Our paper Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones? was published online as open access article.
I will present our work An Augmented Lagrangian Filter Method (joint work with Sven Leyffer) at the SIAM CSE21 conference on March 1 at 10:10 (local time)/16:10 (UTC+1). I will also chair the session on Methods for Constrained Optimization in CSE starting at 9:45 (local time)/15:45 (UTC+1).
More information here.
Our article Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones? has been accepted for publication in the Computers and Chemical Engineering journal.
It was cowritten with Philipp Seufert, Jan Schwientek (Fraunhofer ITWM), Gleb Karpov, Gleb Ryzhakov, Ivan Oseledets (Skoltech), Norbert Asprion (BASF SE) and Michael Bortz (ITWM).
ResearchGate link here.
ScienceDirect link here.
Thorsten Koch and I are giving a lecture at the Technische Universität Berlin called Industrial Data Science. It covers topics such as data visualization, probability and statistics, optimization and machine learning. The 15 sessions take place from Nov 2020 to Feb 2021.
I have the pleasure to start a new contract as a postdoctoral researcher at the Technische Universität Berlin (TUB), under the supervision of Thorsten Koch. I joined the UNSEEN project dedicated to the optimization of energy model systems using a combination of Mixed Integer Programming and Machine Learning techniques. It is a cooperation between the TUB, the Zuse Institut Berlin (ZIB), Jülich Supercomputing Center, GAMS and the DLR. I will be responsible for the project on the TUB/ZIB side.
The slides of my talk Argonot: An Open-Source Software Framework for Nonlinear Optimization at ISMP 2018 are available on ResearchGate:
Iterative methods for nonlinear optimization usually share common ingredients, such as strategies to compute a descent direction or mechanisms that promote global convergence. Our new open-source framework for nonlinearly constrained optimization, Argonot, offers a selection of off-the-shelf strategies that can be assembled at will. Argonot thus implements a variety of methods (e.g. trust-region filter SQP, line-search penalty Sl1QP, …) and interfaces with specialized solvers (BQPD, MA57) with no programming effort from the user. Argonot also provides an interface to the algebraic modeling language AMPL. We present extensive results on a subset of problems from the CUTEst collection, and compare Argonot against state-of-the-art solvers CONOPT, IPOPT, KNITRO, LANCELOT, LOQO, MINOS and SNOPT.
Our paper A survey of nonlinear robust optimization (Sven Leyffer, Matt Menickelly, Todd Munson, Charlie Vanaret and Stefan M. Wild) was published online today in the INFOR: Information Systems and Operational Research journal.
Our preprint Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones? is online on ResearchGate!
On the occasion of the 5th anniversary of my doctoral defense in Toulouse (France), I finally completed the English translation of my dissertation, entitled “Hybridization of interval methods and evolutionary algorithms for solving difficult optimization problems“.
My rigorous solver Charibde combines an interval branch and contract algorithm with an evolutionary algorithm in a parallel fashion ; the two algorithms exchange bounds, solutions and domains via message passing, in order to intensify the pruning of the search space. Charibde proved competitive with interval-based solvers GlobSol, IBBA and Ibex on a subset of difficult COCONUT problems. I also provided new optimality results for highly multimodal problems for which few (even approximate) solutions are known. Finally, I presented the first numerical proof of optimality for the open Lennard-Jones problem with five atoms. State-of-the-art interval-based solvers did not converge within reasonable time, while exhaustive solvers BARON and Couenne produced erroneous results.