Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Extended Newton-type Algorithm for $\ell_2$-Regularized Sparse
  Logistic Regression and Its Efficiency for Classifying Large-scale Datasets   Sparse logistic regression, as an effective tool of classification, has been
developed tremendously in recent two decades, from its origination the
$\ell_1$-regularized version to the sparsity constrained models. This paper is
carried out on the sparsity constrained logistic regression by the Newton
method. We begin with establishing its first-order optimality condition
associated with a $\tau$-stationary point. This point can be equivalently
interpreted as a system of equations which is then efficiently solved by the
Newton method. The method has a considerably low computational complexity and
enjoys global and quadratic convergence properties. Numerical experiments on
random and real data demonstrate its superior performance when against seven
state-of-the-art solvers.

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: cs
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-lat