What is the difference between matrix theory and linear algebra? Hi,
Currently, I'm taking matrix theory, and our textbook is Strang's Linear Algebra. Besides matrix theory, which all engineers must take, there exists linear algebra I and II for math majors. What is the difference,if any, between matrix theory and linear algebra?
Thanks!
 A: A counter-quotation to the one from Dieudonné:

We  share a philosophy about linear algebra: we think basis-free, we write basis-free, but when the chips are down we close the office door and compute with matrices like fury.

(Irving Kaplansky, writing of himself and Paul Halmos)
A: I'm with Jon.  Matrices don't always appear as linear transformations.  Yes, you can look at them as linear transformations, but there are times when it's better not to and study them for their own right.  Jon already gave one example.  Another example is the theory of positive (semi)definite matrices.  They appear naturally as covariance matrices of random vectors.  The notions like schur complements appear naturally in a course in matrix theory, but probably not in linear algebra.
A: Let me elaborate a little on what Steve Huntsman is talking about.  A matrix is just a list of numbers, and you're allowed to add and multiply matrices by combining those numbers in a certain way.  When you talk about matrices, you're allowed to talk about things like the entry in the 3rd row and 4th column, and so forth.  In this setting, matrices are useful for representing things like transition probabilities in a Markov chain, where each entry indicates the probability of transitioning from one state to another.  You can do lots of interesting numerical things with matrices, and these interesting numerical things are very important because matrices show up a lot in engineering and the sciences.
In linear algebra, however, you instead talk about linear transformations, which are not (I cannot emphasize this enough) a list of numbers, although sometimes it is convenient to use a particular matrix to write down a linear transformation.  The difference between a linear transformation and a matrix is not easy to grasp the first time you see it, and most people would be fine with conflating the two points of view.  However, when you're given a linear transformation, you're not allowed to ask for things like the entry in its 3rd row and 4th column because questions like these depend on a choice of basis.  Instead, you're only allowed to ask for things that don't depend on the basis, such as the rank, the trace, the determinant, or the set of eigenvalues.  This point of view may seem unnecessarily restrictive, but it is fundamental to a deeper understanding of pure mathematics.
A: Let me quote without further comment from Dieudonné's "Foundations of Modern Analysis, Vol. 1".

There is hardly any theory which is more elementary [than linear algebra], in spite of the fact that generations of professors and textbook writers have obscured its simplicity by preposterous calculations with matrices. 

A: My opinion: matrix theory mostly deals with matrix of a paticular kind , or a few relevant ones. But linear algebra cares about the general, underlying structrue.  
A: The difference is that in matrix theory you have chosen a particular basis.
A: Although some years ago I would have agreed with the above comments about the relationship between Linear Algebra and Matrix Theory, I DO NOT agree any more!  
See, for example Bhatia's "Matrix Analysis" GTM book.  For example, doubly-(sub)stochastic matrices arise naturally in the classification of unitarily-invariant norms.  They also naturally appear in the study of quantum entanglement, which really has nothing to do with a basis.  (In both instances, all sorts of NONarbitrary bases come into play, mainly after the spectral theorem gets applied.)
Doubly-stochastic matrices turn out to be useful to give concise proofs of basis-independent inequalities, such as the non-commutative Holder inequality:
tr |AB| $\le$ $||A||_p$ $||B||_q$
with 1/p+1/q=1, $|A|=(A^*A)^{1/2}$, and $||A||_p = (tr |A|^p)^{1/p}$
A: Matrix theory is the specialization of linear algebra to the case of finite dimensional vector spaces and doing explicit manipulations after fixing a basis. More precisely: The algebra of $n \times n$ matrices with coefficients in a field $F$ is isomorphic to the algebra of $F$-linear homomorphisms from an $n$-dimensional vector space $V$ over $F$, to itself. And the choice of such an isomorphism is precisely the choice of a basis for $V$. 
Sometimes you need concrete computations for which you use the matrix viewpoint. But for conceptual understanding, application to wider contexts and for overall mathematical elegance, the abstract approach of vector spaces and linear transformations is better.
In this second approach you  can take over linear algebra to more general settings such as modules over rings(PIDs for instance), functional analysis, homological algebra, representation theory, etc.. All these topics have linear algebra at their heart, or, rather, "is" indeed linear algebra..
