Marc E. Pfetsch

SODA Conference 2024 Conference Paper

Sub-Exponential Lower Bounds for Branch-and-Bound with General Disjunctions via Interpolation

• Max Gläser
• Marc E. Pfetsch

ICML Conference 2020 Conference Paper

IPBoost - Non-Convex Boosting via Integer Programming

• Marc E. Pfetsch
• Sebastian Pokutta

Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification by means of integer programming and demonstrate real-world practicability of the approach while circumvent- ing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.

SAT Conference 2009 Conference Paper

Nonlinear Pseudo-Boolean Optimization: Relaxation or Propagation?

• Timo Berthold
• Stefan Heinz 0001
• Marc E. Pfetsch

Abstract Pseudo-Boolean problems lie on the border between satisfiability problems, constraint programming, and integer programming. In particular, nonlinear constraints in pseudo-Boolean optimization can be handled by methods arising in these different fields: One can either linearize them and work on a linear programming relaxation or one can treat them directly by propagation. In this paper, we investigate the individual strengths of these approaches and compare their computational performance. Furthermore, we integrate these techniques into a branch-and-cut-and-propagate framework, resulting in an efficient nonlinear pseudo-Boolean solver.