Improved Maximum Likelihood Decoding using sparse Parity-Check Matrices
Technische Universität Kaiserslautern –
Maximum-likelihood decoding is an important and powerful tool in communications to obtain the optimal performance of a channel code.
Unfortunately, simulating the maximum-likelihood performance of a code is a hard problem whose complexity grows exponentially with the blocklength of the code. In order to optimize the performance, we minimize the number of ones in the underlying parity-check matrix, formulate it as an integer program and give a heuristic algorithm to solve it. Using these minimized matrices, we significantly reduce the runtime of several ML decoders for several codes, resulting in speedups of up to 81% compared to the original matrices.
Date: 2018-Oct-10 Time: 11:30:00 Room: 336
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Workshop “Metabolism and mathematical models: Two for a tango” – 2nd Edition
Title: Workshop Metabolism and mathematical models: Two for a tango – 2nd Edition
Dates: October 25-26, 2022
Location: This workshop will be held in a virtual way
The topic of this workshop is metabolism in general, with a special focus, although not exclusive, on parasitology. Besides an exploration of the biological, biochemical and biomedical aspects, the workshop will also aim at presenting some of the mathematical modelling, algorithmic theory and software development that have become crucial to explore such aspects.
This workshop is being organised in the context of two projects, both with the Inria European Team Erable. One of the projects involves a partnership with the University of São Paulo (USP), in São Paulo, Brazil, more specifically the Institute of Mathematics and Statistics (IME) and the Institute of Biomedical Sciences – Inria Associated Team Capoeira – and the other involves the Inesc-ID/IST in Portugal, ETH in Zürich and EMBL in Heidelberg – H2020 Twinning Project Olissipo.
The workshop is open to all members of these two projects but also, importantly, to the community in general.
The program and more details are available here.