Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Preconditioning for Iterative Linear Equation Solvers   This paper presents a new stochastic preconditioning approach. For symmetric
diagonally-dominant M-matrices, we prove that an incomplete LDL factorization
can be obtained from random walks, and used as a preconditioner for an
iterative solver, e.g., conjugate gradient. It is argued that our factor
matrices have better quality, i.e., better accuracy-size tradeoffs, than
preconditioners produced by existing incomplete factorization methods.
Therefore the resulting preconditioned conjugate gradient (PCG) method requires
less computation than traditional PCG methods to solve a set of linear
equations with the same error tolerance, and the advantage increases for larger
and denser sets of linear equations. These claims are verified by numerical
tests, and we provide techniques that can potentially extend the theory to more
general types of matrices.

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The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
3
The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
1
The categories of this paper are: cs
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol