Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation
  Maximization for Nonconvex Finite-Sum Optimization   The Expectation Maximization (EM) algorithm is a key reference for inference
in latent variable models; unfortunately, its computational cost is prohibitive
in the large scale learning setting. In this paper, we propose an extension of
the Stochastic Path-Integrated Differential EstimatoR EM (SPIDER-EM) and derive
complexity bounds for this novel algorithm, designed to solve smooth nonconvex
finite-sum optimization problems. We show that it reaches the same state of the
art complexity bounds as SPIDER-EM; and provide conditions for a linear rate of
convergence. Numerical results support our findings.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph