6 papers
Topological Quantum Compilation Using Mixed-Integer Programming
Pavel Rytir, Phillip C. Burke, Christos Aravanis +2
We introduce the Mixed-Integer Quadratically Constrained Quadratic Programming framework for the quantum compilation problem and apply it in the context of topological quantum comp…
ExMAG: Learning of Maximally Ancestral Graphs
Petr Ryšavý, Pavel Rytíř, Xiaoyu He +2
In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of c…
Power System Steady-State Estimation Revisited
Pavel Rytir, Ales Wodecki, Martin Malachov +4
In power system steady-state estimation (PSSE), one needs to consider (1) the need for robust statistics, (2) the nonconvex transmission constraints, (3) the fast-varying nature of…
ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations
Pavel Rytir, Ales Wodecki, Georgios Korpas +1
Causal learning from data has received much attention recently. Bayesian networks can be used to capture causal relationships. There, one recovers a weighted directed acyclic graph…
Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons
Vyacheslav Kungurtsev, Fadwa Idlahcen, Petr Rysavy +2
In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time.…
ExDAG: an MIQP Algorithm for Learning DAGs
Pavel Rytir, Ales Wodecki, Jakub Marecek
There has been a growing interest in causal learning in recent years. Commonly used representations of causal structures, including Bayesian networks and structural equation models…