1. Introduction
The determinant of an matrix
is denoted by or , and a basic formula to compute the determinant is
where the summation is taken over all permutations of the set of integers . Furthermore, the function is defined as:
In this paper, we will present a new method to compute the determinant of
a matrix.
2. Preliminaries: The main definitions and lemmas
In matrix theory, a square matrix is called \textit{nonsingular} if and only if its determinant is nonzero. We generalize the nonsingular matrices to
doubly nonsingular:
Definition 2.1.
An matrix is doubly nonsingular if and only if is nonsingular and all matrices of adjacent terms within the are nonsingular.
Example 2.2.
Let . We have , consequently is nonsingular. Clearly, all determinants of adjacent terms are nonzero. Hence, the matrix is doubly nonsingular.
Now, we introduce a new function, which we call the \textit{star fraction}:
Definition 2.3.
Given two matrices
and such that and is doubly nonsingular. The star fraction of on is defined as:
In the next section, we will show that the star fraction is a useful function for calculating the determinant of a matrix.
Now, we shall know about the
Dodgson’s condensation of a matrix that was introduced by Charles Lutwidge Dodgson in 1866 [
1]:
Definition 2.4.
The Dodgson’s condensation of an matrix is an matrix such as such that
Henceforth the notation is denote the first Dodgson’s condensation of a matrix , and the second condensation is and so on.
Clearly a square matrix is doubly nonsingular if and only if all elements of are nonzero.
To prove the main theorem we need the following lemmas.
Lemma 2.5.[3]
We have
where .
Lemma 2.6.
[2, Theorem 1] We have
where are nonzero numbers and .
3. Main results
In the following theorem we present a new method, just to compute the determinant of a matrix.
Theorem 3.1.
Given a matrix
where is a doubly nonsingular matrix with all nonzero elements. Then
Proof.
Since , using Lemma 2.5 we have
Besides, we know that all are nonzero numbers. So, using Lemma 2.6 for all within (1), we have
where is equal to
Using Definition 2.3, we obtain
Clearly using Definition 2.4, the top part of fraction (3) is equal to , consequently (2) and (3) give
The theorem is proved.
Note that in the Theorem 3.1, if the matrix
is not doublynonsingular or if some elements of it are zero, then by adding a multiple of one
row to another row, or a multiple of one column to another column of the main
matrix , these problems can be resolved (since the determinant of the main
matrix does not change).
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Example 3.2. Let
. To obtain the , we have
4. Conclusion
We presented a new method to compute the determinant of a matrix.
In fact, this is a generalization of a simpler method for matrices which was
previously provided in [
2]. It seems to be possible to generalize this method for
matrices of order . Of course, for more generalizations, more calculations are
required.
Competing Interests
The author do not have any competing interests in the manuscript.
Acknowledgments
The author would like to thank the editor and the anonymous referee for their helpful comments.