Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Learning Monte Carlo Method in Fermion Systems   We develop the self-learning Monte Carlo (SLMC) method, a general-purpose
numerical method recently introduced to simulate many-body systems, for
studying interacting fermion systems. Our method uses a highly-efficient update
algorithm, which we design and dub "cumulative update", to generate new
candidate configurations in the Markov chain based on a self-learned bosonic
effective model. From general analysis and numerical study of the double
exchange model as an example, we find the SLMC with cumulative update
drastically reduces the computational cost of the simulation, while remaining
statistically exact. Remarkably, its computational complexity is far less than
the conventional algorithm with local updates.

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The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
1
The categories of this paper are: cs
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The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: acc-phys