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What is Linear Algebra?

We begin our study of linear algebra with an introduction and a motivational example. Is the equation $x^2 + xy +\tan(y^3)=0$ linear or not? Why or why not?

What is Linear Algebra?

We begin our study of linear algebra with an introduction and a motivational example.

  1. Is the equation $x^2 + xy +\tan(y^3)=0$ linear or not? Why or why not?
  2. Find all solutions to the system of two linear equations $2x+3y=-8$, $x-y=6$.
  3. Describe how the production manager might explain the importance of the procedures described in the trail mix application ().

Linear + Algebra

The subject of linear algebra can be partially explained by the meaning of the two terms comprising the title. Linear is a term you will appreciate better at the end of this course, and indeed, attaining this appreciation could be taken as one of the primary goals of this course. However for now, you can understand it to mean anything that is straight or flat. For example in the $xy$-plane you might be accustomed to describing straight lines (is there any other kind?) as the set of solutions to an equation of the form $y = mx + b$, where the slope $m$ and the $y$-intercept $b$ are constants that together describe the line. If you have studied multivariate calculus, then you will have encountered planes. Living in three dimensions, with coordinates described by triples $(x,\,y,\,z)$, they can be described as the set of solutions to equations of the form $ax+by+cz=d$, where $a,\,b,\,c,\,d$ are constants that together determine the plane. While we might describe planes as flat, lines in three dimensions might be described as straight. From a multivariate calculus course you will recall that lines are sets of points described by equations such as $x=3t-4$, $y=-7t+2$, $z=9t$, where $t$ is a parameter that can take on any value.

Another view of this notion of flatness is to recognize that the sets of points just described are solutions to equations of a relatively simple form. These equations involve addition and multiplication only. We will have a need for subtraction, and occasionally we will divide, but mostly you can describe linear equations as involving only addition and multiplication. Here are some examples of typical equations we will see in the next few sections: 2x+3y-4z&=13 & 4x_1+5x_2-x_3+x_4+x_5&=0 & 9a-2b+7c+2d&=-7

What we will not see are equations like: xy + 5yz&=13 & x_1 + x_2^3/x_4 - x_3x_4x_5^2&=0 & \tan(ab)+\log(c-d)&=-7 The exception will be that we will on occasion need to take a square root.

The brief discussion above about lines and planes suggests that linear algebra has an inherently geometric nature, and this is true. Examples in two and three dimensions can be used to provide valuable insight into important concepts of this course. However, much of the power of linear algebra will be the ability to work with flat or straight objects in higher dimensions, without concerning ourselves with visualizing the situation. While much of our intuition will come from examples in two and three dimensions, we will maintain an algebraic approach to the subject, with the geometry being secondary. Others may wish to switch this emphasis around, and that can lead to a very fruitful and beneficial course, but here and now we are laying our bias bare.

Condensed — the full section is in Beezer, A First Course in Linear Algebra.

An Application

We conclude this section with a rather involved example that will highlight some of the power and techniques of linear algebra. Work through all of the details with pencil and paper, until you believe all the assertions made. However, in this introductory example, do not concern yourself with how some of the results are obtained or how you might be expected to solve a similar problem. We will come back to this example later and expose some of the techniques used and properties exploited. For now, use your background in mathematics to convince yourself that everything said here really is correct.

This example is taken from a field of mathematics variously known by names such as operations research, systems science, or management science. More specifically, this is a prototypical example of problems that are solved by the techniques of linear programming.

There is a lot going on under the hood in this example. The heart of the matter is the solution to systems of linear equations, which is the topic of the next few sections, and a recurrent theme throughout this course. We will return to this example on several occasions to reveal some of the reasons for its behavior.

Condensed — the full section is in Beezer, A First Course in Linear Algebra.

Symbols used here

\log_b x,\ \ln x
logarithm, natural log
The exponent b must be raised to for x; ln uses base e.
\sin,\ \cos,\ \tan
sine, cosine, tangent
Ratios of sides in a right triangle; coordinates on the unit circle.
\mathbf{v},\ \vec{v}
vector
A quantity with magnitude and direction; a column of numbers.
A = \begin{pmatrix} a & b \\ c & d \end{pmatrix}
matrix
A rectangular array of numbers; a linear map.
\det A,\ |A|
determinant
Scaling factor of area/volume under A; zero means singular.
A^{-1},\ A^{T}
inverse, transpose
The matrix that undoes A; A with rows and columns swapped.
\lambda
lambda (eigenvalue)
The factor by which an eigenvector is stretched: Av = λv.
\mathbf{u} \cdot \mathbf{v},\ \|\mathbf{v}\|
dot product, norm
Σ u_i v_i; the length of v, √(v·v).

Questions people ask

What does a determinant mean geometrically?

It is the factor by which the matrix scales area (2×2) or volume (3×3), with a negative sign if orientation flips. Zero means the matrix flattens space and cannot be undone.

What is an eigenvector?

A direction the matrix does not turn — it only stretches it by the eigenvalue. Along eigenvectors a complicated matrix acts like multiplication by a number.

Why is matrix multiplication not commutative?

Because a matrix is a transformation and AB means "do B, then A". Rotating then reflecting is not the same as reflecting then rotating.

Jaribu kufanya mambo yako mwenyewe

Parts of this page are adapted from Beezer, A First Course in Linear Algebra (GFDL 1.2). Condensed and re-explained here; errors are ours.

Mengi zaidi katika Linear Algebra