Interactive Optimization and Learning
The IOL research lab is dedicated to exploring the intersection of mathematical optimization and machine learning, with a focus on developing innovative techniques for learning and optimization. By integrating these two fields, we aim to create new approaches to solving complex problems that leverage the strengths of both optimization and machine learning.
Our group is located both at the Mathematical Optimization research group at the Technische Universität Berlin (TUB) and in the AI in Society, Science, and Technology (AIS²T) department at the Zuse Institute Berlin (ZIB). We are also part of the Berlin mathematics research center MATH+ as well as the Berlin Mathematical School (BMS).
Upcoming seminar talks
Ronald de Wolf
(QuSoft, CWI and University of Amsterdam)
Quantum Algorithms for Optimization
@ Zuse Institute Berlin (ZIB), Room 2006 (Seminar Room)
Publication highlights
Software repositories
All our publically accessible software repositories are available on GitHub. We have a list of actively maintained repositories:
- FrankeWolfe.jl, a toolbox for Frank-Wolfe and conditional gradients algorithms
- Boscia.jl, a package for Branch-and-Bound on top of Frank-Wolfe methods
- BellPolytopes.jl, a package that addresses the membership problem for local polytopes
We are also actively involved in the development of the SCIP Optimization Suite at ZIB and its interfaces to other programming languages:
- SCIP, one of the fastest academically developed solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP)
- SoPlex, an optimization package for solving linear programming problems.
- PaPILO parallel presolve routines for (mixed integer) linear programming problems
- PySCIPOpt, a Python interface for SCIP
- SCIP.jl, a Julia interface for SCIP
- JSCIPOpt, a Java interface for SCIP
- russcip, a Rust interface for SCIP