Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-grained Search Space Classification for Hard Enumeration Variants
  of Subset Problems   We propose a simple, powerful, and flexible machine learning framework for
(i) reducing the search space of computationally difficult enumeration variants
of subset problems and (ii) augmenting existing state-of-the-art solvers with
informative cues arising from the input distribution. We instantiate our
framework for the problem of listing all maximum cliques in a graph, a central
problem in network analysis, data mining, and computational biology. We
demonstrate the practicality of our approach on real-world networks with
millions of vertices and edges by not only retaining all optimal solutions, but
also aggressively pruning the input instance size resulting in several fold
speedups of state-of-the-art algorithms. Finally, we explore the limits of
scalability and robustness of our proposed framework, suggesting that
supervised learning is viable for tackling NP-hard problems in practice.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat