The COIN-OR Cup recognizes and celebrates the best contributions to open-source operations research software development and use associated with the COIN-OR software collection. To remind you, COIN-OR stands for “COmputational INfrastructure for Operations Research”. It is the definitive collection of free, open-source operations research software that lets you concentrate on your research instead of re-implementing software. See https://www.coin-or.org. Get on board!

GUIDELINES FOR COIN-OR CUP SUBMISSIONS:

A submission or nomination must contain:

1. A synopsis of an effective use of COIN-OR or valuable contribution to COIN-OR (or both!), including an explanation of its significance. Maximum 3 pages (pdf format).
2. Copies of relevant papers or documents (pdf format). We especially encourage nominations describing an effective use of COIN-OR that the community may not know about.
3. A submission to this Google form: Nominations for the 2025 COIN-OR Cup Competition

Self-nominations are welcome and encouraged.

Submission deadline: Tuesday, September 30th 2025, 11:59pm ET.

Please e-mail your submissions to coin-cup-2025@coin-or.org in a single zipped archive, containing a description of your submission and copies of all relevant papers/documents in pdf format.

Committee members: Daniel Bienstock, Haihao Lu (chair), Jinwen Yang.

The winner will be announced and celebrated during the INFORMS annual meeting.  Details for the announcement and celebration will be revealed when available.

We are pleased to announce the addition of NVIDIA cuOpt to the COIN-OR repository on Github. cuOpt is an open source library implementing GPU-accelerated algorithms for optimization. The addition of cuOpt to the COIN-OR repository follows the announcement of COIN-OR’s collaboration with NVIDIA, which you can read about here.

A New Chapter: COIN-OR and NVIDIA cuOpt Join Forces for Open-Source Optimization

Optimization has always been at the core of computational research, and now, a new frontier is emerging—one where open-source innovation meets GPU acceleration. COIN-OR and NVIDIA are collaborating to bring NVIDIA cuOpt, a GPU-accelerated optimization engine, into the COIN-OR open-source ecosystem. This collaboration aims to empower researchers, developers, and industry leaders with cutting-edge tools to drive breakthroughs in large-scale optimization.

This is more than just an open-source release—it’s about fostering a dynamic, community-driven ecosystem where experts can push the boundaries of optimization, explore novel GPU-powered techniques, and accelerate real-world impact.

The COIN-OR Foundation: A Legacy of Open Innovation

The COIN-OR Foundation was born out of a need: By the late ‘90s, optimization research was advancing rapidly, yet the lack of open-source software was slowing progress. In 2000, IBM launched the COIN-OR Initiative to change that, providing a public repository of open-source tools for optimization. By 2004, COIN-OR had grown into an independent, nonprofit educational foundation, dedicated to fostering a thriving, community-driven ecosystem for computational operations research.

Today, COIN-OR has evolved into a global hub for open innovation, hosting over 70 projects on GitHub and supporting thousands of researchers and practitioners worldwide. As a strategic partner of the Institute for Operations Research and Management Science (INFORMS) Computing Society, COIN-OR organizes annual competitions, workshops, and fosters the development of major open-source projects such as Cbc, Ipopt, and HiGHS. Our mission remains steadfast: to provide the tools and platform for optimization researchers to push the boundaries of computational science.

New Frontiers in Optimization: GPUs and Machine Learning

As the algorithms for workhorse problem classes, such as mixed-integer linear optimization, have matured, the focus of computational research has shifted towards leveraging existing solvers as black-box tools for tackling increasingly complex problem classes, such as multi-stage stochastic optimization, robust optimization, and bilevel optimization.

The rise of machine learning and GPU acceleration has now created new opportunities to reimagine optimization algorithms. By harnessing the massive parallelism of GPUs, classical optimization methods—such as continuous linear optimization—are being revolutionized, enabling faster, more scalable solutions. Early indications suggest that GPU acceleration could lead to  entirely new algorithmic breakthroughs, transforming how optimization problems are solved.

Forging a Strategic Collaboration: COIN-OR and NVIDIA cuOpt

At COIN-OR, we believe in building on the past to shape the future and our partnership with NVIDIA marks a milestone in the evolution of open-source optimization. We are thrilled to collaborate with NVIDIA as it releases NVIDIA cuOpt—its GPU-accelerated optimization algorithms—under an open-source license, making it available within the COIN-OR repository. 

This partnership isn’t just about making cuOpt open source—it’s about building a vibrant research and development environment, where  COIN-OR and NVIDIA will co-sponsor implementation challenges, host dedicated sessions at major conferences, and engage with the broader COIN-OR community.

By combining NVIDIA’s GPU expertise with COIN-OR’s deep roots in open-source optimization, we aim to fuel innovation, accelerate research, and reimagine new possibilities in large-scale optimization. 

This is just the beginning. We look forward to the groundbreaking discoveries that will emerge from this partnership. If you are a researcher, developer, or industry leader passionate about optimization, now is the time to get involved—join us as we embark on this exciting journey!

Nominations are solicited for candidates for the position of director
on the Strategic Leadership Board (SLB). All positions are
for three-year terms (through December 31, 2027). Any interested
individual is eligible to be a director of the Foundation.

COIN-OR is evolving rapidly, and directors can have a significant
influence on the development of the Foundation. Candidates should be
actively interested in COIN-OR and committed to active participation
in Foundation business. For more information on the activities of and
expectations for directors, prospective candidates are encouraged to
contact any current directors or the Foundation Secretary
(secretary at coin-or.org).

Note that membership in COIN-OR is not required. Candidates with
expertise in the software industry or experience as a director for a
non-profit organization will be eagerly welcomed.

The Foundation encourages the nomination of any interested individuals,
particularly users of COIN-OR software, who have not previously
been active participants in COIN-OR projects. Diversity of viewpoints
and skills is both needed and appreciated.

For all director positions, a candidate may be nominated by any
interested individual. Self-nomination is encouraged. All nominations
must be seconded by a full member of the Foundation.

Nominations should be emailed to the Secretary (secretary at
coin-or.org) and will be accepted until November 15, 2024. Candidates
must agree to be nominated and will be contacted to provide a short
biography and statement to be distributed to the full members of the
Foundation prior to the election.

We are pleased to announce that  Haihao Lu and Jinwen Yang are awarded the 2024 COIN-OR Cup for cuPDLP.jl. This project has demonstrated great novelty by leveraging an underexplored first-order method for linear programming. In addition to delivering promising numerical results on large-scale LP instances, cuPDLP has gained traction in the optimization community, with several solvers already integrating it. Also, cuPDLP actively adopts GPU acceleration techniques, which open the door for significant advancements in computational efficiency. The exceptional contributions made by cuPDLP.jl have advanced the computational infrastructure for linear programming, and we look forward to seeing its continued growth and impact.

Prize Committee: Mihai Anitescu (Argonne National Laboratory), Sungho Shin (MIT), François Pacaud (Université Paris)

If you are attending the INFORMS Annual Meeting in Seattle on October 21, join us to celebrate the winners and the 20th anniversary of the COIN-OR Foundation. Watch the conference’s social media for the time and location.

Congratulations to Robin Lougee, Coin-OR co-founder, for winning the INFORMS Computing Society Distinguished Service Award for 2024. To quote the award announcement: “Robin was a co-creator of the Computational Infrastructure for Operations Research (COIN-OR) initiative, which was established in 2004 as a strategic partner of the INFORMS Computing Society (ICS). The goal of the COIN-OR initiative is to accelerate the development and adoption of open-source software for computational operations research, a major goal of ICS itself. She was co-recipient of the 2014 INFORMS Impact Prize for this effort, which has been of major benefit to ICS.”

Well done, Robin; it is fantastic to see your contributions over so many years being acknowledged.

COIN-OR Strategic Leadership Board

Nominations are solicited for candidates for the position of director
on the Strategic Leadership Board (SLB). All positions are
for three-year terms (through December 31, 2026). Any interested
individual is eligible to be a director of the Foundation.

COIN-OR is evolving rapidly, and directors can have a significant
influence on the development of the Foundation. Candidates should be
actively interested in COIN-OR and committed to active participation
in Foundation business. For more information on the activities of and
expectations for directors, prospective candidates are encouraged to
contact any current directors or the Foundation Secretary
(secretary at coin-or.org).

Note that membership in COIN-OR is not required. Candidates with
expertise in the software industry or experience as a director for a
non-profit organization will be eagerly welcomed.

The Foundation encourages the nomination of any interested individuals,
particularly users of COIN-OR software, who have not previously
been active participants in COIN-OR projects. Diversity of viewpoints and
skills is both needed and appreciated.

For all director positions, a candidate may be nominated by any
interested individual. Self-nomination is encouraged. All nominations
must be seconded by a full member of the Foundation.

Nominations should be emailed to the Secretary (secretary at coin-
or.org) and will be accepted until January 10, 2024. Candidates must
agree to be nominated and will be contacted to provide a short
biography and statement to be distributed to the full members of the
Foundation prior to the election.

More information on the COIN-OR foundation can be found at
https://www.coin-or.org/about-the-foundation/.

Big congratulations to Sungho Shin, François Pacaud, and Mihai Anitescu for winning the 2023 COIN-OR Cup in a very competitive field with their work ExaModels (https://github.com/sshin23/ExaModels.jl) and MadNLP (https://github.com/MadNLP/MadNLP.jl).

Open-source software packages ExoModels and MadNLP represent a significant breakthrough in efficiently solving large-scale NLPs by leveraging the capabilities of modern GPUs. These packages achieve a remarkable performance enhancement in solving real-world nonlinear optimization problems at scale. A noteworthy example is the recent accomplishment in accelerating alternating current (AC) optimal power flow (OPF) problems by up to a factor of ten compared to state-of-the-art tools. 

We were delighted to celebrate the winning nomination at the INFORMS Annual meeting. Thanks to Jordan Jalving, Carl Laird, Ruth Misener, and Calvin Tsay for serving on the committee.

Congratulations to Francesco Ceccon, Joshua Haddad, Jordan Jalving, Carl D Laird, Ruth Misener, Alexander Thebelt, and Calvin Tsay for winning the 2022 COIN-OR Cup for their submission OMLT (https://github.com/cog-imperial/OMLT). 

We didn’t *only* select OMLT because it makes us hungry for a yummy 🍳 omelet 🍳. OMLT represents neural networks and gradient-boosted trees in an algebraic modeling language (Pyomo) suitable for integrating into optimization formulations. Optimizing over trained surrogate models is important because it allows integration of NNs or GBTs into larger decision-making problems. Computer science applications include maximizing a neural acquisition function or verifying neural networks. In engineering applications, machine learning models may replace complicated constraints or serve as surrogates in larger design and operations problems. OMLT supports GBTs through an ONNX interface and NNs through both ONNX and Keras interfaces. OMLT transforms these pre-trained machine learning models into the algebraic modeling language Pyomo to encode the optimization formulations.

But also, the association with our tummies did help the winners, who we hope will make us breakfast.