Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973051" target="_blank" >RIV/49777513:23520/24:43973051 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ifacol.2024.08.498" target="_blank" >https://doi.org/10.1016/j.ifacol.2024.08.498</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2024.08.498" target="_blank" >10.1016/j.ifacol.2024.08.498</a>

Result language
angličtina
Original language name
Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
Original language description
This paper deals with the state estimation of stochastic systems and examines the possible employment of tensor decompositions in grid-based filtering routines, in particular, the tensor-train decomposition. The aim is to show that these techniques can lead to a massive reduction in both the computational and storage complexity of grid-based filtering algorithms without considerable tradeoffs in accuracy. This claim is supported by an algorithm descriptions and numerical illustrations.
Czech name
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Czech description
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Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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