Is linear algebra laying the foundation for something important?
I'm majoring in mathematics and currently enrolled in Linear Algebra. It's very different, but I like it (I think). My question is this: What doors does this course open? (I saw a post about Linear Algebra being the foundation for Applied Mathematics -- but I like doing math for the sake of math, not so much the applications.) Is this a stand-alone class, or will the new things I'm learning come into play later on?
Solution 1:
Linear Algebra is indeed one of the foundations of modern mathematics. There are a lot of things which use the language and tools developed in linear algebra:
Multidimensional Calculus, i.e. Analysis for functions of many variables, i.e. vectors (for example, the first derivative becomes a matrix)
Differential Geometry, which investigates structures which look locally like a vector space and functions on this.
Functional Analysis, which is essentially linear algebra on infinite-dimensional vector spaces which is the foundation of quantum mechanics.
Multivariate Statistics, which investigates vectors whose entries are random. For instance, to describe the relation between two components of such random vector, one can calculate the correlation matrix. Furthermore, one can apply a technique called singular value decomposition (which is close to calculating the eigenvalues of a matrix) to find which components are having a main influence on the data.
Tagging on to the Multivariate Statistics and multidimensional calculus, there are a number of Machine Learning techniques which require you to find a (local) minimum of a nonlinear function (the likelihood function), for example for neural nets. Generally speaking, on can try to find the parameters which maximize the likelihood, e.g. by applying the gradient descent method, which uses vectors arithmetic. (Thanks, frogeyedpeas!)
Control Theory and Dynamical Systems theory is mainly concerned with differential equations where matrices are factors in front of the functions. It helps tremendously to know the eigenvalues of the matrices involved to predict how the system will behave and also how to change the matrices in front to make sure the system behaves like you want it to - in Control Theory, this is related to the poles and zeros of the transfer function, but in essence it's all about placing eigenvalues at the right place. This is not only relevant for mechanical systems, but also for electric engineering. (Thanks, Look behind you!)
Optimization in general and Linear Programming in particular is closely related to multidimensional calculus, namely about finding minima (or maxima) of functions, but you can use the structure of vector spaces to simplify your problems. (Thanks, Look behind you!)
On top of that, there are a lot of applications in engineering and physics which use tools of linear algebra and the fields listed above to solve real-world problems (often calculating eigenvalues and solving differential equations).
In essence, a lot of things in the mathematical toolbox in one variable can be lifted up to the multivariable case with the help of Linear Algebra.
Edit: This list is by no means complete, these were just the topics which came to my mind at first thought. Not mentioning a particular field doesn't mean that this field irrelevant, but just that I don't feel qualified to write a paragraph about it.
Solution 2:
By now, we can roughly say that all we fully understand in Mathematics is linear.
So I guess Linear Algebra is a good topic to master.
Both in Mathematics and in Physics you tend to bring the problem to a Linear problem and then solve it with Linear Algebras techniques. This happens in Algebra with linear representations, in Functional Analysis with the study of Hilbert Spaces, in Differential Geometry with the tangent spaces, and so on almost in every field of Mathematics. Indeed I think there should be at least 3 courses of Linear Algebra (undergraduate, graduate, advanced), everytime with different insights on the subject.
Solution 3:
Excellent answers above. I would just like to add:
- Computer graphics. As soon as you scale, rotate and/or translate an object on a computer screen, that is Linear Algebra in action. Computer graphics is an academic field that depend heavily on Linear Algebra.
Solution 4:
Mathematics are all about building theorems and properties from a set of ground hypothesis.
Linear algebra is the theory around the set of properties that define vector spaces and linear mappings, and these properties are verified by a huge number of other mathematical constructions, but can also verified as first order approximations of most physical systems.
So to answer your question, if you like math for themselves, you will love Linear Algebra for its own beauty. You really start from a simple set of properties and build very interesting objects. But it is also a mandatory step for most engineering or mathematics majors, as its properties are verified by a huge number of systems.
Solution 5:
Well, let's try to imagine how to avoid linear algebra.
Anything that involves functions of $2$ or more variables, that can be differentiated, will very soon use linear operators or matrices. No geometry!
Also, no algebra or number theory or topology. Almost anything in algebra or its client subjects that involves associative (not necessarily commutative) multiplication, such as composition of functions, requires the simpler case of linear algebra as a preliminary.
That leaves calculus of one real or complex variable. There, it will still be hard to avoid linear algebra, unless you also avoid recursion relations, differential and difference equations, operators on the functions, measures, Fourier and Laplace transforms, special function and orthogonal polynomials, discrete or finite-element methods, calculus of variations. Sooner or later one meets linear-algebra concepts like eigenvalue, linear operator and quadratic form.
One could try to hide in parts of analysis that seem less algebraic. The Riemann zeta function does not look like it needs any linear algebra. But understanding it requires some algebraic number theory, and then there are all the conjectures about its relations to linear operators, and theorems that the zeros are spaced according to patterns typical of random... matrices. Fractal geometry doesn't look very algebraic, until you ask why the Mandelbrot set looks self-similar at all scales, or use iterated function systems, which are directly based on linear algebra. Probability theory is full of Markov chains, which are complicated linear algebra.
OK then. As long as one sticks to subjects that are outside of algebra, analysis and geometry, and are so qualitative that no serious algebra/analysis/geometry is used for calculating anything, then there might not be a need to ever know linear algebra! This excludes concrete pursuits like combinatorics, computer science and all of applied mathematics.
For the very few subjects that are left, it is still questionable whether anyone could really make use of them without knowing linear algebra. There are too many basic thought patterns that are learned in linear algebra, such as reasoning about higher-dimensional spaces, that have permeated the language and ideas in other parts of mathematics.