multiKND`

. The formulation is controlled with parameter `:K`

, `:N`

, and `:D`

, which yields the following problem:

```
Max x[1] * x[2] * x[3] + x[3] * x[4] * x[5] + x[5] * x[6] * x[7]
Subject to
x[1] + x[2] + x[3] <= 3
x[3] + x[4] + x[5] <= 3
x[5] + x[6] + x[7] <= 3
0.1 <= x[i] <= 10 for all i in {1,2,..,6,7}
```

To know a instance better, you can do the following to get a dictionary of meta info. Note that as we continue to develop and test. More verified attributes will be added to the meta.

```
julia> meta = MINLPLib.fetch_meta("minlp2", "blend029")
Dict{String,Any} with 18 entries:
"NINTVARS" => 0
"NLINCONS" => 202
"OBJBOUND" => nothing
"NAME" => "blend029"
"OBJVAL" => nothing
"LIBRARY" => "minlp2"
"NCONS" => 214
"NVARS" => 103
"OBJTYPE" => "linear"
"NLOPERATOR" => Any["mul"]
"LOAD" => 1.7414
"OBJSENSE" => "Max"
"NSDPCONS" => 0
"NNLCONS" => 12
"NBINVARS" => 36
"NQUADCONS" => 0
"NSOSCONS" => 0
```

Currently, MINLPLib.jl contains over 6000 instances. Experimenting with all
of them can be computationally heavy and risky. For your research projects, the scope may be
limited to a specific type of problems. Hence, it is necessary to construct you very own
library for research experiments. Below, we write a small `.jl`

script that will generate a
user instance library (without actually copying the problem) called `USERLib`

that collect all instances with
`exp`

function. The directory will be created with name `instance/USERLib`

. Please see our
PODLib designed for
testing Alpine.jl solver. This customized library contains instances reference
as well as full instances. Note that each instance can be drastically different with different formulations and
implementations (peak into the library called `special`

and you will see some examples).
It is up to you to select and construct your own instance library for more exciting and convenient experiments.

```
using MINLPLib
# Collect target problem from these libraries
for lib in ["bcp", "global", "ibm", "inf", "minlp", "minlp2"]
NAMES = MINLPLib.fetch_names(lib) # Fetch a list of instance names
for i in NAMES
Meta = MINLPLib.fetch_meta(lib, i) # Fetch instance meta
isempty(Meta) && continue
!haskey(Meta, "NLOPERATOR") && continue
if "exp" in M["NLOPERATOR"][1]
MINLPLib.add_to_lib("USERLib", lib, i) # Collect instance
else
continue
end
end
end
println(MINLPLib.fetch_names("USERLib"))
```

08/25/2017

7 days ago

200 commits