Abstract
This paper deals with the relationships between the concepts of linear dependence and independence of vectors, and the bideterminant of a matrix including the links between the bideterminant and rank of a square matrix in semilinear spaces of \(n\)-dimensional vectors over commutative zerosumfree semirings. First, it discusses some properties of bideterminant of a matrix, then introduces the concepts of semi-linear dependence and strong linear independence of a set of vectors, respectively, and gives a necessary and sufficient condition that the bideterminant of a matrix is equal to \(0\). In the end, it shows a necessary and sufficient condition that the rank of an \(n\)-square matrix is equal to \(n\).
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Notes
Some authors defined a linear dependence of vectors in \(\mathcal {V}_n\) as that: vectors \(\mathbf a _1\),\(\ldots , \mathbf a _n\), \( n\geqslant 2\), are linearly dependent if and only if there exist two nonempty disjoint subsets of indices \(J_{1}\subset \underline{n}\) and \(J_{2}\subset \underline{n}\) together with \(0\ne \lambda _{i}\in L, i\in J_{1}\cup J_{2}\), such that \(\sum _{j\in J_{1}}\lambda _{j}{\mathbf{a}}_j=\sum _{j\in J_{2}}\lambda _{j}{\mathbf{a}}_j\) (see e.g. Cechlárová and Plávka 1996; Golan 1999; Gondran and Minoux 2008). This definition means that a set of vectors is linearly dependent if and only if it is either linearly dependent in the sense of Definition 2.5 or semi-linearly dependent in the sense of Definition 4.1.
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The authors thank the referees for their valuable comments and suggestions.
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Communicated by L. Spada.
Supported by National Natural Science Foundation of China (No. 11171242), Doctoral Fund of Ministry of Education of China (No. 20105134110002), Sichuan Youth Fund (No. 2011JQ0055) and Sichuan Education Department Scientific Research Fund (No. 13ZB0165).
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Wang, Xp., Shu, Qy. The bideterminants of matrices over semirings. Soft Comput 18, 729–742 (2014). https://doi.org/10.1007/s00500-013-1163-y
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DOI: https://doi.org/10.1007/s00500-013-1163-y