Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth
  Nonconvex Statistical Learning   Modern statistical applications often involve minimizing an objective
function that may be nonsmooth and/or nonconvex. This paper focuses on a broad
Bregman-surrogate algorithm framework including the local linear approximation,
mirror descent, iterative thresholding, DC programming and many others as
particular instances. The recharacterization via generalized Bregman functions
enables us to construct suitable error measures and establish global
convergence rates for nonconvex and nonsmooth objectives in possibly high
dimensions. For sparse learning problems with a composite objective, under some
regularity conditions, the obtained estimators as the surrogate's fixed points,
though not necessarily local minimizers, enjoy provable statistical guarantees,
and the sequence of iterates can be shown to approach the statistical truth
within the desired accuracy geometrically fast. The paper also studies how to
design adaptive momentum based accelerations without assuming convexity or
smoothness by carefully controlling stepsize and relaxation parameters.

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The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc