In some literature on linear algebra determinants play a critical role and are emphasized in the earlier chapters. (See books by Anton & Rorres, and Lay). However in other literature it is totally ignored until the latter chapters. (See Gilbert Strang). How much importance should we give the topic of determinants . I tend to use it to find linear independence of vectors and might extend this to finding the inverse but I think Gauss Jordan and LU might be easier for inverse. Does it have any other uses in Linear Algebra. Are there areas where determinants are used and have a real impact? Are there any real life applications of determinants? Is there a really good motivating example or explanation which will hook students into this topic? Sorry but on a linear algebra where should determinants be placed? Like I sain in my comment - in some literature it is at the beginning whilst in others it is bolted on at the end. I like the idea of checkiing if vectors are independent by using determinants so think they should be placed before independence of vectors. What do you think? If you teach a linear algebra course where do you place this topic.
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You are witnessing a shift in emphasis away from determinants. This does not mean they are unimportant; on the contrary, they are quite important – think of the change of variable formula in multiple integrals, for instance – but by introducing them too early in linear algebra courses, and spending too much time on their properties, we have encouraged students to use determinants where their use is not appropriate, such as in solving linear systems. You will find the most extreme cases in old linear algebra texts, introducing determinants by their closed formula, which requires on the order of $n!$ operations to compute an $n\times n$ determinant.
There are several ways to motivate the determinant but I can think of no more interesting approach than the essential feature which characterizes it: Every multilinear alternating form defined on the product $(R^n)^n := R^n \times \cdots \times R^n$ is a scalar multiple of the determinant function. In fact, the determinant function is the unique multilinear alternating function when evaluated on the Identity matrix, treating columns as vectors, yields unity. From this characterization, one can formally derive the admittedly horrendous-looking combinatorial formula. For illustration purposes, for lower dimensions, say n=2 or n =3, it is tractable to compute the determinant explicitly using this characterization. This is preferable in my opinion than stating the formula and showing that said formula satisfies properties A-Z.
The determinant shows up in a surprising number of places and for this reason alone is important. I think though it might be an excellent place to introduce the idea that mathematical objects are not necessarily important for what they are but, rather, the properties they satisfy (e.g., universal properties). If you think linear algebra is a good place to introduce the basic concepts of abstract mathematics I think the determinant does this quite nicely. The description of the determinant above is elementary enough that anyone taking linear algebra should be able to understand it and proving subsequent theorems about determinants goes significantly smoother with this characterization as opposed to trying to pound it out with the combinatorial formula.
This is quite an informal answer.
Determinants basically help to describe the nature of solutions of linear equations. The determinant of a real matrix is just some real number, telling you about the invertibility of the matrix and hence telling you things about linear equations wrapped up in the matrix.
The determinant being non-zero is equivalent to the matrix being invertible, which is equivalent to the corresponding sets of linear equations having EXACTLY one solution.
The determinant being zero means the matrix is NOT invertible. In this case the corresponding sets of linear equations can either have infinitely many solutions or none at all (depending on the numbers on the RHS).
So really the determinant is useful anywhere that linear equations crop up. For example, when checking linear independence, this is the same as demanding the existence of a UNIQUE solution to a set of linear equations (i.e. the zero vector solution). This is the same as the matrix determinant being non-zero as discussed above. Linear dependence must therefore be the same as the determinant being zero (so that there may be non-zero solutions to the equations, i.e. so that some of the vectors really can be made to add up non-trivially to give one of the others).