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IPOPT#

IPOPT (Interior Point OPTimizer, pronounced "Eye-Pea-Opt") is an open-source non-linear optimizer using the interior point method.

IPOPT is commonly used in solving power flow, e.g., AC Optimal Power Flow, and controls problems. Please refer to their project website for the source code. The documentation can be found here.

Note

IPOPT with HSL linear solvers is available as a module on Kestrel. Please see IDAES Solvers for additional details. We recommend using the system module for ease-of-use and only build if the module does not meet your needs.

Installation from source#

Info

We advise building all applications on a compute node using an interactive session. Please see Running Interactive Jobs for additional details.

Optional Pre-requisites#

We will build IPOPT using all prerequisites mentioned below. Users may pick and choose depending on their needs.

Metis#

It is highly recommended to install Metis - Serial Graph Partitioning and Fill-reducing Matrix Ordering software to improve the performance of linear solvers such as MUMPS and HSL.

Warning

Using HSL linear solvers requires installing Metis. Metis is optional for MUMPS.

We will install Metis using Anaconda. However, it can also be installed from source. To install using Anaconda, we will create a clean environment with only Metis. For this example, the conda environment is being constructed within a directory in the hpcapps project on Kestrel. Users can create a conda environment in any place of their choice.

module load conda
conda create -p /projects/hpcapps/kpanda/conda-envs/metis python
conda activate /projects/hpcapps/kpanda/conda-envs/metis
conda install conda-forge::metis

Coinbrew#

Coinbrew is a package manager to install COIN-OR tools. It makes installing IPOPT and its dependencies easier. However, it is not necessary to the installation if one clones the repositories individually. A user can download coinbrew by running the following command

wget https://raw.githubusercontent.com/coin-or/coinbrew/master/coinbrew

Intel oneAPI MKL#

Intel oneAPI MKL provides BLAS and LAPACK libraries for efficient linear algebra. Additionally, it also provides access to oneMKL PARDISO linear solver that is compatible with IPOPT.

Note

oneMKL PARDISO is not available on Kestrel GPU nodes since they consist of AMD processors.

HSL#

HSL (Harwell Subroutine Library) is a set of linear solvers that can greatly accelerate the speed of optimization over other linear solvers, e.g., MUMPS. HSL can be installed separately as well using ThirdParty-HSL. Please see here for installation on Kestrel.

Installation#

In this demonstration, we will install Ipopt within /projects/msoc/kpanda/apps/Ipopt/install. However, one is free to set their install directory as they wish. Starting with the base working directory /projects/msoc/kpanda/apps/ we will do the following

cd /projects/msoc/kpanda/apps/ # go into the base working directory
wget https://raw.githubusercontent.com/coin-or/coinbrew/master/coinbrew # install coinbrew
coinbrew fetch Ipopt # Fetch Ipopt and its dependencies

This will download 2 additional directories Ipopt and ThirdParty. ThirdParty, furthermore, contains 3 subdirectories ASL, HSL, and Mumps. The source code of all but HSL will be downloaded.

Next, we will create our install directories and subdirectories

mkdir -p /projects/msoc/kpanda/apps/Ipopt/install # create the install directory
cd /projects/msoc/kpanda/apps/Ipopt/install # enter the directory
mkdir bin lib include # create some subdirectories

We then add symbolic links to Metis in the install directory.

Note

If libmetis.so is in your LD_LIBRARY_PATH you do not need to do this step.

cd /projects/msoc/kpanda/apps/Ipopt/install/lib
ln -s /projects/hpcapps/kpanda/conda-envs/metis/lib/libmetis.so libmetis.so
cd ../include
ln -s /projects/hpcapps/kpanda/conda-envs/metis/include/metis.h metis.h
cd /projects/msoc/kpanda/apps/ # go back base directory

This has two advantages. First, we don't need to add /projects/hpcapps/kpanda/conda-envs/metis/lib/ to the LD_LIBRARY_PATH. The second advantage is that anaconda puts all the environments libraries and include files in the same directories with libmetis.so and metis.h. Many of these libraries overlap with those used by HSL, Mumps and IPOPT but are not necessarily the same versions. Loading a different version of a library than those compiled against can cause unexpected behavior.

Next, we will load additional modules. If users require oneMKL PARDISO or would like to leverage intel performance optimization, run the following commands

module load intel-oneapi-mkl

Alternatively, users can load the open source Netlib LAPACK using the command

module load netlib-lapack # Please ensure you do not have intel-oneapi-mkl loaded

We will now copy the HSL source code tarball into /projects/msoc/kpanda/apps/ThirdParty/HSL/, unpack it, and rename or (create a symbolic link to the unpacked directory) as coinhsl.

We are now ready to install IPOPT and its dependencies. We will use the default compilers available in the Kestrel programming environment. Going back to the base directory, we will run the following commands

cd /projects/msoc/kpanda/apps/ # go back base directory
./coinbrew build Ipopt --disable-java \
--prefix=/kfs2/projects/msoc/kpanda/apps/Ipopt/install \
--with-metis \
--with-metis-cflags=-I/projects/hpcapps/kpanda/conda-envs/metis/include \
--with-metis-lflags="-L/projects/hpcapps/kpanda/conda-envs/metis/lib -lmetis" \
--parallel-jobs 4 \
--verbosity 4 \
--reconfigure

Usage#

The installed Ipopt is now ready to be used. We need to update our PATH AND LD_LIBRARY_PATH environment variables. In our demonstrations this will be

export PATH=/projects/msoc/kpanda/apps/Ipopt/install/bin:${PATH}
export LD_LIBRARY_PATH=/projects/msoc/kpanda/apps/Ipopt/install/lib:${LD_LIBRARY_PATH}

Note

Do not forget to load intel-oneapi-mkl or netlib-lapack before running IPOPT else your runs will fail.

Using Custom IPOPT with JuMP#

To use our custom installation of IPOPT with Ipopt.jl, we do the following:

  1. Open the Julia REPL and activate an environment that has IPOPT installed
  2. Tell Julia and Ipopt.jl the location of our IPOPT library and executable 
    ENV["JULIA_IPOPT_LIBRARY_PATH"] = ENV["/projects/msoc/kpanda/apps/Ipopt/install/lib"]
ENV["JULIA_IPOPT_EXECUTABLE_PATH"] = ENV["/projects/msoc/kpanda/apps/Ipopt/install/bin"]
  3. Rebuild Ipopt.jl with the above environment variables set to pick up the new library and executable 
    using Pkg; Pkg.build("Ipopt");
  4. Print the path Ipopt.jl has stored for libipopt.so. This should be the location of your compiled version. 
    using Ipopt; println(Ipopt.libipopt_path)

Info

The IPOPT build that comes with Ipopt.jl seems to expect the HSL library to have the name libhsl.so. The repo ThirdParty-HSL builds the library libcoinhsl.so. The simplest fix is to do the following:

cd /projects/msoc/kpanda/apps/Ipopt/install/lib # install directory
# Create a symbolic link called libhsl.so
ln -s libcoinhsl.so libhsl.so

The following Julia code is useful for testing the HSL linear solvers are working

using JuMP, IPOPT

m = JuMP.Model(()->IPOPT.Optimizer(linear_solver="ma97"))
@variable(m, x)
@objective(m, Min, x^2)
JuMP.optimize!(m)