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Diffstat (limited to 'sampling_alg_lac2020/LAC-20.tex')
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diff --git a/sampling_alg_lac2020/LAC-20.tex b/sampling_alg_lac2020/LAC-20.tex index fc58394..c8d0230 100644 --- a/sampling_alg_lac2020/LAC-20.tex +++ b/sampling_alg_lac2020/LAC-20.tex @@ -369,10 +369,16 @@ We already explained the core part of the sample selection algorithm. The remain Note that the worst-case complexity of evaluating the objective function is linear in the number of samples for the instrument that we are considering. However, in practice we can avoid evaluation for most samples by simply starting with the \enquote{most promising} sample and recursively evaluate the neighbors until the future possible evaluations cannot beat the currently best value. +\section{Emulation Capabilities} +\todo{Talk about which other algorithms we are a general case of, i.e., which algorithms can we emulate using the right power values and parameter settings.} + \section{Implementation} \todobent{Give a short introduction to DrumGizmo, including a link to the git repository.} \todo{Talk about the timeline, i.e., when were the releases and what is still unreleased?} + \todoandre{Talk about how the sampling algorithm was implemented. What do we need to store?} + + \todoandre{Add some of the source code to the paper?} \todoandre{Give less important implementation details, e.g., like adaptive search starting from the most promising value} |