## Determinants

The determinant of a square matrix can be calculated easily by multiplying the eigenvalues, for example those produced by the R function eigen(). However, until now there does not seem to be an R function that produces the formula for calculating the determinant, as a function of the elements of the matrix. This formula is called the symbolic representation of the determinant. In other words, for the 2x2 matrix $$V_{2}$$ we want a function to return $$v_{11}v_{22} - v_{12}v_{21}$$ for an unstructured, general 2x2 matrix and $$v_{11}v_{22} - v^2_{12}$$ for a symmetric 2x2 matrix, and not the numeric values for a particular numeric matrix.

The determinant of a pxp square matrix is an algebraic sum of p! (p-factorial) terms, half of which have a coefficient of +1 and half of which have a coefficient of -1. Each of the terms is a product of exactly p of the elements of the matrix. Each product contains an element from each row and one from each column. In the general case, when the matrix is not further structured, each of the matrix elements is linear. There are no replications of the elements of the matrix within a product and there are no replications of any of the products in the sum that makes up the determinant.

In addition to the general case, we are interested in the case in which the matrix is symmetric. Here, some of the matrix elements in a product can be duplicated because both $$v_{ij}$$ and $$v_{ji}$$ are present in that product. It is also possible that some of the product terms are duplicated when $$v_{ij}$$ appears in one term and $$v_{ji}$$ appears in another term along with the same collection of the other p-2 elements in each term.

## Objective

Our objective is to provide functions that will return the most efficient symbolic representation of the determinant of any pxp matrix, whether general or symmetric. We have no interest here in developing a method of calculating the numerical value of the determinant of a square matrix. Rather, we are interested in the literal structure of determinants.

## The detguide

We first construct a guide for printing the symbolic representation of the determinant of a general pxp matrix, $$V_{p}$$. The ij-th element of $$V_{p}$$ raised to a power k, $$v^k_{ij}$$, can be represented as a row vector (i,j,k). The product of p such terms can be represented as a p x 3 matrix. The sum of p!/2 such products can be represented as matrices in p!/2 levels of a list. A determinant is the symbolic difference between two such sums.

That is, a detguide can be constructed as a list of 2 levels, each of which is a list of p!/2 levels. (A determinant always has the same number of positive terms and negative terms.) By representing the determinant as such a list, we can directly write out the symbolic representation.

For a 2x2 matrix one “negative” term ($$v_{12}v_{21}$$) is subtracted from one “positive” term ($$v_{11}v_{22}$$). The labels of “positive” and “negative” do not apply to the value of the elements; rather, it refers to the algebraic formula of the calculation: “negative” terms will receive a leading minus sign when the term is parsed/printed.

A 3x3 matrix has 9 elements. In calculating the determinant for a 3x3 matrix, each of the elements of the first row is multiplied by its 2x2 cofactor. Each cofactor, in turn, consists of a positive and a negative term so that there are a total of 3 positive products and 3 negative products.

To create the detguide, the formula of Laplace (1772) is used. The result of multiplying a matrix element and its cofactor is represented by binding the row representation of the element to the matrix of each term in the determinant of the cofactor. Attention must be paid to the sign of the element in the formula. The sign is not shown in the detguide; rather, positive products are placed into the first sub-element of the list and negative elements are placed in the second sub-element. Products receive their sign from the function that parses the detguide (see below).

For each p only the detguide for the general matrix (not for the symmetric) is stored. The effect of symmetry on the symbolic representation is effected when the detguide is parsed.

# Parsing the detguide: the symbolic representation

The textual representation of the determinant of $$V_{p}$$ is created directly from the detguide of the pxp matrix. That is, it is not necessary first to parse the determinants of matrices of smaller size within it. The most basic symbolic representation is a simple listing of each product of the determinant with a leading + or – sign. It is useful to order these so as to make it easier to identify patterns of elements or of products.

# Challenges

Detguides and their symbolic representations can become extremely large as p gets large. Detguides are produced iteratively, so each must be stored in order to produce the next larger one quickly. Accordingly, methods must be developed to limit the size of detguides, and they are stored by functions in this package in a permanent file of the user’s choosing and not in the R workspace.

After a large detguide is created, it is difficult to know if it is functionally correct. That is, would its application to a numeric matrix of the same size yield a value equal to the product of its eigenvalues calculated in the usual way? See the description of the function, confirm.det() and the README file.

For symmetric matrices, as p increases the product terms become more complicated. Instead of strictly linear elements, some of these may be quadratic. It is not possible for any diagonal element of the matrix to appear more than once in any product, so diagonal terms can never be squared in any product. If p=3, there can be only 1 quadratic element in a product. If p=4, there can be none, 1, or 2 quadratic terms. In general, there can be none, 1, 2, …, floor(p/2) quadratic elements. The symbolic representation would treat each of these groups as separate forms.

# Taking on the challenges

We note from the formula for the determinant provided by Leibniz and cited in Wikipedia - Leibniz Formula for Determinants, that the symbolic representation can be written directly without the need to employ determinants of the minors of a given row. This saves considerable storage space. Moreover, there are relationships between the products with leading + signs and those with leading - signs that allows us to save only the minidetguide, the part of the detguide with leading + sign. Finally, by subdividing the minidetguide into products having the same leading matrix element and storing them accordingly, we can omit both the leading element and the notation indicating the row of each element. In the Leibniz formula, the row is always 1,2,3,…,p for each element of a product.

What we have done is to reduce the storage requirement at the cost of greater processing time for parsing the minidetguide into the symbolic representation of the determinant. In fact, we could eliminate the storage requirement entirely by writing a function that creates the minidetguide and parses it in one step. For large p, it seems that such a function would take a very long time to run even though it does not involve determinants of lower order.

# Discussion

In this version of the package, we are aware of only a few possible forms and that some products can appear twice in the detguide of symmetric matrices. As we gain experience with larger and larger values of p, it is possible (for example) that products could appear even 4 times in a symbolic representation (doubling the presence of products that appear twice for smaller p). We would expect to modify the *parsedetguide( )* or *parsemini()* function to accommodate this. These functions include a coefficient to account for multiple product terms. For such terms, the original line number is maintained (with the duplicated lines skipped).

Parsing the detguide is the end result of these calculations. New explorations into the structure of determinants should be conducted on the detguide, not on the parsed, textual version.