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Documents authored by Engel, Daniel

Document
DOI: 10.4230/OASIcs.Tokenomics.2021.13
Presentation and Publication: Loss and Slippage in Networks of Automated Market Makers

Authors: Daniel Engel and Maurice Herlihy

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

Abstract
Automated market makers (AMMs) are smart contracts that automatically trade electronic assets according to a mathematical formula. This paper investigates how an AMM’s formula affects the interests of liquidity providers, who endow the AMM with assets, and traders, who exchange one asset for another at the AMM’s rates. Linear slippage measures how a trade’s size affects the trader’s return, angular slippage measures how a trade’s size affects the subsequent market price, divergence loss measures the opportunity cost of providers' investments, and load balances the costs to traders and providers. We give formal definitions for these costs, show that they obey certain conservation laws: these costs can be shifted around but never fully eliminated. We analyze how these costs behave under composition, when simple individual AMMs are linked to form more complex networks of AMMs.
Cite as

Daniel Engel and Maurice Herlihy. Presentation and Publication: Loss and Slippage in Networks of Automated Market Makers. In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{engel_et_al:OASIcs.Tokenomics.2021.13,
  author =	{Engel, Daniel and Herlihy, Maurice},
  title =	{{Presentation and Publication: Loss and Slippage in Networks of Automated Market Makers}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{13:1--13:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.13},
  URN =		{urn:nbn:de:0030-drops-159103},
  doi =		{10.4230/OASIcs.Tokenomics.2021.13},
  annote =	{Keywords: Decentralized Finance, AMM, Uniswap}
}
Document
DOI: 10.4230/OASIcs.VLUDS.2011.135
A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization

Authors: Daniel Engel, Lars Hüttenberger, and Bernd Hamann

Published in: OASIcs, Volume 27, Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011

Abstract
Dimension reduction is commonly defined as the process of mapping high-dimensional data to a lower-dimensional embedding. Applications of dimension reduction include, but are not limited to, filtering, compression, regression, classification, feature analysis, and visualization. We review methods that compute a point-based visual representation of high-dimensional data sets to aid in exploratory data analysis. The aim is not to be exhaustive but to provide an overview of basic approaches, as well as to review select state-of-the-art methods. Our survey paper is an introduction to dimension reduction from a visualization point of view. Subsequently, a comparison of state-of-the-art methods outlines relations and shared research foci.
Cite as

Daniel Engel, Lars Hüttenberger, and Bernd Hamann. A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization. In Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011. Open Access Series in Informatics (OASIcs), Volume 27, pp. 135-149, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{engel_et_al:OASIcs.VLUDS.2011.135,
  author =	{Engel, Daniel and H\"{u}ttenberger, Lars and Hamann, Bernd},
  title =	{{A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and Visualization}},
  booktitle =	{Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011},
  pages =	{135--149},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-46-0},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{27},
  editor =	{Garth, Christoph and Middel, Ariane and Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2011.135},
  URN =		{urn:nbn:de:0030-drops-37475},
  doi =		{10.4230/OASIcs.VLUDS.2011.135},
  annote =	{Keywords: high-dimensional, multivariate data, dimension reduction, manifold learning}
}
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