Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximating the Permanent by Sampling from Adaptive Partitions   Computing the permanent of a non-negative matrix is a core problem with
practical applications ranging from target tracking to statistical
thermodynamics. However, this problem is also #P-complete, which leaves little
hope for finding an exact solution that can be computed efficiently. While the
problem admits a fully polynomial randomized approximation scheme, this method
has seen little use because it is both inefficient in practice and difficult to
implement. We present AdaPart, a simple and efficient method for drawing exact
samples from an unnormalized distribution. Using AdaPart, we show how to
construct tight bounds on the permanent which hold with high probability, with
guaranteed polynomial runtime for dense matrices. We find that AdaPart can
provide empirical speedups exceeding 25x over prior sampling methods on
matrices that are challenging for variational based approaches. Finally, in the
context of multi-target tracking, exact sampling from the distribution defined
by the matrix permanent allows us to use the optimal proposal distribution
during particle filtering. Using AdaPart, we show that this leads to improved
tracking performance using an order of magnitude fewer samples.

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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: econ