Written by Luka Kerr on November 3, 2018

## Algebra

### Subspaces

A subspace $S$ is only a subspace of $V$ over a field $\mathbb{F}$ if:

- The zero vector $\vec{0}$ belongs to $S$
- $S$ is closed under vector addition
- $S$ is closed under scalar multiplication

### Linear Combinations and Spans

Given a set of vectors $S = { v_1, v_1 \dots{} v_n }$:

- A
**linear combination**of $S$ is a sum of scalar multiples of the form $\lambda_1 v_1 + \dots{} \lambda_n v_n$ - A
**span**of $S$ is the set of all linear combinations of $S$ - A
**spanning set**is the finite set $S$ such that every vector in a vector space $V$ can be expressed as a linear combination of vectors in $S$

### Linear Independence

#### Linearly Independent

A set of vectors $S$ is said to be linearly independent if for each vector in $S$, the only values of the scalars $\lambda_1, \lambda_2 \dots{} \lambda_n$ for which $\lambda_1 v_1 + \lambda_2 v_2 + \dots{} + \lambda_n v_n = 0$, are $0$.

#### Linearly Dependent

A set of vectors $S$ is said to be linearly dependent if it is not a linearly independent set. In other words, there exists a $\lambda_n$ that is not $0$.

### Basis and Dimension

A set of vectors $S$ is a basis for a vector space $V$ if:

- $S$ is a linearly independent set
- $S$ is a spanning set for $V$

### Linear Maps

A function $T : V \to W$ is called a **linear transformation** if:

- Addition is preserved: $T(\bold{v} + \bold{u}) = T(\bold{v}) + T(\bold{u})$
- Scalar multiplication is preserved: $T(\lambda \bold{v}) = \lambda T(\bold{v})$

### Linear Map Subspaces

#### Kernel

*Definition:* $ker(A) = { \bold{v} \in \mathbb{R}^n : A \bold{v} = 0 }$

*Calculate:* row reduce $A$, solve for $\lambda$’s and form a span

*Basis:* form a set using the smallest number of linearly independent vectors from $ker(A)$

#### Nullity

*Definition:* $nullity(A) = dim(ker(A))$

*Calculate:* find $ker(A)$ and take its dimension OR take $dim(A) - rank(A)$

#### Image

*Definition:* $im(A) = { \bold{b} \in \mathbb{R}^n : A \bold{x} = \bold{b} }$

*Calculate:* row reduce $(A | b)$

*Basis:* row reduce, find the linearly independent columns and take those columns
from the original matrix as a span

#### Rank

*Definition:* $rank(A) = dim(im(A))$

*Calculate:* find $im(A)$ and take its dimension OR row reduce $A$ and
take the number of leading columns

### Eigenvalues and Eiegnvectors

#### Eigenvalue

*Definition:* $\lambda$’s such that $A \bold{v} = \lambda \bold{v}$

*Calculate:* solve $det(A - \lambda I) = 0$ where $I$ is the identity matrix for $A$

#### Eigenvector

*Definition:* $\bold{v}$ such that $A \bold{v} = \lambda \bold{v}$ for
each eigenvalue $\lambda$

*Calculate:* solve $ker(A - \lambda I)$ for each eigenvalue $\lambda$

### Diagonalisation

A square matrix $A$ is said to be diagonalisable if there exists an invertible matrix $M$ and a diagonal matrix $D$ such that $M^{-1} A M = D$.

Given $n$ linearly independent eigenvectors $\bold{v_1}, \dots, \bold{v_n}$ and corresponding eigenvalues $\lambda_1, \dots, \lambda_n$, we let

\[M = (\bold{v_1} | \dots | \bold{v_n}) , \quad D = \begin{pmatrix} \lambda_1 & & 0 \\ & \ddots & \\ 0 & & \lambda_n \end{pmatrix}\]such that $M^{-1} A M = D$ holds.

### Systems of Differential Equations

The system

\[\begin{cases} \frac{dx}{dt} = a_1 y_1 + b_1 y_2 \\ \frac{dy}{dt} = a_2 y_2 + b_2 y_2 \end{cases}\]can be written as

\[\begin{pmatrix} a_1 & b_1 \\ a_2 & b_2 \end{pmatrix} \begin{pmatrix} y_1 \\ y_2 \end{pmatrix}\]where

\[\begin{pmatrix} y_1 \\ y_2 \end{pmatrix} = c_1 \bold{v_1} e^{\lambda_1 t} + c_2 \bold{v_2} e^{\lambda_2 t}\]given that $\lambda_k, \bold{v_k}$ are eigenvalue-eigenvector pairs.

### Probability

#### Rules and Conditions

- $P(A \cap B) = P(A) + P(B) - P(A \cup B)$
- $P(A^c) = 1 - P(A)$
- $P(A | B) = \dfrac{P(A \cap B)}{P(B)}$
- Mutual exclusion if $A \cap B = \emptyset$
- Statistically independent if $P(A \cap B) = P(A) \times P(B)$

### Random Variables

#### Probability Distribution

To show a sequence $p_k$ is a probability distribution the following properties must be proven

- $p_k \ge 0$
- $\sum_k^{\infty} p_k = 1$

#### Expected Value

$E(X) = \sum_{\text{all k}} k p_k$

$E(X^2) = \sum_{\text{all k}} k^2 p_k$

#### Variance

$Var(X) = E(X^2) - E(X)^2$

#### Standard Deviation

$SD(X) = \sqrt{Var(X)}$

### Special Distributions

#### Binomial Distribution

$B(n, p, k) = \begin{pmatrix}n \ k\end{pmatrix} p^k (1 - p)^{n - k}$ where $k = 0, 1, \dots{} n$

#### Geometric Distribution

$G(p, k) = (1 - p)^{k - 1} p$

### Continuous Random Variables

A random variable $X$ is continuous if $F_X(x)$ is continuous.

#### Probability Density Function

The probability density function of a continuous random variable $X$ is defined by

\[f(x) = f_X(x) = \dfrac{d}{dx} F(x) \ , \ x \in \mathbb{R}\]if $F(x)$ is differentiable, and $\lim_{x \to a^-} \dfrac{d}{dx} F(x)$ if $F(x)$ is not differentiable at $x = a$.

#### Expected Value

$E(X) = \displaystyle \int_{- \infty}^{\infty} x \ f(x) \ dx$

#### Variance

$Var(X) = E(X^2) - (E(X))^2$

### Special Continuous Distributions

#### Normal Distribution

A continuous random variable $X$ has a normal distribution $N(\mu, \sigma^2)$ if it has a probability density $\phi (x) = \dfrac{1}{\sqrt{2 \pi \sigma^2}} e^{- \frac{1}{2} (\frac{x - \mu}{\sigma})^2}$ where $- \infty < x < \infty$.

#### Exponential Distribution

A continuous random variable $T$ has an exponential distribution $Exp(\lambda)$ if it has a probability distribution density

\[f(t) = \begin{cases} \lambda e ^{- \lambda t} & \text{if t} \ge 0 \\ 0 & \text{if t} < 0 \end{cases}\]## Calculus

### Partial Differentiation

To find the partial derivative of a function with two variables $x$ and $y$, we can treat one of the variables as a constant and differentiate with respect to the other.

### Tangent Plane To Surfaces

Suppose $F$ is a function of two variables, and $P$ is a point $(x_0, y_0, z_0)$ that lies on the surface $z = F(x, y)$.

#### Tangent Plane Of Surface

$z = z_0 + F_x (x_0, y_0) (x - x_0) + F_y (x_0, y_0)(y - y_0)$

#### Normal Vector To Surface

\[\begin{pmatrix} F_x (x_0, y_0) \\ F_y (x_0, y_0) \\ -1 \end{pmatrix}\]### Total Differential Approximation

$\triangle F \approx F_x (x_0, y_0) (x - x_0) + F_y (x_0, y_0) (y - y_0)$

### Chain Rule

For a function $F$ with two variables $x$ and $y$, the chain rule can be defined as

\[\dfrac{dF}{dt} = \dfrac{\partial F}{\partial x} \dfrac{dx}{dt} + \dfrac{\partial F}{\partial y} \dfrac{dy}{dt}\]Don’t forget to substitute in $x$ and $y$ after finding the derivatives.

### Functions Of Two Or More Variables

#### Partial Derivatives

For a function $F$ of three variables $x$, $y$ and $z$, the partial derivatives of $F$ can be defined as

\[F_x = \dfrac{\partial F}{\partial x}, \quad F_y = \dfrac{\partial F}{\partial y}, \quad F_z = \dfrac{\partial F}{\partial z}\]#### Chain Rule

For a function $F$ of three variables $x$, $y$ and $z$, where $x$ and $y$ are each functions of both $u$ and $v$, the chain rule for $F$ can be defined as

\[\dfrac{\partial F}{\partial u} = \dfrac{\partial F}{\partial x} \dfrac{\partial x}{\partial u} + \dfrac{\partial F}{\partial y} \dfrac{\partial y}{\partial u} + \dfrac{\partial F}{\partial z} \dfrac{\partial z}{\partial u}\] \[\dfrac{\partial F}{\partial v} = \dfrac{\partial F}{\partial x} \dfrac{\partial x}{\partial v} + \dfrac{\partial F}{\partial y} \dfrac{\partial y}{\partial v} + \dfrac{\partial F}{\partial z} \dfrac{\partial x}{\partial v}\]### Integration Techniques

#### Trigonometric Integrals

Considers integrals of the form \(\int \cos^m x \ \sin^n x \ dx\)

**Cases:**

- $m$ or $n$ or both are odd: $u = \sin x$, $du = \cos x \ dx$
- $m$ and $n$ are even: $\cos^2x = \dfrac{1 + \cos 2x}{2}$, $\sin^2x = \dfrac{1 - \cos 2x}{2}$

### Ordinary Differential Equations

#### Seperable ODEs

Seperable ODEs are differential equations where two variables are involved (usually $x$ and $y$) that can be seperated so that all the $y$’s are on one side, and all the $x$’s are on the other. The tend to be in the form

\[f(y) \frac{dy}{dx} = g(x)\]**To solve:**

- Move all the $y$’s to one side, and the $x$’s to the other. So $f(y) \dfrac{dx}{dy} = g(x)$ becomes $f(y) dy = g(x) dx$
- Integrate both sides with respect to the respective variable $\int f(y) dy = \int g(x) dx$
- Solve for $y$

#### First Order Linear ODEs

First Order Linear ODEs are differential equations that involve functions of a single variable. They can be written in the form \(\dfrac{dy}{dx} + f(x) y = g(x)\)

**To solve:**

- Write the differential equation as above
- Find the integrating factor $e^{\int f(x) dx}$, this is denoted by $h(x)$
- Multiply both sides by the integrating factor $h(x)$ to obtain $\dfrac{d}{dx} (h(x) y) = g(x) h(x)$
- Integrate both sides with respect to $x$, and solve for $y$

#### Exact ODEs

Exact ODEs are differentiable equations involving functions with two or more
variables. They are typically of the form \(F(x, y) + G(x, y) \frac{dy}{dx} =
0\) and are said to be *exact* if \(\dfrac{\partial F}{\partial y} = \dfrac{\partial G}{\partial x}\)

**To solve:**

- Show that a differential equation is exact by proving the above property
- Look for a function $H(x, y)$ such that \(\def\arraystretch{2} \begin{array}{l} \dfrac{\partial H}{\partial x} = F(x, y) \qquad (1) \\ \dfrac{\partial H}{\partial y} = G(x, y) \qquad (2) \end{array}\)
- Integrate (1) with respect to $x$ to find $H(x, y) = f(x, y) + C(y)$
- To find $C(y)$, partially differentiate $H(x, y)$ with respect to $y$ (leaving all of the $x$ components constant) and compare that with the partial derivative of $H$ with respect to $y$. This gives $C’(y)$ and thus allows to find $C(y)$

### Second Order Linear ODEs

A second order linear ODE with constant coefficients is said to be homogeneous if it is of the form \(y'' + ay' + by = 0\) where $a$ and $b$ are real numbers.

#### Characteristic Equation

The characteristic equation of a second order linear ODE is given by

\[\lambda^2 + a\lambda + b = 0\]### Taylor Polynomial

For a differentiable function $f$, the Taylor polynomial $p_n$ of order $n$ at $x = a$ is

\[p_n (x) = f(a) = f'(a) (x - a) + \frac{f''(a)}{2!} (x - a)^2 + \frac{f^{(3)} (a)}{3!} (x - a)^3 + \dots{} + \frac{f^{(n)} (a)}{n!} (x - a)^n\]#### Taylor’s Theorem

\[f(x) = p_n(x) + R_{n + 1} (x)\]where

\[R_{n + 1} (x) = \frac{f^{(n + 1)} (c)}{(n + 1)!} (x - a)^{n + 1}\]### Sequences

When evaluating limits, functions and sequences are identical. This is shown below \(\lim_{x \to \infty} f(x) = L \implies \lim_{n \to \infty} a_n = L\)

A sequence diverges when $\lim_{n \to \infty} a_n \pm \infty$ or $\lim_{n \to \infty} a_n$ does not exist. Otherwise, the sequence converges.

#### Properties Of Sequences

Given a sequence of real numbers ${ a_n }_{n = 0}^{\infty}$, the following properties hold

*increasing*if $a_n < a_{n + 1}$ for each $n \in \mathbb{N}$*non-decreasing*if $a_n \le a_{n + 1}$ for each $n \in \mathbb{N}$*decreasing*if $a_n > a_{n + 1}$ for each $n \in \mathbb{N}$*non-increasing*if $a_n \ge a_{n + 1}$ for each $n \in \mathbb{N}$- $M$ is a
*upper bound*if $a_n \le M$ for each $n \in \mathbb{N}$ - $M$ is a
*lower bound*if $a_n \ge M$ for each$n \in \mathbb{N}$

### Infinite Series

#### The $k$th Term Divergence Test

$\sum_{k = 1}^{\infty} a_k$ diverges if $\lim_{n \to \infty} a_k$ fails to exist, or is non-zero.

#### Integral Test

#### Comparison Test

Suppose that ${ a_k }_{k = 0}^{\infty}$ and ${ b_k }_{k = 0}^{\infty}$ are two positive sequences such that $ak \le bk$ for every natural number $k$.

- If $\sum_{k = 0}^{\infty} b_k$ converges, then $\sum_{k = 0}^{\infty} a_k$ converges
- If $\sum_{k = 0}^{\infty} b_k$ diverges, then $\sum_{k = 0}^{\infty} a_k$ diverges

Usually used for series of the form $\sum_{k = 1}^{\infty} \dfrac{1}{k^p}$, such that this series converges if $p > 1$ and diverges if $p \le 1$.

#### Ratio Test

Suppose that $\sum a_k$ is an infinite series with positive terms and that $\lim_{k \to \infty} \dfrac{a_{k + 1}}{a_k} = r$.

- If $r < 1$ then $\sum a_k$ converges
- If $r > 1$ then $\sum a_k$ diverges
- If $r = 1$ this test is inconclusive

#### Alternating Series Test

### Taylor Series

$\displaystyle \sum_{k = 0}^{\infty} \frac{f^{(k)} (a)}{k!} (x - a)^k$

### Power Series

Given a sequence ${ a_k }_{k = 0}^{\infty}$ is a sequence of real numbers and that $a \in \mathbb{R}$, then

\[\sum_{k = 0}^{\infty} a_k x^k\]is the power series of $x$, and

\[\sum_{k = 0}^{\infty} a_k (x - a)^k\]is a power series of $x - a$.