NLPModelsAlgencan.jl is a NLPModels interface to the Algencan nonlinear solver.

Algencan is a large scale high performance augmented Lagrangian solver written by Ernesto Birgin and Mario Martínez. It has many special features like being able to use the HSL library to speed up the sparse matrix linear algebra and some smart acceleration strategies.

NLPModelsAlgencan.jl is based on Algencan that is a software from the Tango Project. If you use this software in your research, we kindly ask you to cite it according to its guidelines. In particular, if you use Algencan we suggest citing:

1. R. Andreani, E. G. Birgin, J. M. Martínez and M. L. Schuverdt, "On Augmented Lagrangian methods with general lower-level constraints", SIAM Journal on Optimization 18, pp. 1286-1309, 2007.
2. R. Andreani, E. G. Birgin, J. M. Martínez and M. L. Schuverdt, "Augmented Lagrangian methods under the Constant Positive Linear Dependence constraint qualification", Mathematical Programming 111, pp. 5-32, 2008.

If your work uses Gencan, the suggested references are:

1. E. G. Birgin and J. M. Martínez, "Large-scale active-set box-constrained optimization method with spectral projected gradients", Computational Optimization and Applications 23, pp. 101-125, 2002.
2. M. Andretta, E. G. Birgin and J. M. Martínez, "Practical active-set Euclidian trust-region method with spectral projected gradients for bound-constrained minimization", Optimization 54, pp. 305-325, 2005.
3. E. G. Birgin and J. M. Martínez, "A box-constrained optimization algorithm with negative curvature directions and spectral projected gradients", Computing [Suppl] 15, pp. 49-60, 2001.

At this point this is beta software. It will only work with Julia LTS or later.

The package downloads and installs Algencan upon installation. Therefore, you need to have a minimal development environment installed. You need at least gcc, gfortran, make and a development version of a BLAS/Lapack libraries (for example libopenblas-dev). The BLAS/Lapack implementation is important to get good performance. Use a high quality one like Openblas or Intel MKL.

There are three main modes of installation, depending on how you want to compile Algencan.

Obs: We only give support for MA57 at this point.

The preferred way to use Algencan is to link it against an HSL function to solve sparse linear systems. To do this you need to grab your copy of MA57 from HSL. It has a free academic license. You should receive a file named hsl_ma57-5.2.0.tar.gz.

All you need to do is to create an environment variable named MA57_SOURCE pointing to this file before installing NLPModelsAlgencan.jl. For example, if the file is located at the /tmp folder, in bash you would do:

export MA57_SOURCE=/tmp/hsl_ma57-5.2.0.tar.gz

After that just install NLPModelsAlgencan.jl from Julia's REPL and import it to force pre-compilation.

(@v1.x) pkg> add NLPModelsAlgencan
julia> using NLPModelsAlgencan

Just type

(@v1.x) pkg> add NLPModelsAlgencan
julia> using NLPModelsAlgencan

in package mode in Julia's REPL.

This will download Algencan's code, compile it and make it available to the NLPModelsAlgencan.jl package. However, there is a major caveat here. The Algencan solver will be compiled without any HSL support. This will have a major negative impact on its behavior and performance. You should use HSL whenever you have access to it.

If you have your own copy of libalgencan.so (note that you need a shared library), you can use it. Just create an environment variable like below pointing to the directory where the library resides before installing NLPModelsAlgencan.jl.

export ALGENCAN_LIB_DIR=/path/where/algencan/libray/is

You can then proceed to install NLPModelsAlgencan.jl from the REPL

(@v1.x) pkg> add NLPModelsAlgencan
julia> using NLPModelsAlgencan

This wiki page documents the steps I used to compile a version of libalgencan.so with HSL support.