Resumen: For many applications, it is convenient to have good upper bounds for the norm of the inverse of a given matrix. In this paper, we obtain such bounds when A is a Nekrasov matrix, by means of a scaling matrix transforming A into a strictly diagonally dominant matrix. Numerical examples and comparisons with other bounds are included. The scaling matrices are also used to derive new error bounds for the linear complementarity problems when the involved matrix is a Nekrasov matrix. These error bounds can improve considerably other previous bounds. Idioma: Inglés DOI: 10.1016/j.amc.2019.04.027 Año: 2019 Publicado en: Applied Mathematics and Computation 358 (2019), 119-127 ISSN: 0096-3003 Factor impacto JCR: 3.472 (2019) Categ. JCR: MATHEMATICS, APPLIED rank: 7 / 260 = 0.027 (2019) - Q1 - T1 Factor impacto SCIMAGO: 0.969 - Computational Mathematics (Q1) - Applied Mathematics (Q1)