Welcome to the Ipopt home page
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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 from
min f(x) x in R^n s.t. g_L <= g(x) <= g_U x_L <= x <= x_U
where f(x): R^n --> R is the objective function, and g(x): R^n --> R^m are the constraint functions. The vectors g_L and g_U denote the lower and upper bounds on the constraints, and the vectors x_L and x_U are the bounds on the variables x. The functions f(x) and 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_L and g_U to the same value.
Ipopt is part of the COIN-OR Initiative.
Ipopt is written in C++ and is released as open source code under the Eclipse Public License (EPL). It is available from the COIN-OR initiative. 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 interfaces to CUTEr optimization testing environment, as well as the MATLAB and R programming environments. 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.
You can also obtain the Ipopt code via Subversion. Please refer to the documentation and the General Configuration and Installation Instructions for COIN-OR projects.
Please make sure you read the current issues page before you try to install Ipopt.
Also still available is the no longer maintained older Fortran version.
Download Ipopt binaries (including mex files for use in MATLAB).
Documentation and Support
- Installation Instructions
- Interfacing Ipopt
- Special Features:
- Option Files, Options Reference, Options available via AMPL
- FAQ (Frequently Asked Questions)
- Mailing list archive
- Compilation hints collected by users (partly outdated)
- Current configuration and installation issues for COIN-OR projects
- General Configuration and Installation Instructions for COIN-OR projects
- short Ipopt tutorial
- Hints and tricks on using Ipopt
- Doxygen documentation for the Ipopt source code can be generated by executing make doxydoc in an Ipopt build directory
- Mailing list: subscribe to get notifications about updates and to post questions and comments regarding Ipopt
- Bug report system: If you believe you found a bug in the code, please use the bug report system (see the View Tickets and New Ticket tabs on put of this page).
Please include as much information as possible, and if possible some (ideally simple) example code so that we can reproduce the error. Note that you will have to log in first, as described here.
COIN-OR/AIMMSlinks: Using Ipopt via AIMMS
COIN-OR/GAMSlinks: Using Ipopt via GAMS
COIN-OR/Optimization Services: Using Ipopt via OS
CasADi: Using Ipopt in a symbolic framework for automatic differentiation and numeric optimization.
csipopt: Interfacing Ipopt from .NET languages such as C#, F# and Visual Basic.NET.
JuliaOpt: Using Ipopt via Julia
OPTI Toolbox: Windows x86 + x64 MATLAB Interface, including binaries
pyipopt: Interfacing Ipopt from Python
sci-ipopt: Interfacing Ipopt from Scilab (a free MATLAB-like environment)
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