The goal of Machine Learning to automatically learn from data, extract knowledge and to
make decisions without any human intervention. Such automatic (aML) approaches show …

Computational and storage costs of resolution of large sparse linear systems Ax= b can be
performed by reducing the bandwidth of A. Bandwidth reduction consists of carrying out …

” Advances in Bio-inspired Combinatorial Optimization Problems” illustrates several recent
bio-inspired efficient algorithms for solving NP-hard problems. Theoretical bio-inspired …

This paper considers the bandwidth reduction problem for large-scale matrices in serial
computations. A heuristic for bandwidth reduction reorders the rows and columns of a given …

The iterated greedy metaheuristic generates a sequence of solutions by iterating over a
constructive heuristic using destruction and construction phases. In the last few years, it has …

The bandwidth, average bandwidth, envelope, profile and antibandwidth of the matrices
have been the subjects of study for at least 45 years. These problems have generated …

This paper studies the bandwidth reduction problem for large-scale sparse matrices in serial
computations. A heuristic for bandwidth reduction reorders the rows and columns of a given …

The matrix bandwidth minimization problem (MBMP) consists in finding a permutation of the
lines and columns of a given sparse matrix in order to keep the non-zero elements in a band …

The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of
the rows and the columns of a square matrix such that the nonzero entries are collected …

This paper addresses the bandwidth reduction problem in graphs, which is relevant in
several applications, such as reducing memory consumption and computational cost in …