Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-smooth Non-convex Bregman Minimization: Unification and new
  Algorithms   We propose a unifying algorithm for non-smooth non-convex optimization. The
algorithm approximates the objective function by a convex model function and
finds an approximate (Bregman) proximal point of the convex model. This
approximate minimizer of the model function yields a descent direction, along
which the next iterate is found. Complemented with an Armijo-like line search
strategy, we obtain a flexible algorithm for which we prove (subsequential)
convergence to a stationary point under weak assumptions on the growth of the
model function error. Special instances of the algorithm with a Euclidean
distance function are, for example, Gradient Descent, Forward--Backward
Splitting, ProxDescent, without the common requirement of a "Lipschitz
continuous gradient". In addition, we consider a broad class of Bregman
distance functions (generated by Legendre functions) replacing the Euclidean
distance. The algorithm has a wide range of applications including many linear
and non-linear inverse problems in signal/image processing and machine
learning.

21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex