Definition. Let ${\bf u}=(u_1,u_2,u_3)$ and ${\bf v}=(v_1,v_2,v_3)$. Then the cross product ${\bf u}\times {\bf v}$ is defined by \begin{equation}\label{eq:crossprod}{\bf u}\times{\bf v}=(u_2v_3-u_3v_2,u_3v_1-u_1v_3,u_1v_2-u_2v_1)\end{equation} The cross product can be also written as the determinant \begin{equation}\label{eq:crossprod2}{\bf u}\times{\bf v}=\begin{vmatrix}{\bf i} & {\bf j} & {\bf k}\\u_1 & u_2 & u_3\\v_1 & v_2 & v_3\end{vmatrix}\end{equation} One can calculate the determinant as shown in Figure 1. You multiply three entries along each indicated arrow. When you multiply three entries along each red arrow, you also multiply by −1. This is called the Rule of Sarrus named after a French mathematician Pierre Frédéric Sarrus.
Figure 1. The Cross Product
Unlike the dot product, the outcome of the dot product is a vector. Also unlike the dot product, the cross product is anticommutative i.e. $${\bf u}\times{\bf v}=-{\bf v}\times{\bf u}$$ Furthermore, ${\bf u}\times{\bf v}$ is orthogonal to both ${\bf u}$ and ${\bf v}$. This can be seen by showing that $$({\bf u}\times{\bf v})\cdot{\bf u}=({\bf u}\times{\bf v})\cdot{\bf v}=0$$ The cross product tells us about the orientation of the plane containing two vectors ${\bf u}$ and ${\bf v}$ as shown in Figure 2.
Figure 2. The orientations
Theorem. If $\theta$ is the angle between ${\bf u}$ and ${\bf v}$ ($0\leq\theta\leq\pi$), then \begin{equation}\label{eq:crossprod3}|{\bf u}\times{\bf v}|=|{\bf u}||{\bf v}|\sin\theta\end{equation}
Proof. It would require some work with algebra but one can show that $$|{\bf u}\times{\bf v}|^2=|{\bf u}|^2|{\bf v}|^2-({\bf u}\cdot{\bf v})^2$$ This, along with ${\bf u}\cdot{\bf v}=|{\bf u}||{\bf v}|\cos\theta$, will lead to \eqref{eq:crossprod3}.
From \eqref{eq:crossprod3}, we can easily see that two nonzero vectors ${\bf u}$ and ${\bf v}$ are parallel if and only if ${\bf u}\times{\bf v}=0$.
The standard basis vectors ${\bf i}$, ${\bf j}$, ${\bf k}$ satisfy the following cross products: $${\bf i}\times{\bf j}={\bf k},\ {\bf j}\times{\bf k}={\bf i},\ {\bf k}\times{\bf i}={\bf j}$$
The following theorem summarizes the properties of the cross product.
Theorem. Let ${\bf u}$, ${\bf v}$, and ${\bf w}$ be vectors and $c$ a scalar. Then
The products in 5 and 6 are called, respectively, a scalar triple product and a vector triple product.
From Figure 3, we see that \begin{equation}\label{eq:areaparallelogram}|{\bf u}\times{\bf v}|\end{equation} is equal to the area of the parallelogram determined by ${\bf u}$ and ${\bf v}$.
Figure 3. The area of a parallelogram
Example. Find a vector perpendicular to the plane that passes through the points $P(1,4,6)$, $Q(-2,5,-1)$, and $R(1,-1,1)$.
Solution. The vectors $\overrightarrow{PQ}=(-3,1,-7)$ and $\overrightarrow{PR}=(0,-5,-5)$ lie in the plane through $P,Q,R$. So the cross product $$\overrightarrow{PQ}\times\overrightarrow{PR}=(-40,-15,15)$$ is perpendicular to the plane.
Example. Find the area of the triangle with vertices $P(1,4,6)$, $Q(-2,5,-1)$, and $R(1,-1,1)$.
Solution. In the previous example, we found $\overrightarrow{PQ}\times\overrightarrow{PR}=(-40,-15,15)$ and by \eqref{eq:areaparallelogram} we know that $|\overrightarrow{PQ}\times\overrightarrow{PR}|=\sqrt{(-40)^2+(-15)^2+{15}^2}=5\sqrt{82}$ is the area of the parallelogram determined by the two vectors $\overrightarrow{PQ}$ and $\overrightarrow{PR}$. The area of the triangle with vertices $P$, $Q$, and $R$ is just the half of the area of the parallelogram i.e. $\frac{5}{2}\sqrt{82}$.
From Figure 4, the volume of the parallelepiped determined by ${\bf u}$, ${\bf v}$, and ${\bf w}$ is $$V=|{\bf v}\times{\bf w}||{\bf u}|\cos\theta={\bf u}\cdot({\bf v}\times{\bf w})$$ In Figure 4, the vectors ${\bf u}$, ${\bf v}$, and ${\bf w}$ are positioned well enough so that the triple scalar product ${\bf u}\cdot({\bf v}\times{\bf w})$ is positive but depending on how they are positioned, it could be negative. Since the volume always has to be positive, it is given by \begin{equation}\label{eq:volumeparallelepiped}V=|{\bf u}\cdot({\bf v}\times{\bf w})|\end{equation}
Figure 4. The volume of a parallelepiped
The scalar triple product ${\bf u}\cdot({\bf v}\times{\bf w})$ can be written nicely by the determinant \begin{equation}\label{eq:scalartripleprod}{\bf u}\cdot({\bf v}\times{\bf w})=\begin{vmatrix}u_1 & u_2 & u_3\\v_1 & v_2 & v_3\\w_1 & w_2 & w_3\end{vmatrix}\end{equation} The calculation of the determinant can be done by the rule of Sarrus shown in Firgure 1.
Example. Determine if ${\bf u}=(1,4,-7)$, ${\bf v}=(2,-1,4)$, and ${\bf w}=(0,-9,18)$ are coplanar.
Solution. From Figure 4 above, one can easily see that the three vectors ${\bf u}$, ${\bf v}$ and ${\bf w}$ are coplanar (i.e. they are in the same plane) if and only if $\theta=\frac{\pi}{2}$ if and only if ${\bf u}\cdot ({\bf v}\times{\bf w})=0$. \begin{align*}{\bf u}\cdot ({\bf v}\times{\bf w})&=\begin{vmatrix}1 & 4 & -7\\2 & -1 & 4\\0 & -9 & 18\end{vmatrix}\\&=0\end{align*} Therefore, ${\bf u}$, ${\bf v}$ and ${\bf w}$ are coplanar.
The notion of the cross product can be used to describe physical effects involving rotations such as the circulation of electric/magnetic fields or fluids. Here we discuss the torque as a physical application of the cross product. Look at Figure 5.
Figure 5. Torque
Assume that a force ${\bf F}$ is acting on a rigid body at a point given by a position vector ${\bf r}$. The resulting turning effect ${\bf\tau}$, called the torque, can be measured by \begin{equation}\label{eq:torque}{\bf\tau}={\bf r}\times{\bf F}\end{equation}
Example. A bolt is tightened by applying a 40 N force to a 0.25 m wrench as shown in Figure 6. Find the magnitude of the torque about the center of the bolt.
Figure 6. Toque
Solution. The magnitude of the torque is \begin{align*}|{\bf\tau}|&=|{\bf r}\times{\bf F}|=|{\bf r}||{\bf F}|\sin 75^\circ=(0.25)(40)\sin 75^\circ\\&=10\sin 75^\circ\approx 9.66\ \mathrm{Nm}\end{align*}
Examples in this note have been taken from [1].
References.
[1] Calculus, Early Transcendentals, James Stewart, 6th Edition, Thompson Brooks/Cole
Definition. Let ${\bf u}=(u_1,u_2,u_3)$ and ${\bf v}=(v_1,v_2,v_3)$. Then the dot product ${\bf u}\cdot{\bf v}$ is defined by $${\bf u}\cdot{\bf v}=u_1v_1+u_2v_2+u_3v_3$$
The name “product” is misleading as it is not really an operation. The reason is simple because the outcome of a dot product is a scalar, not a vector. So what is a big deal about this dot product? The dot product defines the length of a vector. Let ${\bf u}=(u_1,u_2,u_3)$. Then $$|{\bf u}|=\sqrt{{\bf u}\cdot {\bf u}}=\sqrt{u_1^2+u_2^2+u_3^3}$$ Furthermore it can also define the distance between two points in space as shown in Figure 1:
Figure 1. Distance between two point P and Q
Let two position vectors ${\bf v}=(v_1,v_2,v_3)$ and ${\bf w}=(w_1,w_2.w_3)$ respectively represent points $P$ and $Q$ in space. The the distance $\overline{PQ}$ between the two points $P$ and $Q$ is the length of the vector ${\bf v}-{\bf w}$ $$\overline{PQ}=|{\bf v}-{\bf w}|=\sqrt{({\bf v}-{\bf w})\cdot({\bf v}-{\bf w})}=\sqrt{(v_1-w_1)^2+(v_2-w_2)^2+(v_3-w_3)^2}$$
Although this is beyond the scope of our discussion here, I would like to mention that the notion of the dot product can be generalized so that it can give rise to a different kind of length. Such generalization is called a scalar product or an inner product. You can read more about it here in case you are interested. A scalar product would satisfy the properties 1-4 in the above theorem. The dot product is associated with the length we are most familiar with, called the Euclidean length but that is not the only kind of length out there. For example, in the vector space of continuous functions on the closed interval $[0,1]$ (which was mentioned here), the scalar product of two functions $f$ and $g$, denoted by $\langle f,g\rangle$ is defined by $$\langle f,g\rangle=\int_0^1 f(x)g(x)dx$$ and the length of $f$ is defined by $$|f|=\sqrt{\langle f,f\rangle}=\sqrt{\int_0^1|f(x)|^2dx}$$ This type of a scalar product plays a very important role in quantum mechanics. It is used to measure the probability of a particle (such as an electron) to be in a particular quantum mechanical state.
There is an alternative description of the dot product.
Theorem. If $\theta$ is the angle between the vectors ${\bf u}$ and ${\bf v}$, where $0\leq\theta\leq\pi$, then \begin{equation}\label{eq:dotproduct}{\bf u}\cdot{\bf v}=|{\bf u}||{\bf v}|\cos\theta\end{equation}
Proof. By applying the Law of Cosines to triangle $\triangle OPQ$ in Figure 1, we obtain \begin{equation}\label{eq:lawcosine}|{\bf u}-{\bf v}|^2=|{\bf u}|^2+|{\bf v}|^2-2|{\bf u}||{\bf v}|\cos\theta\end{equation} \begin{align*}|{\bf u}-{\bf v}|^2&=({\bf u}-{\bf v})\cdot({\bf u}-{\bf v})\\&={\bf u}\cdot{\bf u}-{\bf u}\cdot{\bf v}-{\bf v}\cdot{\bf u}+{\bf v}\cdot{\bf v}\\&=|{\bf u}|^2-2{\bf u}\cdot{\bf v}+|{\bf v}|^2\end{align*}Replacing $|{\bf u}-{\bf v}|^2$ in \eqref{eq:lawcosine} by this last expression results in $$|{\bf u}|^2-2{\bf u}\cdot{\bf v}+|{\bf v}|^2=|{\bf u}|^2+|{\bf v}|^2-2|{\bf u}||{\bf v}|\cos\theta$$ and this simplifies to $${\bf u}\cdot{\bf v}=|{\bf u}||{\bf v}|\cos\theta$$
Example. Find the angle between the vector ${\bf u}=(2,2,-1)$ and ${\bf v}=(5,-3,2)$.
The alternative description of the dot product in \eqref{eq:dotproduct} is usually introduced as the definition of the dot product in high school/freshmen physics course.
From \eqref{eq:dotproduct}, we see that two vectors ${\bf u}$ and ${\bf v}$ are perpendicular or orthogonal (i.e. the angle $\theta$ between ${\bf u}$ and ${\bf v}$ is $\frac{\pi}{2}$) if and only if ${\bf u}\cdot{\bf v}=0$.
Example. $2{\bf i}+2{\bf j}-{\bf k}$ is perpendicular to $5{\bf i}-4{\bf j}+2{\bf k}$ because $$(2{\bf i}+2{\bf j}-{\bf k})\cdot(5{\bf i}-4{\bf j}+2{\bf k})=2(5)+2(-4)+(-1)(2)=0$$
This is beyond the scope of our discussion here but the notion of orthogonality of two vectors can be extended to higher dimensional spaces or more abstract vector spaces by defining that: two vectors ${\bf u}$ and ${\bf v}$ are said to be orthogonal if $\langle{\bf u},{\bf v}\rangle=0$, where $\langle\ ,\ \rangle$ denotes a scalar product. In our case, $\langle{\bf u},{\bf v}\rangle={\bf u}\cdot{\bf v}$. For example, Let $V$ be the set of all continuous functions on the closed interval $[-1,1]$. Then $V$ is a vector space with addition and scalar multiplication defined in the usual way that I discussed here. Also $\langle\ ,\ \rangle$ defined by $$\langle f,g\rangle=\int_{-1}^1f(x)g(x)dx$$ for $f,g\in V$ is a scalar product. The two functions $\sin(2n\pi x)$ and $\cos(2n\pi x)$ are continuous on $[-1,1]$ so they belong to $V$ i.e. they are vectors. They are also orthogonal because $$\langle\sin(2n\pi x),\cos(2n\pi x)\rangle=\int_{-1}^1\sin(2n\pi x)\cos(2n\pi x)dx=0$$
Let us take a look at Figure 2.
Figure 2. Scalar projection
Imagine that light rays coming down on the vector ${\bf u}$ at the direction perpendicular to the vector ${\bf v}$. The the shadow of ${\bf u}$ will be cast on ${\bf v}$ (the red line segment in Figure 2). Mathematically, this shadow is called the orthographic projection of ${\bf u}$ onto ${\bf v}$. In fact, the red line segment is the orthographic projection of the length of the vector ${\bf u}$ onto ${\bf v}$. We denote it by $\mathrm{comp}_{\bf v}{\bf u}$ and call it the scalar projection of ${\bf u}$ onto ${\bf v}$. Using basic trigonometry, we can easily find that $$\mathrm{comp}_{\bf v}{\bf u}=|{\bf u}|\cos\theta$$ However, we prefer to express the scalar projection free of the angle $\theta$ i.e. in terms of only ${\bf u}$ and ${\bf v}$. This can be done using \eqref{eq:dotproduct}: \begin{align*}\mathrm{comp}_{\bf v}{\bf u}&=|{\bf u}|\cos\theta\\&=|{\bf u}|\frac{{\bf u}\cdot{\bf v}}{|{\bf u}||{\bf v}|}\\&=\frac{{\bf u}\cdot{\bf v}}{|{\bf v}|}\end{align*} Hence we obtained our preferred form of the scalar projection \begin{equation}\label{eq:scalarprojection}\mathrm{comp}_{\bf v}{\bf u}=\frac{{\bf u}\cdot{\bf v}}{|{\bf v}|}\end{equation} One can also consider the vector projection of ${\bf u}$ onto ${\bf v}$. All you have to do is to multiplying the scalar projection \eqref{eq:scalarprojection} by the direction of ${\bf v}$: \begin{equation}\label{eq:vectorprojection}\mathrm{proj}_{\bf v}{\bf u}=\mathrm{comp}_{\bf v}{\bf u}\frac{{\bf v}}{|{\bf v}|}=\frac{{\bf u}\cdot{\bf v}}{|{\bf v}|^2}{\bf v}\end{equation}
Example. Find the scalar projection and the vector projection of ${\bf u}=(1,1,2)$ onto ${\bf v}=(-2,3,1)$.
Solution. The scalar projection is $$\mathrm{comp}_{\bf v}{\bf u}=\frac{{\bf u}\cdot{\bf v}}{|{\bf v}|}=\frac{1(-2)+1(3)+2(1)}{\sqrt{(-2)^2+3^2+1^2}}=\frac{3}{\sqrt{14}}$$ The direction of ${\bf v}$ is $\frac{1}{\sqrt{14}}(-2,3,1)$. Hence, the vector projection is $$\mathrm{proj}_{\bf v}{\bf u}=\mathrm{comp}_{\bf v}{\bf u}\frac{1}{\sqrt{14}}(-2,3,1)=\frac{3}{14}(-2,3,1)=\left(-\frac{3}{7},\frac{9}{14},\frac{3}{14}\right)$$
Consider a vector ${\bf v}=(v_1,v_2,v_3)$ in space. The angle $\alpha$ between ${\bf v}$ and ${\bf i}$, the angle $\beta$ between ${\bf v}$ and ${\bf j}$, and the angle $\gamma$ between ${\bf v}$ and ${\bf k}$ are called the direction angles of ${\bf v}$. (See Figure 3.)
Figure 3. Direction angles
Now, \begin{equation}\begin{aligned}\cos\alpha&=\frac{{\bf v}\cdot{\bf i}}{|{\bf v}||{\bf i}|}=\frac{v_1}{|{\bf v}|}\\\cos\beta&=\frac{{\bf v}\cdot{\bf j}}{|{\bf v}||{\bf j}|}=\frac{v_2}{|{\bf v}|}\\\cos\gamma&=\frac{{\bf v}\cdot{\bf k}}{|{\bf v}||{\bf k}|}=\frac{v_3}{|{\bf v}|}\end{aligned}\label{eq:directioncosine}\end{equation} $\cos\alpha$, $\cos\beta$ and $\cos\gamma$ are called the direction cosines of vector ${\bf v}$. It follows from \eqref{eq:directioncosine} that $(\cos\alpha,\cos\beta,\cos\gamma)$ is the direction of ${\bf v}$, hence the name directions cosines.
Example. Find the direction angles of the vector ${\bf v}=(1,2,3)$.
Solution. $|{\bf v}|=\sqrt{1^2+2^2+3^2}=\sqrt{14}$. Using \eqref{eq:directioncosine}, we have \begin{align*}\alpha&=\cos^{-1}\left(\frac{1}{\sqrt{14}}\right)\approx 74^\circ\\\beta&=\cos^{-1}\left(\frac{2}{\sqrt{14}}\right)\approx 58^\circ\\\gamma&=\cos^{-1}\left(\frac{3}{\sqrt{14}}\right)\approx 37^\circ\end{align*}
Work
Consider a linear motion i.e. a motion of an object along a straight line. See Figure 4.
Figure 4. Work
Suppose that an object is moved by a force ${\bf F}$. If the displacement is ${\bf D}$, then the work $W$ done by this force ${\bf F}$ is defined by the scalar projection of ${\bf F}$ onto ${\bf D}$, $|{\bf F}|\cos\theta$ (this is the component of ${\bf F}$ that actually moved the object) times the distance moved $|{\bf D}|$: \begin{equation}\label{eq:work}W={\bf F}\cdot{\bf D}\end{equation}
Example. A wagon is pulled a distance of 100 m along a horizontal path by a constant force of 70 N. The handle of the wagon is held at an angle of $35^\circ$ above the horizontal. Find the work done by the force.
Solution. The force ${\bf F}$ and the displacement ${\bf D}$ are as depicted in Figure 5.
Figure 5. Work
Thus the work $W$ is \begin{align*}W&={\bf F}\cdot{\bf D}=|{\bf F}||{\bf D}|\cos 35^\circ\\&=70(100)\cos 35^\circ=5734\ \mathrm{J}\end{align*} where J, called Joule, is a unit for work which stands for Newton times meter.
Example. A force given by the vector ${\bf F}=3{\bf i}+4{\bf j}+5{\bf k}$ moves a particle from the point $P(2,1,0)$ to the point $Q(4,6,2)$. Find the work done by the force.
Solution. Here, there is no mention of a particular path the particle is taking. We assume that the motion is again linear. The displacement is ${\bf D}=\overrightarrow{PQ}=(4-2,6-1,2-0)=(2,5,2)$. Hence the work is \begin{align*}W&={\bf F}\cdot{\bf D}=(3,4,5)\cdot(2,5,2)\\&=6+20+10=36\end{align*}
Examples in this note have been taken from [1].
References.
[1] Calculus, Early Transcendentals, James Stewart, 6th Edition, Thompson Brooks/Cole
A vector is a quantity that has both direction and magnitude. Examples of vectors include displacement, velocity, force, weight, momentum, etc. A quantity that has only magnitude is called a scalar. Scalars are really just numbers. Examples of scalars include distance, speed, mass, temperature, etc. Since a vector has both direction and magnitude, it can be visually represented by a directed arrow. For instance, if a particle moves from a point $A$ to another point $B$, its displacement (i.e. the shortest distance from $A$ to $B$) is denoted by $\overrightarrow{AB}$ and it is visually represented by a directed arrow as in Figure 1.
Figure 1. A vector
The direction at which the arrow is pointing is the direction of the vector $\overrightarrow{AB}$ and the length of the arrow is the magnitude of the vector $\overrightarrow{AB}$. When it is not necessary to specify the initial point and the terminal point, a vector is denoted by a lower case alphabet letter with arrow on top like $\vec v$ or in boldface like ${\bf v}$.
Two vectors are said to be the same or equivalent if they have the same direction and the magnitude regardless of where they are located. If vectors $\overrightarrow{AB}$ and $\overrightarrow{CD}$ are the same, we write $\overrightarrow{AB}=\overrightarrow{CD}$. Figure 2 shows two equivalent vectors $\overrightarrow{AB}$ and $\overrightarrow{CD}$.
Figure 2. Equivalent vectors
The equivalence of two vectors implies that a vector can be moved around maintaining its characters (direction and magnitude) so it stays as the same vector although its location has changed (meaning its initial and terminal points have changed). Moving a vector without changing direction and magnitude is called a parallel translation.
There are two types of operations on vectors. One is vector addition and the other is scalar multiplication. Vector addition is defined pictorially by using a parallelogram or a triangle. Figure 3 shows vector addition $$\overrightarrow{AB}+\overrightarrow{AC}=\overrightarrow{AD}$$ by using a parallelogram.
Figure 3. Vector addition by a parallelogram
Figure 4 shows vector addition $$\overrightarrow{AB}+\overrightarrow{BC}=\overrightarrow{AC}$$ by using a triangle. The two ways of adding two vectors are indeed equivalent. The only difference is how you locate two vectors to add them together.
Figure 4. Vector addition by a triangle
Scalar multiplication is a product between a scalar and a vector. While many people, even some (less careful) mathematicians, consider it as an operation, it is not an operation but an action. I am not going to talk about what an action is here. In case someone is curious, you can visit the Wikipedia page on Group Actionhere. In calculus level, distinction between an operation and an action is not really important at all. Let $c$ be a scalar and $v$ a vector. What the scalar multiplication $c{\bf v}$ does is, depends on the value of $c$, it can stretch (when $c>1$), shrink (when $0<c<1$), or reverse the direction (when $c=-1$) of vector ${\bf v}$ as illustrated in Figure 5.
Figure 5. Scalar multiplication
Using vector addition and scalar multiplication, one can define subtraction of a vector ${\bf v}$ from another vector ${\bf u}$: $${\bf u}-{\bf v}:={\bf u}+(-{\bf v})$$ See Figure 6.
Figure 6. Vector substraction
Earlier I mentioned that a vector can be moved around while preserving its direction and magnitude, and a parallel translation of a vector is still considered to be the same as the previous vector, although it is now at a different location. Among all those same vectors, we are particularly interested in vectors that are starting the origin $O$. Figure 7 shows an example of such a vector.
Figure 7. A position vector
A vectors whose initial point is the origin $O$ is called a position vector or a located vector. A position vector is determined only by its terminal point, thereby it can be identified with a point in space and conversely a point in space can be identified with a position vector. For example, if the terminal point of a position vector ${\bf v}$ is $(a_1,a_2,a_3)$, then we regard them the same i.e. ${\bf v}=(a_1,a_2,a_3)$. Why this is such a big deal? The directed arrow representation of a vector has a lot of limitations. The most severe limitation is that it can only be useful when we can see them, i.e. their usage is limited within 3-dimensions as our perception does not allow us to go beyond 3-dimensions. However, Einstein’s theory of relativity (which has also been confirmed by numerous experiments and observations) that our universe is actually 4-dimensional. But that’s the universe we observe right now. String theory tells us that the universe can have up to 26-dimensions. It does not have to go that far beyond though. There are many other places here down on earth including computer science, economics, etc. where the notions of vectors in higher dimensions are being used. Considering position vectors resolve the limitation. Furthermore, now that we identify vectors with points in space and points are represented by ordered $n$-tuples (ordered pairs, triples, quadruples, depending on the dimension of the space) which are algebraic objects, we can use the power of algebra to describe the properties of vectors.
Given the points $A(a_1,a_2,a_3)$ and $B(b_1,b_2,b_3)$, the vector ${\bf v}$ which is represented by the directed arrow $\overrightarrow{AB}$ is $${\bf v}=(b_1-a_1,b_2-a_2,b_3-a_3)$$
Example. Find the vector represented by the directed arrow with initial point $A(2,-3,4)$ and $B(-2,1,1)$.
Solution. ${\bf v}=(-2-2,1-(-3),1-4)=(-4,4,-3)$.
The length or magnitude of a vector ${\bf v}$ is denoted by $|{\bf v}|$. For a vector ${\bf v}=(v_1,v_2)$ in the plane, $|{\bf v}|$ is given by $$|{\bf v}|=\sqrt{v_1^2+v_2^2}$$ (It’s easy to see this from Figure 7 using the Pythagorean law.) Similarly, for a vector ${\bf v}=(v_1,v_2,v_3)$ in space, $$|{\bf v}|=\sqrt{v_1^2+v_2^2+v_3^3}$$ It follows from the definition that \begin{equation}\label{eq:length}|c{\bf v}|=|c||{\bf v}|\end{equation} where $c$ is a scalar.
Vector addition and scalar multiplication can be nicely defined algebraically without using parallelograms or triangles. Furthermore, these algebraic definitions apply to vectors in arbitrary $n$-dimensional space. For vectors ${\bf u}=(u_1,u_2,u_3)$, ${\bf v}=(v_1,v_2,v_3)$ and a scalar $c$, \begin{align*}{\bf u}+{\bf v}&:=(u_1+v_1,u_2+v_2,u_3+v+3)\\c{\bf u}&:=(cu_1,cu_2,cu_3)\end{align*}
${\bf u}+{\bf 0}={\bf u}$, where ${\bf 0}=(0,0,\cdots,0)$
${\bf u}+(-{\bf u})={\bf 0}$
$c({\bf u}+{\bf v})=c{\bf u}+c{\bf v}$
$(c+d){\bf u}=c{\bf u}+d{\bf u}$
$(cd){\bf u}=c(d{\bf u})$
$1{\bf u}={\bf u}$
It turns out the the original definition of vectors as quantities that have both direction and magnitude is quite obsolete and that even the definition of vectors by ordered $n$-tuples is not adequate enough to address much needed a broader notion of vectors arising in modern physics and engineering. For this reason, in modern treatment of vectors we no longer define what an individual vector is but instead we define a vector space. Simply speaking, a set $V$ with addition $+$ and scalar multiplication $\cdot$ satisfying the properties 1-8 is called a vector space, and the elements of $V$ are called vectors. Under this broader notion of vectors, things that were previously inconceivable to become vectors are now considered vectors. For example, $V$ the set of all continuous real-valued functions on the closed interval $[0,1]$ with addition $+$ and scalar multiplication $\cdot$ are defined by: \begin{align*}(f+g)(x)&:=f(x)+g(x)\\(cf)(x)&:=cf(x)\end{align*} for $f,g\in V$ and a scalar $c$. Then it is straightforward to show that the properties 1-8 are satisfied and therefore, $(V,+,\cdot)$ is a vector space and we regard continuous real-valued functions on $[0,1]$ as vectors. In fact, in quantum mechanics wave functions are state vectors. Another example is signal processing where functions are regarded as vectors. We are not going to delve into vector spaces further here. It is a main topic of linear algebra. For those who are curious, more examples of vector spaces can be found here.
There are infinitely many vectors. So it is humanly impossible to check if a certain property regarding vectors holds for all vectors. However there a particular finite set of vectors, called a basis, that constitute the entire vectors. A vector ${\bf u}=(u_1,u_2,u_3)$ can be written as \begin{equation}\label{eq:lincomb}{\bf u}=u_1(1,0,0)+u_2(0,1,0)+u_3(0,0,1)\end{equation} So we see that any vector can be represented by the three vectors $${\bf i}=(1,0,0),\ {\bf j}=(0,1,0),\ {\bf k}=(0,0,1)$$ by applying vector addition and scalar multiplication finitely many times as in \eqref{eq:lincomb}. The expression on the right hand side of the identity in \eqref{eq:lincomb} is called a linear combination or a superposition of ${\bf i}$, ${\bf j}$, ${\bf k}$. The three vectors ${\bf i}$, ${\bf j}$, ${\bf k}$ are called the canonical or standard basis vectors.
Figure 8. The standard basis
The number of standard basis vectors determines the dimension of the space. The dimension of a space is not necessarily finite though we are considering only finite dimensional spaces here (actually only 2- or 3-dimensional spaces). The set $V$ of all continuous functions on $[0,1]$ is infinite dimensional. The set of all state vectors in a quantum mechanics system is, in general, an infinite dimensional space called a Hilbert space.
Example. If ${\bf u}={\bf i}+2{\bf j}-3{\bf k}$ and ${\bf v}=4{\bf i}+7{\bf k}$, express $2{\bf u}+3{\bf v}$ in terms of ${\bf i}$, ${\bf j}$, ${\bf k}$.
Often in geometry and physics, we are only interested in the direction of a vector. A unit vector is a vector with length 1. Any non-zero vector can be re-scaled to a unit vector with the same direction. All that’s required is dividing the given vector by its magnitude. If ${\bf u}\ne {\bf 0}$, then $$\hat{\bf u}:=\frac{{\bf u}}{|{\bf u}|}$$ is a unit vector which has the same direction as ${\bf u}$: Using \eqref{eq:length}, $$|\hat{\bf u}|=\left|\frac{{\bf u}}{|{\bf u}|}\right|=\frac{1}{|{\bf u}|}|{\bf u}|=1$$
Example. Find the unit vector in the direction of the vector $2{\bf i}-{\bf j}-2{\bf k}$.
Solution. The length of the vector is $\sqrt{2^2+(-1)^2+(-2)^2}=\sqrt{9}=3$. Hence the unit vector with the same direction is $$\frac{2}{3}{\bf i}-\frac{1}{3}{\bf j}-\frac{2}{3}{\bf k}$$
In physics, when several forces are acting on an object, the resultant force or the net force experienced by the object is the vector sum of these forces.
Example. A 100-lb weight hangs from two wires as shown in Figure 9. Find the tension forces ${\bf T}_1$ and ${\bf T}_2$ in both wires and their magnitudes.
Figure 9. The resultant force
Solution. We first express the tensions ${\bf T}_1$ and ${\bf T}_2$ in terms of their horizontal and vertical components (the vectors in green in Figure 10).
Figure 10. The resultant force
\begin{align}\label{eq:tension1}{\bf T}_1&=-|{\bf T}_1|\cos 50^\circ{\bf i}+|{\bf T}_1|\sin 50^\circ{\bf j}\\\label{eq:tension2}{\bf T}_2&=|{\bf T}_2|\cos 32^\circ{\bf i}+|{\bf T}_2|\sin 32^\circ{\bf j}\end{align} The net force ${\bf T}_1+{\bf T}_2$ of the tensions must counterbalance the weight ${\bf w}$ so that the mass stays hung as in the figure, i.e. $${\bf T}_1+{\bf T}_2=-{\bf w}=100{\bf j}$$ From equations \eqref{eq:tension1} and \eqref{eq:tension2}, we have $$(-|{\bf T}_1|\cos 50^\circ+|{\bf T}_2|\cos 32^\circ){\bf i}+(|{\bf T}_1|\sin 50^\circ+|{\bf T}_2|\sin 32^\circ){\bf j}=100{\bf j}$$ By comparing the components, we obtain the following equations: \begin{align*}-|{\bf T}_1|\cos 50^\circ+|{\bf T}_2|\cos 32^\circ&=0\\|{\bf T}_1|\sin 50^\circ+|{\bf T}_2|\sin 32^\circ&=100\end{align*} Solving these equations simultaneously we find \begin{align*}|{\bf T}_1|&=\frac{100}{\sin 50^\circ+\tan 32^\circ\cos 50^\circ}\approx 85.64\mathrm{lb}\\|{\bf T}_2|&=\frac{|{\bf T}_1|\cos 50^\circ}{\cos 32^\circ}\approx 64.91\mathrm{lb}\end{align*} Therefore, $${\bf T}_1\approx -55.05{\bf i}+65.60{\bf j},\ {\bf T}_2\approx 55.05{\bf i}+34.40{\bf j}$$
Examples in this note have been taken from [1].
References.
[1] Calculus, Early Transcendentals, James Stewart, 6th Edition, Thompson Brooks/Cole
In mathematics and also in applications, we often encounter problems that require to maximize or minimize the value of a certain quantity. The general procedure can be summarized as:
Express the quantity to be maximized or minimized in terms of a single variable. The quantity may be described in terms of two variables however with given constraint it could be reduced to a single variable.
Differentiate the function obtained in step 1 and set the derivative equal to 0.
Solve the equation from step 2 to obtain critical values and determine whether they maximize or minimize the given quantity. Usually the first or second derivative test is a convenient tool for the required inspection.
Example. A farmer has 2400 ft of fencing and wants to fence off a rectangular field that borders a straight river. He needs no fence along the river. What are the dimensions of the field that has the largest area?
Solution. Let $x$ and $y$ denote the length and the width of the rectangular field. Suppose that the side along the river has the length $x$. Then the area is $A=xy$ and the required fencing in terms $x$ and $y$ is $x+2y=2400$. This fencing is a constraint and solve it for $y$ to obtain $y=1200-\frac{x}{2}$. Plugging this into $A$ for $y$, the area can be written as a function of a single variable $x$: $$A(x)=1200x-\frac{x^2}{2}$$ $A'(x)=1200-x$ and setting this equal to 0, we find $x=1200$. Since $A^{\prime\prime}(x)=-1<0$, by the second derivative test $x=1200$ gives rise to the absolute maximum of $A(x)$. The required dimensions are $1200\ \mbox{ft}\times 600\ \mbox{ft}$ where the side that borders the river is 1200 ft and the resulting largest area is 720,000 $\mbox{ft}^2$.
Example. A box with a square base and open top must have a volume of 32,000 $\mbox{cm}^3$. Find the dimensions of the box that minimize the amount of material used.
Solution. Let $x$ and $h$ be the length and the height of the box, respectively. Then $x^2h=32000$ and we want to minimize the surface area $A=x^2+4xh$. Solve the volume constraint for $h$ to obtain $h=\frac{32000}{x^2}$. Plugging this into $A$ for $h$, we write $A$ as a function of a single variable $x$: $$A(x)=x^2+\frac{128000}{x}$$ $A'(x)=2x-\frac{128000}{x^2}$ and setting it equalto 0, we find $x=40$. Since $A^{\prime\prime}(x)=2+\frac{256000}{x^3}>0$ for all $x>0$, $A(40)$ is the absolute minimum. Therefore the required dimensions are $40\ \mbox{cm}\times 40\ \mbox{cm}\times 20\ \mbox{cm}$.
Example. If 1200 $\mbox{cm}^2$ of material is available to make a box with a square base and an open top, find the largest possible volume of the box.
Solution. Let $x$ and $h$ be the length and the height of the box, respectively. Then $x^2+4xh=1200$ and we want to maximize $V=x^2h$. Solve the area for $h$ to obtain $h=\frac{1200-x^2}{4x}$. Plugging this into $V$, we write the volume as a function of a single variable $x$: $$V(x)=300x-\frac{1}{4}x^3$$ $V'(x)=300-\frac{3}{4}x^2$ and setting it equal to 0, we find $x=20$. Since $V'(x)$ is a quadratic polynomial with a negative leading coefficient, $V(20)=4000\ \mbox{cm}^3$ is the largest possible volume of the box.
Example. Find the point on the parabola $y^2=2x$ that is the closest to the point $(1,4)$.
Solution. Let $(x,y)$ denote a point on the parabola $y^2=2x$. The distance between $(x,y)$ and $(1,4)$ is $d=\sqrt{(x-1)^2+(y-4)^2}$ and we want to minimize this. Note minimizing $d$ is equivalent to minimizing $d^2=(x-1)^2+(y-4)^2$. Solve the equation of parabola for $x$ to obtain $x=\frac{y^2}{2}$. Plugging this into $d^2$, we can write it as a function of a single variable $y$: $$f(y)=\left(\frac{y^2}{2}-1\right)^2+(y-4)^2=\frac{y^4}{4}-8y+17$$ $f'(y)=y^3-8$ and setting it equal to 0, we find $y=2$. Since $f^{\prime\prime}(y)=3y^2>0$ for all $y\ne 0$, $(x,y)=(2,2)$ is the point on the parabola $y^2=2x$ that is the closest to $(1,4)$.
The shortest distance from (1,4) to the parabola y^2=2x.
Remark. The above problem also can be solved using a simple geometric fact that the shortest path from $(1,4)$ to the parabola $y^2=2x$ would be normal to the tangent line (i.e. the path is perpendicular to the tangent line). Let $(a,b)$ be the point on the parabola that is closest to $(1,4)$. By implicit differentiation we find $\frac{dy}{dx}=\frac{1}{y}$ and so the normal line at $(a,b)$ has the slope $-b$. The equation of the normal line is then $y-4=-b(x-1)$. Since this line is passing through $(a,b)$, $b-a=-b(a-1)$ or $ab=4$. $(a,b)$ is also on the parabola so we have $b^2=2a$. Solve the two equations simultaneously to obtain $b=2$ and hence $a=2$. Therefore, $(a,b)=(2,2)$.
Example. An open box is to be made out of a 6-inch by 18-inch piece of cardboard by cutting out squares of equal size from the four corners and bending up the sides. Find the dimensions of the resulting box that has the largest volume.
Solution. When you tackle this type of problems, it is very important to draw a picture that properly depicts the description of the problem as shown in the following figure.
From the figure, the length and the width of the box are, respectively, $18-2x$ and $y=6-2x$. Thus, the volume $V$ is $$V(x)=(6-2x)(18-2x)x=4x^3-48x^2+108x$$ To find the critical points, set $$V'(x)=12x^2-96x+108=0$$ or equivalently $x^2-8x+9=0$. This quadratic equation has two solutions $x=4\pm\sqrt{7}$. Recall that in order for a critical point $c$ to maximize the volume, it is required that $$V^{\prime\prime}(c)=24c-96<0,$$ i.e. $c<4$. Thus, $x=4-\sqrt{7}=1.35425$ maximizes the volume $V(x)$. The dimensions of the box that has the largest volume is then $$15.2915\times 3.2915$$
Example. A fence 4 feet tall runs parallel to a tall building at a distance of 2 feet from the building. What is the length of the shortest ladder that will reach from the ground over the fence to the wall of the building?
Solution. The following figure depicts the description of the problem.
The big and small right triangles are similar triangles, so we have $$\frac{x+2}{y}=\frac{x}{\sqrt{x^2+16}}$$ which is equal to $\cos\theta$. Solving the equation for $y$, we obtain $$y=\sqrt{x^2+16}+\frac{2\sqrt{x^2+16}}{x}$$ To minimize $y$, we find $$y’=\frac{x^3-32}{x^2\sqrt{x^2+16}}$$ and there is only one critical point $x=\root 3\of{32}=2\root 3\of{4}$. The resulting $y$ value, i.e. the length of the shortest ladder is then $8.32388$. By the second derivative, one can confirm that the critical point indeed minimizes the length of the ladder. But even without using the second derivative, one can make the length of such ladder as long as one likes, so there is no maximum length of such ladder.
When we defined the definite integral $\int_a^b f(x)dx$, it was assumed that the limits $a$ and $b$ of the integral are finite and the integrand $f(x)$ is continuous on the closed interval $[a,b]$. Even if these assumptions are not satisfied, we can still consider a notion of integral extended from the definite integral (the Riemann integral). This extended integral is called the improper integral.
Infinite Limits
A definite integral, in which one or both limits of integration are infinite, is defined by the following: \begin{align*}\int_a^\infty f(x)dx&=\lim_{t\to\infty}\int_a^tf(x)dx\\\int_{-\infty}^b f(x)dx&=\lim_{t\to -\infty}\int_t^bf(x)dx\end{align*} The improper integrals are said to be convergent if the corresponding limit exists. Otherwise, divergent.
If both $\int_{-\infty}^a f(x)dx$ and $\int_a^\infty f(x)dx$ are convergent, we define $$\int_{-\infty}^\infty f(x)dx=\int_{-\infty}^a f(x)dx+\int_a^\infty f(x)dx$$
$\int_{-\infty}^\infty f(x)dx$ can be also defined by the double limit $$\int_{-\infty}^\infty f(x)dx=\lim_{b\to\infty\\a\to -\infty}\int_a^b f(x)dx$$
$\int_{-\infty}^\infty\frac{2x}{1+x^2}dx$ is divergent as $\int_{-\infty}^0\frac{2x}{1+x^2}dx=-\infty$ and $\int_0^\infty\frac{2x}{1+x^2}dx=\infty$. On the other hand, $$\lim_{a\to\infty}\int_{-a}^a\frac{2x}{1+x^2}dx=0$$ This is called the Cauchy principal value of the integral $\int_{-\infty}^\infty\frac{1}{1+x^2}dx$ and is denoted by $$\mathrm{p.v.}\int_{-\infty}^\infty\frac{1}{1+x^2}dx$$ The Cauchy principal value is a method of assigning values to certain ill-defined improper integrals. We will not, however, be considering the Cauchy principal value here.
Discontinuous Integrand
If $f(x)$ is continuous for all values of $x$ in the domain $a\leq x\leq b$ except $x=b$ or $x=a$, $\int_a^b f(x)dx$ is defined by \begin{equation}\label{eq:impropint}\int_a^b f(x)dx=\lim_{t\to b-}\int_a^t f(x)dx\end{equation} or \begin{equation}\label{eq:impropint2}\int_a^b f(x)dx=\lim_{t\to a+}\int_t^b f(x)dx\end{equation} provided the corresponding limit exists.
When $f(x)$ is continuous for all values of $x$ in the domain $a\leq x\leq b$ except $x=c$ (where $a< c <b$), $\int_a^b f(x)dx$ is defined by $$\int_a^b f(x)dx=\int_a^c f(x)dx+\int_c^b f(x)dx$$ where the integrals in the RHS are evaluated in accordance with \eqref{eq:impropint} and \eqref{eq:impropint2}, respectively.
Example. Consider $\int_{-1}^1\frac{dx}{x^2}$.
The graph of y=1/x^2 on [-1,1]
Solution. Since the integrand is discontinous at $x=0$, we write the integral in two parts as $$\int_{-1}^1\frac{dx}{x^2}=\int_{-1}^0\frac{dx}{x^2}+\int_0^1\frac{dx}{x^2}=\infty+\infty=\infty$$
Remarks. If one mindlessly evaluates the integral as an ordinary definite integral, we obtain $$\int_{-1}^1\frac{dx}{x^2}=\left[-\frac{1}{x}\right]_{-1}^1=-2$$ However, this is nonsense because the integrand is always positive.
