MadNLP.jl

A solver for nonlinear programming
Author sshin23
Popularity
31 Stars
Updated Last
1 Year Ago
Started In
September 2020

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MadNLP is a nonlinear programming (NLP) solver, purely implemented in Julia. MadNLP implements a filter line-search algorithm, as that used in Ipopt. MadNLP seeks to streamline the development of modeling and algorithmic paradigms in order to exploit structures and to make efficient use of high-performance computers.

Installation

pkg> add MadNLP

Build

The build process requires C and Fortran compilers. If they are not installed, do

shell> sudo apt install gcc gfortran # Linux
shell> brew cask install gcc gfortran # MacOS

MadNLP is interfaced with non-Julia sparse/dense linear solvers:

All the dependencies except for HSL solvers, Pardiso, and CUDA are automatically installed. To build MadNLP with HSL linear solvers (Ma27, Ma57, Ma77, Ma86, Ma97), the source codes need to be obtained by the user from http://www.hsl.rl.ac.uk/ipopt/ under Coin-HSL Full (Stable). Then, the tarball coinhsl-2015.06.23.tar.gz should be placed at deps/download. To use Pardiso, the user needs to obtain the Paridso shared libraries from https://www.pardiso-project.org/, place the shared library file (e.g., libpardiso600-GNU720-X86-64.so) at deps/download, and place the license file in the home directory. The absolute path for deps/download can be obtained by:

julia> import MadNLP; joinpath(dirname(pathof(MadNLP)),"..","deps","download")

To use cuSOLVER, functional NVIDIA driver and corresponding CUDA toolkit need to be installed by the user. After obtaining the files, run

pkg> build MadNLP

Build can be customized by setting the following environment variables.

julia> ENV["MADNLP_CC"] = "/usr/local/bin/gcc-9"    # default is "gcc"
julia> ENV["MADNLP_FC"] = "/usr/local/bin/gfortran" # default is "gfortran"
julia> ENV["MADNLP_BLAS"] = "openblas"              # default is "mkl" if available "openblas" otherwise
julia> ENV["MADNLP_ENALBE_OPENMP"] = false          # default is "true"
julia> ENV["MADNLP_OPTIMIZATION_FLAG"] = "-O2"      # default is "-O3"

Alternatively, if the user has already installed HSL/pardiso library, one can simply specify the library path as follows:

julia> ENV["MADNLP_HSL_LIBRARY_PATH"] = "/opt/lib/libcoinhsl.so"
julia> ENV["MADNLP_PARDISO_LIBRARY_PATH"] = "/opt/lib/libpardiso.so" 

In this case, the source code is not compiled and the provided shared library is directly used.

Usage

MadNLP is interfaced with modeling packages:

JuMP interface

using MadNLP, JuMP

model = Model(()->MadNLP.Optimizer(linear_solver=MadNLP.Ma57,print_level=MadNLP.INFO,max_iter=100))
@variable(model, x, start = 0.0)
@variable(model, y, start = 0.0)
@NLobjective(model, Min, (1 - x)^2 + 100 * (y - x^2)^2)

optimize!(model)

Plasmo interface

using MadNLP, Plasmo

graph = OptiGraph()
@optinode(graph,n1)
@optinode(graph,n2)
@variable(n1,0 <= x <= 2)
@variable(n1,0 <= y <= 3)
@constraint(n1,x+y <= 4)
@objective(n1,Min,x)
@variable(n2,x)
@NLnodeconstraint(n2,exp(x) >= 2)
@linkconstraint(graph,n1[:x] == n2[:x])

MadNLP.optimize!(graph;linear_solver=MadNLP.Ma97,print_level=MadNLP.DEBUG,max_iter=100)

NLPModels interface

using MadNLP, CUTEst
model = CUTEstModel("PRIMALC1")
madnlp(model,linear_solver=MadNLP.PardisoMKL,print_level=MadNLP.WARN,max_wall_time=3600)

Using special linear solvers

In order to use GPU solvers, CUDA should be imported to the Main module.

using MadNLP, CUDA
model = Model(()->MadNLP.Optimizer(linear_solver=MadNLP.LapackGPU))
# ...

In order to use multi-threaded solvers (Schur and Schwawrz), julia session should be started with -t flag.

julia -t 16 # to use 16 threads

Solver options

To see the list of MadNLP solver options, check the OPTIONS.md file.

Bug reports and support

Please report issues and feature requests via the Github issue tracker.