Anytime Tradeoff Strategies with Multiple Targets: Unterschied zwischen den Versionen
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|vortragstyp=Masterarbeit | |vortragstyp=Masterarbeit | ||
|betreuer=Edouard Fouché | |betreuer=Edouard Fouché | ||
|termin=Institutsseminar/2019-11-22 | |termin=Institutsseminar/2019-11-22 Zusatztermin | ||
|kurzfassung=Modern applications typically need to find solutions to complex problems under limited time and resources. In settings, in which the exact computation of indicators can either be infeasible or economically undesirable, the use of “anytime” algorithms, which can return approximate results when interrupted, is particularly beneficial, since they offer a natural way to trade computational power for result accuracy. | |kurzfassung=Modern applications typically need to find solutions to complex problems under limited time and resources. In settings, in which the exact computation of indicators can either be infeasible or economically undesirable, the use of “anytime” algorithms, which can return approximate results when interrupted, is particularly beneficial, since they offer a natural way to trade computational power for result accuracy. | ||
However, modern systems typically need to solve multiple problems simultaneously. E.g. in order to find high correlations in a dataset, one needs to examine each pair of variables. This is challenging, in particular if the number of variables is large and the data evolves dynamically. | However, modern systems typically need to solve multiple problems simultaneously. E.g. in order to find high correlations in a dataset, one needs to examine each pair of variables. This is challenging, in particular if the number of variables is large and the data evolves dynamically. | ||
Version vom 4. November 2019, 07:21 Uhr
| Vortragende(r) | Marco Heyden | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Edouard Fouché | |
| Termin | [[Institutsseminar/2019-11-22 Zusatztermin|]] | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Modern applications typically need to find solutions to complex problems under limited time and resources. In settings, in which the exact computation of indicators can either be infeasible or economically undesirable, the use of “anytime” algorithms, which can return approximate results when interrupted, is particularly beneficial, since they offer a natural way to trade computational power for result accuracy.
However, modern systems typically need to solve multiple problems simultaneously. E.g. in order to find high correlations in a dataset, one needs to examine each pair of variables. This is challenging, in particular if the number of variables is large and the data evolves dynamically. This thesis focuses on the following question: How should one distribute resources at anytime, in order to maximize the overall quality of multiple targets? First, we define the problem, considering various notions of quality and user requirements. Second, we propose a set of strategies to tackle this problem. Finally, we evaluate our strategies via extensive experiments. | |