Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Strong Convexity from Data with Primal-Dual First-Order
  Algorithms   We consider empirical risk minimization of linear predictors with convex loss
functions. Such problems can be reformulated as convex-concave saddle point
problems, and thus are well suitable for primal-dual first-order algorithms.
However, primal-dual algorithms often require explicit strongly convex
regularization in order to obtain fast linear convergence, and the required
dual proximal mapping may not admit closed-form or efficient solution. In this
paper, we develop both batch and randomized primal-dual algorithms that can
exploit strong convexity from data adaptively and are capable of achieving
linear convergence even without regularization. We also present dual-free
variants of the adaptive primal-dual algorithms that do not require computing
the dual proximal mapping, which are especially suitable for logistic
regression.

30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph