134 research outputs found
Robust Assignments via Ear Decompositions and Randomized Rounding
Many real-life planning problems require making a priori decisions before all
parameters of the problem have been revealed. An important special case of such
problem arises in scheduling problems, where a set of tasks needs to be
assigned to the available set of machines or personnel (resources), in a way
that all tasks have assigned resources, and no two tasks share the same
resource. In its nominal form, the resulting computational problem becomes the
\emph{assignment problem} on general bipartite graphs.
This paper deals with a robust variant of the assignment problem modeling
situations where certain edges in the corresponding graph are \emph{vulnerable}
and may become unavailable after a solution has been chosen. The goal is to
choose a minimum-cost collection of edges such that if any vulnerable edge
becomes unavailable, the remaining part of the solution contains an assignment
of all tasks.
We present approximation results and hardness proofs for this type of
problems, and establish several connections to well-known concepts from
matching theory, robust optimization and LP-based techniques.Comment: Full version of ICALP 2016 pape
An Improved Approximation Algorithm for the Matching Augmentation Problem
We present a 5/3-approximation algorithm for the matching augmentation problem (MAP): given a multi-graph with edges of cost either zero or one such that the edges of cost zero form a matching, find a 2-edge connected spanning subgraph (2-ECSS) of minimum cost.
A 7/4-approximation algorithm for the same problem was presented recently, see Cheriyan, et al., "The matching augmentation problem: a 7/4-approximation algorithm," Math. Program., 182(1):315-354, 2020.
Our improvement is based on new algorithmic techniques, and some of these may lead to advances on related problems
Spectral Relaxations and Fair Densest Subgraphs
Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention. In this paper, we address the problem of identifying a densest subgraph, while ensuring that none of one binary protected attribute is disparately impacted. Unfortunately, the underlying algorithmic problem is NP-hard, even in its approximation version: approximating the densest fair subgraph with a polynomial-time algorithm is at least as hard as the densest subgraph problem of at most k vertices, for which no constant approximation algorithms are known. Despite such negative premises, we are able to provide approximation results in two important cases. In particular, we are able to prove that a suitable spectral embedding allows recovery of an almost optimal, fair, dense subgraph hidden in the input data, whenever one is present, a result that is further supported by experimental evidence. We also show a polynomial-time, -approximation algorithm, whenever the underlying graph is itself fair. We finally prove that, under the small set expansion hypothesis, this result is tight for fair graphs. The above theoretical findings drive the design of heuristics, which we experimentally evaluate on a scenario based on real data, in which our aim is to strike a good balance between diversity and highly correlated items from Amazon co-purchasing graphs
Dynamic operation, efficient calibration, and advanced data analysis of gas sensors : from modelling to real-world operation
This thesis demonstrates the use of dynamic operation, efficient calibration and advanced data analysis using metal oxide semiconductor (MOS) gas sensors as an example â from modeling to real-world operation. The necessary steps for an applicationspecific, selective indoor volatile organic compound (VOC) measurement system are addressed, analyzed and improved. Factors such as sensors, operation, electronics and calibration are considered. The developed methods and tools are universally transferable to other gas sensors and applications. The basis for selective measurement is temperature cyclic operation (TCO). The model-based understanding of a semiconductor gas sensor in TCO for the optimized development of operating modes and data evaluation is addressed and, for example, the tailored and stable detection of short gas pulses is developed. Two successful interlaboratory tests for the measurement of VOCs in independent laboratories are described. Selective measurements of VOCs in the laboratory and in the field are successfully demonstrated. Calibrations using the proposed techniques of randomized design of experiment (DoE), model-based data evaluation and calibration with machine learning methods are employed. The calibrated models are compared with analytical measurements using release tests. The high agreement of the results is unique in current research.Diese Thesis zeigt den Einsatz von dynamischem Betrieb, effizienter Kalibrierung, und fortschrittlicher Datenanalyse am Beispiel von Metalloxid Halbleiter (MOS) Gassensoren â von der Modellierung bis zum realen Betrieb. Die notwendigen Schritte fĂŒr ein anwendungsspezifisches, selektives Messystem fĂŒr flĂŒchtige organische Verbindungen (VOC) im Innenraum werden adressiert, analysiert und verbessert. Faktoren wie z.B. Sensoren, Funktionsweise, Elektronik und Kalibrierung werden berĂŒcksichtigt. Die entwickelten Methoden und Tools sind universell auf andere Gassensoren und Anwendungen ĂŒbertragbar. Grundlage fĂŒr die selektive Messung ist der temperaturzyklische Betrieb (TCO). Auf das modellbasierte VerstĂ€ndnis eines Halbleitergassensors im TCO fĂŒr die optimierte Entwicklung von Betriebsmodi und Datenauswertung wird eingegangen und z.B. die maĂgeschneiderte und stabile Detektion von kurzen Gaspulsen entwickelt. Zwei erfolgreiche Ringversuche zur Messung von VOCs in unabhĂ€ngigen Laboren werden beschrieben. Selektive Messungen verschiedener VOCs im Labor und im Feld werden erfolgreich demonstriert. Dabei kommen Kalibrierungen mit den vorgeschlagenen Techniken des randomisierten Design of Experiment (DoE), der modellbasierten Datenauswertung und Kalibrierung mit Methoden des maschinellen Lernens zum Einsatz. Die kalibrierten Modelle werden anhand von Freisetzungstests mit analytischen Messungen verglichen. Die hohe Ăbereinstimmung der Ergebnisse ist einzigartig in der aktuellen Forschung
Recent Advances in Fully Dynamic Graph Algorithms
In recent years, significant advances have been made in the design and
analysis of fully dynamic algorithms. However, these theoretical results have
received very little attention from the practical perspective. Few of the
algorithms are implemented and tested on real datasets, and their practical
potential is far from understood. Here, we present a quick reference guide to
recent engineering and theory results in the area of fully dynamic graph
algorithms
Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs
Finding dense subgraphs is a fundamental algorithmic tool in data mining,
community detection, and clustering. In this problem, one aims to find an
induced subgraph whose edge-to-vertex ratio is maximized.
We study the directed case of this question in the context of semi-streaming
and massively parallel algorithms. In particular, we show that it is possible
to find a approximation on randomized streams even in a single
pass by using memory on -vertex graphs. Our
result improves over prior works, which were designed for arbitrary-ordered
streams: the algorithm by Bahmani et al. (VLDB 2012) which uses
passes, and the work by Esfandiari et al. (2015) which makes one pass but uses
memory. Moreover, our techniques extend to the Massively Parallel
Computation model yielding rounds in the super-linear and rounds in the nearly-linear memory regime. This constitutes a quadratic
improvement over state-of-the-art bounds by Bahmani et al. (VLDB 2012 and WAW
2014), which require rounds even in the super-linear memory regime.
Finally, we empirically evaluate our single-pass semi-streaming algorithm on
benchmarks and show that, even on non-randomly ordered streams, the quality
of its output is essentially the same as that of Bahmani et al. (VLDB 2012)
while it is times faster on large graphs
Approximate Fully Dynamic Directed Densest Subgraph
We give a fully dynamic algorithm maintaining a -approximate
directed densest subgraph in amortized
time or worst-case time per edge update
(where hides factors), based on earlier work by Chekuri
and Quanrud [arXiv:2210.02611, arXiv:2310.18146]. This result improves on
earlier work done by Sawlani and Wang [arXiv:1907.03037], which guarantees
worst case time for edge insertions and deletions
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