1,095 research outputs found
Algorithms for the minimum sum coloring problem: a review
The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex
coloring problem which has a number of AI related applications. Due to its
theoretical and practical relevance, MSCP attracts increasing attention. The
only existing review on the problem dates back to 2004 and mainly covers the
history of MSCP and theoretical developments on specific graphs. In recent
years, the field has witnessed significant progresses on approximation
algorithms and practical solution algorithms. The purpose of this review is to
provide a comprehensive inspection of the most recent and representative MSCP
algorithms. To be informative, we identify the general framework followed by
practical solution algorithms and the key ingredients that make them
successful. By classifying the main search strategies and putting forward the
critical elements of the reviewed methods, we wish to encourage future
development of more powerful methods and motivate new applications
A note on a sports league scheduling problem
Sports league scheduling is a difficult task in the general case. In this
short note, we report two improvements to an existing enumerative search
algorithm for a NP-hard sports league scheduling problem known as "prob026" in
CSPLib. These improvements are based on additional rules to constraint and
accelerate the enumeration process. The proposed approach is able to find a
solution (schedule) for all prob026 instances for a number T of teams ranging
from 12 to 70, including several T values for which a solution is reported for
the first time.Comment: 9 page
Reinforcement learning based local search for grouping problems: A case study on graph coloring
Grouping problems aim to partition a set of items into multiple mutually
disjoint subsets according to some specific criterion and constraints. Grouping
problems cover a large class of important combinatorial optimization problems
that are generally computationally difficult. In this paper, we propose a
general solution approach for grouping problems, i.e., reinforcement learning
based local search (RLS), which combines reinforcement learning techniques with
descent-based local search. The viability of the proposed approach is verified
on a well-known representative grouping problem (graph coloring) where a very
simple descent-based coloring algorithm is applied. Experimental studies on
popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves
competitive performances compared to a number of well-known coloring
algorithms
Towards Effective Exact Algorithms for the Maximum Balanced Biclique Problem
The Maximum Balanced Biclique Problem (MBBP) is a prominent model with
numerous applications. Yet, the problem is NP-hard and thus computationally
challenging. We propose novel ideas for designing effective exact algorithms
for MBBP. Firstly, we introduce an Upper Bound Propagation procedure to
pre-compute an upper bound involving each vertex. Then we extend an existing
branch-and-bound algorithm by integrating the pre-computed upper bounds. We
also present a set of new valid inequalities induced from the upper bounds to
tighten an existing mathematical formulation for MBBP. Lastly, we investigate
another exact algorithm scheme which enumerates a subset of balanced bicliques
based on our upper bounds. Experiments show that compared to existing
approaches, the proposed algorithms and formulations are more efficient in
solving a set of random graphs and large real-life instances
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