Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Rapid Learning for Integer Programs   Conflict learning algorithms are an important component of modern MIP and CP
solvers. But strong conflict information is typically gained by depth-first
search. While this is the natural mode for CP solving, it is not for MIP
solving. Rapid Learning is a hybrid CP/MIP approach where CP search is applied
at the root to learn information to support the remaining MIP solve. This has
been demonstrated to be beneficial for binary programs. In this paper, we
extend the idea of Rapid Learning to integer programs, where not all variables
are restricted to the domain {0,1}, and rather than just running a rapid CP
search at the root, we will apply it repeatedly at local search nodes within
the MIP search tree. To do so efficiently, we present six heuristic criteria to
predict the chance for local \rapidlearning to be successful. Our computational
experiments indicate that our extended Rapid Learning algorithm significantly
speeds up MIP search and is particularly beneficial on highly dual degenerate
problems.

8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph