https://wiki.scc.kit.edu/lsdf/index.php?title=Algorithm_Selection_for_Combinatorial_Auctions&feed=atom&action=historyAlgorithm Selection for Combinatorial Auctions - Revision history2024-03-28T21:28:44ZRevision history for this page on the wikiMediaWiki 1.31.16https://wiki.scc.kit.edu/lsdf/index.php?title=Algorithm_Selection_for_Combinatorial_Auctions&diff=4308&oldid=prevDiana.gudu: Diana.gudu moved page Combinatorial Auctions to Algorithm Selection for Combinatorial Auctions2016-09-05T14:02:06Z<p>Diana.gudu moved page <a href="/lsdf/index.php?title=Combinatorial_Auctions" class="mw-redirect" title="Combinatorial Auctions">Combinatorial Auctions</a> to <a href="/lsdf/index.php?title=Algorithm_Selection_for_Combinatorial_Auctions" title="Algorithm Selection for Combinatorial Auctions">Algorithm Selection for Combinatorial Auctions</a></p>
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</td></tr></table>Diana.guduhttps://wiki.scc.kit.edu/lsdf/index.php?title=Algorithm_Selection_for_Combinatorial_Auctions&diff=4307&oldid=prevDiana.gudu: Created page with "= Description = In this research project at SCC, we are investigating a machine learning approach to select the best algorithm for solving an instance of a combinatorial auct..."2016-09-05T14:01:31Z<p>Created page with "= Description = In this research project at SCC, we are investigating a machine learning approach to select the best algorithm for solving an instance of a combinatorial auct..."</p>
<p><b>New page</b></p><div>= Description =<br />
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In this research project at SCC, we are investigating a machine learning approach to select the best algorithm for solving an instance of a combinatorial auction [0], based on features of the problem instance. This approach is called ''algorithm selection'' [1] and is usually applied to NP-hard problems (like combinatorial auctions), where computing an optimal solution is intractable, and approximate algorithms perform significantly different across the input space. <br />
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This work is motivated by resource allocation in cloud computing, where a flexible and market-driven allocation modeled as a combinatorial auction can improve both provider revenue and client utility.<br />
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Your tasks would be to investigate and implement different auction algorithms, on which you can later apply various classification algorithms for feature-based algorithm selection.<br />
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You will have to work with existing C++ code for auction input generation and testing, as well as the ''scikit-learn'' [3] python toolset for machine learning.<br />
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= Tasks =<br />
* research existing algorithms for allocation in combinatorial auctions<br />
* implement different algorithms<br />
* evaluate algorithms' performance under different scenarios<br />
* evaluate classification approach on the implemented algorithm portfolio<br />
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= Requirements =<br />
* good knowledge of C++ and Python<br />
* some theoretical computer science background or machine learning knowledge would be a plus<br />
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= References =<br />
: [0] S. De Vries and R. V. Vohra. Combinatorial auctions: A survey. INFORMS Journal on computing, 2003. [http://dx.doi.org/10.1287/ijoc.15.3.284.16077 doi:10.1287/ijoc.15.3.284.16077].<br />
: [1] Kotthoff, Lars. "Algorithm selection for combinatorial search problems: A survey." AI Magazine 35.3 (2014): 48-60. [http://aaaipress.org/ojs/index.php/aimagazine/article/download/2460/2438]<br />
: [2] Leyton-Brown, Kevin, Eugene Nudelman, and Yoav Shoham. "Empirical hardness models: Methodology and a case study on combinatorial auctions." Journal of the ACM (JACM) 56.4 (2009): 22. [http://dx.doi.org/10.1145/1538902.1538906 doi:10.1145/1538902.1538906]<br />
: [3] [http://scikit-learn.org/stable/ scikit-learn]<br />
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= Contact =<br />
[mailto:diana.gudu@kit.edu Diana.Gudu@kit.edu]</div>Diana.gudu