Old and New Open Questions in Optimization

Princeton University is actively monitoring the situation around coronavirus (Covid-19) and the evolving guidance from government and health authorities. Any updates to this event, including rescheduling to a later date, will be posted to this website. For the latest University guidance for University members and visitors is available on the University’s Emergency Management website. 

Due to ongoing concerns and public safety and health restrictions associated with COVID-19, this workshop has been postponed.  Additional details will be provided at a later date.

Old and New Open Questions in Optimization

Our main goal is to inform researchers, especially young scientists, about old and new open questions in optimization.

Regarding long standing questions, we place a particular emphasis on the BFGS algorithm: one of the more widely used optimization algorithms today—a remarkable feat considering it is turning 50 in 2020. Just as remarkable is the fact that several elementary questions about it remain unanswered.
Regarding new questions, we put the spotlight on emerging opportunities in non-convexity, and its ties to machine learning. After the reigns of linear and of convex programming, we need a new theory of what “tractable optimization” means.


  • Nicolas Boumal

    Department of Mathematics
    Princeton University (moving to EPFL in the summer of 2020)

  • Yuxin Chen


  • Damian Scieur

    Damien Scieur
    Samsung SAIL Montreal


We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation and our Princeton University partners:

CSMLDataX Logo - A series of lines converging at a 45 degree angle to a point with DataX in the foreground