Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Doubly Stochastic Primal-Dual Coordinate Method for Bilinear
  Saddle-Point Problem   We propose a doubly stochastic primal-dual coordinate optimization algorithm
for empirical risk minimization, which can be formulated as a bilinear
saddle-point problem. In each iteration, our method randomly samples a block of
coordinates of the primal and dual solutions to update. The linear convergence
of our method could be established in terms of 1) the distance from the current
iterate to the optimal solution and 2) the primal-dual objective gap. We show
that the proposed method has a lower overall complexity than existing
coordinate methods when either the data matrix has a factorized structure or
the proximal mapping on each block is computationally expensive, e.g.,
involving an eigenvalue decomposition. The efficiency of the proposed method is
confirmed by empirical studies on several real applications, such as the
multi-task large margin nearest neighbor problem.

25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con