|Version 8 (modified by andreasw, 7 years ago) (diff)|
Success Stories and Active Work of Ipopt Users
This section is intended for people to describe their successes using Ipopt. Ipopt has been a COIN-OR open-source project since 2002 with a number of active users and contributors. While there have been many successes in this time, this Wiki has only been available since the release of Ipopt 3.0 (Aug. 26th, 2005). Please feel free to contribute successes, past or present, and for any version of Ipopt.
If you are using Ipopt, please add some comments about your application to the Wiki page. We are all eager to hear about Ipopt, its Applications, and its Users. Please have a look here to see how to edit this Wiki page. If you have a publication about work that uses Ipopt, please add a reference here.
Ipopt used in SmartFolio
Submitted by Boris Gnedenko, Managing director of Modern Investment Technologies Ltd.
SmartFolio is an Excel-based asset allocation, portfolio optimization and risk management software. It contains a wide range of advanced optimization criteria, most of which result in general NLP problems, as well as more traditional QP portfolio optimization problem. To successfully address these issues SmartFolio uses Ipopt 3.0.1, selected for being far more stable and several orders faster than standard Excel Solver.
For more details about SmartFolio see www.smartfolio.com.
Solution of Mixed Integer Nonlinear Programs
Ongoing research project involving IBM and Carnegie Mellon University.
A joint project has been started to develop open source solvers for mixed integer nonlinear programs (MINLPs), and has resulted in the COIN-OR project Bonmin.
Ipopt 3.2 is the primary NLP solver currently used in this development. To see more details about this project, see also the CMU-IBM Open Source MINLP Project.
Solution of Almost Separable Problems with Few Common Variables
Submitted by Carl Laird, Co-author of Ipopt 3.0.0.
There are a number of optimization problems that can be cast into a form that has an almost separable structure with few common variables (Optimal Design Under Uncertainty). The linear system that is solved at each iteration of Ipopt inherits this problem structure and decomposition techniques exist for efficient solution of these large NLPs. My primary interest in contributing to the new C++ version of Ipopt was to help build an object-oriented optimization tool that could be easily modified for the solution of large structured problems.
Currently, I have implemented a CompositeNLP that allows the building of a large almost separable problem and I am working on the decomposition technique (both serial and parallel version).