Ipopt (Interior Point OPTimizer, pronounced eye-pea-Opt) is a software package for large-scale nonlinear optimization. It is designed to find (local) solutions of mathematical optimization problems of the form
min f(x) x ∈ Rⁿ s.t. g_L ≤ g(x) ≤ g_U x_L ≤ x ≤ x_U
f(x): Rⁿ --> R is the objective function, and
g(x): Rⁿ --> Rᵐ
are the constraint functions. The vectors
g_U denote the lower and upper bounds on the constraints, and the vectors
x_U are the bounds on the variables
g(x) can be nonlinear and nonconvex, but should be twice continuously differentiable.
Note that equality constraints can be formulated in the above formulation by setting the corresponding components of
g_U to the same value.
Ipopt is written in C++ and is released as open source code under the Eclipse Public License (EPL). The code has been written by Andreas Wächter and Carl Laird. The COIN-OR project managers for Ipopt are Andreas Wächter und Stefan Vigerske. For a list of all contributors, see the AUTHORS file.
The Ipopt distribution can be used to generate a library that can be linked to one's own C++, C, Fortran, or Java code, as well as a solver executable for the AMPL modeling environment. The package includes an interface to the R programming environment. IPOPT can be used on Linux/UNIX, Mac OS X, and Windows platforms.
As open source software, the source code for Ipopt is provided without charge. You are free to use it, also for commercial purposes. You are also free to modify the source code (with the restriction that you need to make your changes public if you decide to distribute your version in any way, e.g. as an executable); for details see the EPL license. And we are certainly very keen on feedback from users, including contributions!
In order to compile Ipopt, certain third party code is required (such as some linear algebra routines). Those are available under different conditions/licenses.
For information on projects that use Ipopt, refer to the Success Stories page.
Please consult the detailed installation instructions in the Ipopt documentation. In the following, we only summarize some main points.
Ipopt requires at least one of the following solvers for systems of linear equations:
- MA27, MA57, HSL_MA77, HSL_MA86, or HSL_MA97 from the Harwell Subroutines Library (HSL). It is recommended to use project ThirdParty-HSL to build a HSL library for use by Ipopt.
- Parallel Sparse Direct Linear Solver (Pardiso). Note, that the Intel Math Kernel Library (MKL) also includes a version of Pardiso, but the one from Pardiso Project often offers better performance.
- MUltifrontal Massively Parallel sparse direct Solver (MUMPS). It is highly recommended to use project ThirdParty-Mumps to build a MUMPS library for use by Ipopt.
- Watson Sparse Matrix Package
A fast implementation of BLAS and LAPACK is required by Ipopt.
To build the AMPL interface of Ipopt, the AMPL Solver Library (ASL) is required. It is recommended to use project ThirdParty-ASL to build a ASL library for use by Ipopt.
After installation of dependencies, an Ipopt build and installation follows these 4 steps:
./configure --helpto see available options.
maketo build the Ipopt libraries. If ASL was made available, also Ipopt executables will be build.
make testto test the Ipopt build.
make installto install Ipopt (libraries, executables, and header files).
It is suggested to use the same installation prefix (
--prefix option of
when configuring the build of ThirdParty-ASL, ThirdParty-HSL, ThirdParty-MUMPS, and Ipopt.
An alternative to the above steps is to use the
coinbrew script from
coinbrew automates the download of the source code for ASL, MUMPS, and Ipopt
and the sequential build and installation of these three packages.
After obtaining the
coinbrew script, run
/path/to/coinbrew fetch Ipopt --no-prompt /path/to/coinbrew build Ipopt --prefix=/dir/to/install --test --no-prompt --verbosity=3 /path/to/coinbrew install Ipopt --no-prompt
More details on using coinbrew can be found at the instructions on Getting Started with the COIN-OR Optimization Suite.
Some precompiled binaries of Ipopt are also available:
- Ipopt releases page
- JuliaBinaryWrappers provides Ipopt binaries
- AMPL provides binaries for using Ipopt through AMPL
- Pardiso project provides binaries for using Ipopt with Pardiso through Matlab
- Ipopt Documentation with installation instructions, options reference, and more
- Issue tracking system: If you believe you found a bug in the code, please use the issue tracking system. Please include as much information as possible, and if possible some (ideally simple) example code so that we can reproduce the error.
- Mailing list: subscribe to get notifications about updates and to post questions and comments regarding Ipopt
- Mailing list archive
- Ipopt Wiki with hints and tricks
- short Ipopt tutorial
Please Cite Us
We provide this program in the hope that it may be useful to others, and we would very much like to hear about your experience with it. If you found it helpful and are using it within our software, we encourage you to add your feedback to the Success Stories page.
Since a lot of time and effort has gone into Ipopt's development, please cite the following publication if you are using Ipopt for your own research:
- A. Wächter and L. T. Biegler, On the Implementation of a Primal-Dual Interior Point Filter Line Search Algorithm for Large-Scale Nonlinear Programming, Mathematical Programming 106(1), pp. 25-57, 2006 (preprint)