Subject to: Aharon Ben-Tal (Technion, Israel), youtu.be/i02gidY1-LQ, #orms #optimization #robustoptimization @TechnionLive @TelAvivUni @NorthwesternU @INFORMS @siam @TheSIAMNews

Happy to share this new article on Sampling Methods for Multi-Stage Robust Optimization Problems, available today in Annals of Operations Research, join work with Fabrizio Dabbene and Georg Pflug. link.springer.com/article/10… #robustoptimization #constraintsampling

Happy to share this new article "A novel robust optimization model for nonlinear Support Vector Machine" available in European Journal of Operational Reseach, joint work with A. Spinelli: sciencedirect.com/science/ar… #machinelearning #classification #robustoptimization

In our new work, with @matbesancon, we study #robustoptimization problems from an oracle perspective. We develop #FrankWolfe type algorithms applied to a smoothed version of the problem, which converge to an optimal solution within a certain number of oracle calls. 1/n

Our work on data-driven predictions of relevant start scenarios in #robustoptimization is now published and online available: doi.org/10.1016/j.cor.2024.1… In our work we tackle (two-stage) robust combinatorial optimization problems which are extremely hard to solve.

The extended version of our work on #DeepLearning for Two-Stage Robust Integer Optimization is available now: arxiv.org/abs/2310.04345 Our new version of the algorithm can tackle two-stage #robustoptimization problems with uncertainty in the constraints and integer decisions.

The next Robust Optimization Webinar will take place this Friday, October 18, at 15:00 (CET). Speaker: Adam Kasperski (Wrocław University of Science and Technology) Title: Recoverable robust shortest path problem #ROW #RobustOptimization

In my new work I study k-adaptability problems which can be used to approximate two-stage robust optimization problems. I derive bounds on k which guarantee optimality. Link: arxiv.org/abs/2409.12630 #robustoptimization #kadaptability

My presentation on #DeepLearning in #RobustOptimization I gave at the CO@Work workshop is now online (as many of the other talks). youtube.com/watch?v=nX7RLEDG… Big thanks to Timo Berthold and all other organizers for the invitation and the great organization of the workshop.

Piero Gasparotto et al.: TORO Indexer: a PyTorch-based indexing algorithm for kilohertz serial crystallography #SerialCrystallography #RobustOptimization #RealTimeIndexing ... #IUCr journals.iucr.org/paper?S160…

Our paper on using neural networks to speed up solution methods for two-stage robust optimization problems got published now at #ICLR2024 Conference. You can find the updated version here: openreview.net/forum?id=T5Xb… #robustoptimization #neuralnetworks

size matters, in #RobustOptimization. If you are among those who said RO generates conservative solutions, look what your uncertainty set is. We showed in this paper that we can beat even the solution of #stochOpt in different simulations; papers.ssrn.com/sol3/papers.…

#Bilevel people looking at #robustoptimization problems @schmaidt @thuerauf_j x.com/Yoda4ever/status/17240…

Our new work about integrating #neuralnetwork predictions into algorithmic frameworks for solving two-stage #robustoptimization problems is out: arxiv.org/abs/2310.04345 Thanks a lot to @lyeskhalil and especially to Justin Dumouchelle and @_estherjulien who made this work!

#ODS2023 #Day1 #RobustOptimization Alice Calamita talks about a robust optimization approach relying on Benders decomposition to minimize activation and congestion costs in partial set covering facility location problems @ODS2023 #orms

The next season of the #RobustOptimizationWebinar will start soon! We will have again a list of amazing speakers. You can find out more on our webpage: sites.google.com/view/row-se… @schmaidt @melvynsim @hri_lefebvre @_estherjulien #ROW #RobustOptimization

This is actually quite useful in #robustoptimization, where we use this type of duality a lot.

Join us today for the next #ROW with Omar El Housni! #robustoptimization

Our next Robust Optimization Webinar takes place this week Friday, 16 June at 15:00 (CET). Speaker: Omar El Housni (Cornell University) Title: Robust Facility Location Problems #ROW #robustoptimization

Reflections on Dabeen Lee's talk at #MIP2023 on "Non-smooth and robust submodular maximization." Submodularity - the principle of diminishing returns - is an essential concept in optimization, surfacing across various fields, including machine learning, network design, and sensor placement. Lee's presentation tackled the problem of maximizing continuous DR-submodular functions, which may not always be smooth. Drawing potential parallels to energy systems planning, the submodular nature of benefits can be observed in the deployment of Distributed Energy Resources (DERs) or Electric Vehicle (EV) charging stations. As additional units are installed, the marginal benefit in certain locations may decrease. The goal, when positioning DERs, might be to augment the total energy supply to the grid or curtail grid losses without having to curtain wind or shed demand. Similarly, the planning of EV charging stations could aim to maximize coverage or minimize driver inconvenience. Robust and distributionally robust optimization methods could aid decision-making, ensuring performance remains reliable under varying scenarios, thereby leading to more resilient energy and transportation systems. While I speculate on these potential applications, the talk opens up an interesting avenue for thought with the broader impact and adaptability of optimization methodologies in our changing world. #SubmodularOptimization #RobustOptimization #EnergySystems #MIP2023