Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Learnable Gradient Descent Type Algorithm for Non-convex
  Non-smooth Inverse Problems   Optimization algorithms for solving nonconvex inverse problem have attracted
significant interests recently. However, existing methods require the nonconvex
regularization to be smooth or simple to ensure convergence. In this paper, we
propose a novel gradient descent type algorithm, by leveraging the idea of
residual learning and Nesterov's smoothing technique, to solve inverse problems
consisting of general nonconvex and nonsmooth regularization with provable
convergence. Moreover, we develop a neural network architecture intimating this
algorithm to learn the nonlinear sparsity transformation adaptively from
training data, which also inherits the convergence to accommodate the general
nonconvex structure of this learned transformation. Numerical results
demonstrate that the proposed network outperforms the state-of-the-art methods
on a variety of different image reconstruction problems in terms of efficiency
and accuracy.

9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th