Ted Ralphs

CyLP has a new release that supports Numpy 2 for those who were waiting for that! As always, there are binary wheels for all platforms, thanks to #cibuildwheel! github.com/coin-or/CyLP/d…

Super happy to announce @nvidia 's cuOPT as an official @coin_or project: coin-or.org/2025/06/11/ann…

Excited to announce COIN-OR's collaboration with NVIDIA in open-sourcing cuOpt! coin-or.org/2025/03/18/the… blogs.nvidia.com/blog/cuopt-ope…

We're happy to announce the formation of the Bilevel Optimization Society! See the Website for a description and and bylaws: bileveloptimization.org. Register to become a member by filling in this form: forms.office.com/e/xa7m37UDcJ. First elections coming soon, spread the word!

Absentee ballot received! Please vote!

We reformulate the max clique problem as a bilevel optimization problem along the lines of Motzkin-Strauss and show that producing a locally optimal solution to a BLP is a combinatorial problem over bases of the lower level. The result is analogous to Ahmadi and Zhang's for QP.

Great working on this paper with my good friend Oleg Prokopyev, who came up with most of the deep ideas. Even if you have no interest in complexity, there's some good insight into the relationship between bilevel, quadratic, and combinatorial optimization. optimization-online.org/?p=28138

Thrilled and honored to have been one of the speakers at the wonderful Fulkerson Centennial Celebration at Cornell with Jack Edmonds in the house! orie.cornell.edu/orie-events/dr…

The upshot: constructing the RVF is equivalent to constructing the frontier. Exploiting this, we adapt our existing generalized cutting-plane algorithm for constructing the classical value function to the multiobjective MILP setting with an arbitrary # of objectives. Enjoy!

Thanks to my brilliant co-authors Samira Fallah and Natashia Boland, our paper detailing the close relationship between the efficient frontier of a multiobjective MILP and the so-called restricted value function of a related MILP has been accepted at MMOR. arxiv.org/abs/2303.00785

If you attend the session in which @schmaidt 's #ISMP talk is scheduled (WA231 — Bilevel Optimization with Discrete Decisions (I), going on now!), make sure you stick around for Federico Battista's talk about some even more recent attempts to improve performance of MibS!

Here are the results over a much larger set of almost 700 instances, most of which are included in the current #BOBILib collection (the rest will be soon).

In support of @schmaidt 's #ISMP talk on the #BOBILib, here are some some newly minted results using the benchmark set with different versions of MibS over time. This includes the most recently released version 1.2.1 (github.com/coin-or/MibS/r…), which I neglected to post about.

My wife @Ke_Yang_ and co-authors have an interesting new paper about bias in LLMs.

I'm very happy to announce my election as a Fellow of @INFORMS, which was formed the year I got my Ph.D! It's been great to "grow up" side-by-side and I am grateful for all the ways it has contributed to my own evolution as a researcher and a human. engineering.lehigh.edu/news/article/l…

A tech report discussing the source of the improvements in depth is due out anytime, so stay tuned!

Here are some URLs in case you're interested: ALPS (github.com/coin-or/CHiPPS…), BiCePS (github.com/coin-or/CHiPPS…), and BLIS (github.com/coin-or/CHiPPS…).

I'm really excited to announce version 1.2.0 of MibS, our solver for bilevel mixed integer linear optimization. There are a lot of of improvements, especially to usability and out-of-the-box performance. github.com/coin-or/MibS/r….