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20202026
most citedEfficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings

1 citations · 1 across the 9 of their papers we have counts for

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cs.CC2026

On the Hardness of Finding Temporally Connected Subgraphs of Any Size

Arnaud Casteigts, Christian Komusiewicz, Nils Morawietz

Temporal graphs are graphs whose edges are present only at certain points in time. Reachability in these graphs is defined via temporal paths, in which edges are traversed in incre…

cs.CC2026

A Parameterized-Complexity Framework for Finding Local Optima

Robert Ganian, Hung P. Hoang, Christian Komusiewicz +1

Local search is a fundamental optimization technique that is both widely used in practice and deeply studied in theory, yet its computational complexity remains poorly understood.…

cs.CC2025

The Parameter Report: An Orientation Guide for Data-Driven Parameterization

Christian Komusiewicz, Nils Morawietz, Frank Sommer +1

A strength of parameterized algorithmics is that each problem can be parameterized by an essentially inexhaustible set of parameters. Usually, the choice of the considered paramete…

cs.CC2024

Parameterized Local Search for Max -Cut

Jaroslav Garvardt, Niels Grüttemeier, Christian Komusiewicz +1

In the NP-hard Max -Cut problem, one is given an undirected edge-weighted graph and aims to color the vertices of with colors such that the total weight of edges wit…

cs.CC2023

Complexity of Local Search for Euclidean Clustering Problems

Bodo Manthey, Nils Morawietz, Jesse van Rhijn +1

We show that the simplest local search heuristics for two natural Euclidean clustering problems are PLS-complete. First, we show that the Hartigan--Wong method for -Means cluste…

cs.CC2022

Multi-Parameter Analysis of Finding Minors and Subgraphs in Edge Periodic Temporal Graphs

Emmanuel Arrighi, Niels Grüttemeier, Nils Morawietz +2

We study the computational complexity of determining structural properties of edge periodic temporal graphs (EPGs). EPGs are time-varying graphs that compactly represent periodic b…