## Linear Algebra Review Notes

Or, everything you ever needed to know from linear algebra.

### Vectors

Elements $\mathbf{v}$ in $\mathbb{R}^n$ are Vectors. A vector can be thought of as $n$ real numbers stacked on top of each other (column vectors).

### Properties of Vectors

Let $\mathbf{v}, \mathbf{a}, \mathbf{b}$ be vectors in $\mathbb{R}^n$, and $\gamma$ a scalar in $\mathbb{R}$ . Let $\theta$ be the angle between vectors A and B.

• $\mathbf{v}^T = (v_1, v_2, \dots, v_n)$ is the Transpose of a vector.

• Scalar Multiplication:
• The scalar product between two vertices, or Dot Product:

The scalar product is also written as $(\mathbf{a},\mathbf{b})$ or as $\mathbf{a}^T\mathbf{b}$. The scalar product is Symmetric. That is, $\mathbf{a} \cdot \mathbf{b} = \mathbf{b} \cdot \mathbf{a}$

• The vector Norm or length of a vector is defined by

There are different â€śkindsâ€ť of norms. This particular norm is called the Euclidean Norm for a vector. More information on norms can be found here.

• Cauchy-Bunyakowski-Schwarz Inequality: The following inequality holds:
• Triangle Inequality: The following inequality holds:
• The cosine of the angle $\theta$ between vectors $\mathbf{a}$ and $\mathbf{b}$ is $\cos{\theta} := \frac{\mathbf{a} \cdot \mathbf{b}}{\| \mathbf{a} \| \| \mathbf{b} \|}$
• $\mathbf{a} \perp \mathbf{b}$ means the vectors $\mathbf{a}$ and $\mathbf{b}$ are Orthogonal. The following is true:

$\mathbf{a} \perp \mathbf{b} \Leftrightarrow \mathbf{a} \cdot \mathbf{b} = 0 \Leftrightarrow \cos{\theta}=0$ $\mathbf{v} \perp \mathbf{v} \Leftrightarrow \mathbf{v} = 0$

### Definitions of Vertices

• A linear subspace $L \subseteq \mathbb{R}^n$ is a set enjoying the following properties:
• For all $\mathbf{a}$, $\mathbf{b} \in L$ it holds that $\mathbf{a} + \mathbf{b} \in L$
• For all $\mathbf{a} \in L$, $\gamma \in \mathbb{R}$ it holds that $\gamma + \mathbf{a} \in L$
• An affine subspace $A \subseteq \mathbb{R}^n$ is any set that can be represented as $\mathbf{v} + L := \{ \mathbf{v} + \mathbf{x} : \mathbf{x} \in L\}$, for some vector $\mathbf{v}$ in $\mathbb{R}^n$ and linear subspace $L \subseteq \mathbb{R}^n$
• A set of vectors $(\mathbf{v_1}, \mathbf{v_2}, \dots, \mathbf{v_n})$ is said to be Linearly Independent if and only if

There can be at most $n$ linearly independent vectors in $\mathbb{R}^n$. Orthogonal vectors are also linearly independent. Linearly independent vectors are not necessarily orthogonal.

• Any collection of $n$ linearly independent vectors is called a Basis.
• A basis is said to be Orthogonal Basis if $\mathbf{v_i} \perp \mathbf{v_j}$ for all vectors in the collection
• A basis is said to be an Orthonormal Basis if $\| \mathbf{v_i} \| = 1$
• The Standard Basis is the collection of orthonormal vectors $(e_1, e_2, \dots , e_n)$ such that $% $. The vectorspace $\mathbb{R}^n$ is equipped with the standard basis.

### Matrices

Consider two spaces $\mathbb{R}^n$ and $\mathbb{R}^k$. All linear functions from $\mathbb{R}^n$ to $\mathbb{R}^k$ may be described using a linear space of Real Matrices $\mathbb{R}^{k \times n}$ .

### Properties of Matrices

Let $A \in \mathbb{R}^{k \times n}$ and $B \in \mathbb{R}^{n \times m}$, and $\gamma$ a scalar in $\mathbb{R}$ . Let $\mathbf{v} \in \mathbb{R}^n$ and $\mathbf{u} \in \mathbb{R}^k$

• $A^T$ is the Transpose of a matrix.
• $A^T_{ij} := A_{ji}$ .
• $(A^T)^T = A$ .
• $(AB)^T = B^T A^T$ .
• Scalar Multiplication:
• Matrix Multiplication: Let $A \in \mathbb{R}^{k \times n}$ and $B \in \mathbb{R}^{n \times m}$. Then

Matrix multiplication is tricky and should be seriously practiced. Matrix multiplication is Associative (meaning $(A B) C = A (B C)$), but it is Non-Commutative (meaning $A B$ may not equal $B A$).

• Let $\mathbf{v} \in \mathbb{R}^n$ be a vector. Than $\mathbf{v} \cong V$ where $V \in \mathbb{R}^{n \times 1}$

• The Norm of a matrix is defined by

• There are different â€śkindsâ€ť of matrix norms. This norm is the Frobenius, or 2-norm. More information on norms can be found here.
• $\| A \| = \| A^T \|$ .
• Triangle Inequality: $\| A + B \| \leq \| A \| + \| B \|$

### Matrix Definitions

• The Range or Range Space of a matrix is defined as
• The Kernel or Null Space of a matrix is defined as

The kernel and the range of a matrix are subspaces of $\mathbb{R}^{n \times m}$

• The Rank of a matrix is defined as the minimum of the dimensions of the range space of $A$ and the range space of $A^T$.
• $\text{rank}(A) = \text{rank}(A^T)$ .
• A matrix is said to have Full Rank if the rank of $A$ equals $\min\{ n, m\}$
• The rank of a matrix is also equal to the number of Pivot Points it has when reduced. This video explains more.

### Square Matrices

Square Matrices have additional unique properties. Let $A \in \mathbb{R}^{n \times n}$

• The determinate of a square matrix is a special number with unique properties. The determinate of a $2 \times 2$ matrix $B$ is calculated by

calculating the determinate of larger matrices is more complex, but a nice tutorial can be found here.

• There exists the Identity Matrix $I$ such that
• If $A \mathbf{v} = \gamma \mathbf{v}$ holds for some vector $\mathbf{v} \in \mathbb{R}^n$ and scalar $\gamma \in \mathbb{R}$, then we call $\mathbf{v}$ a Eigenvector of $A$, and we call $\gamma$ an Eigenvalue of $A$.
• The set of Eigenvectors of $A$, corresponding to their eigenvalues, form a linear subspace of $\mathbb{R}^n$
• Every matrix $A \in \mathbb{R}^{n \times n}$ has $n$ eigenvalues (counted with multiplicity). Eigenvalues may be complex.
• The sum of the $n$ eigenvalues of $A$ is the same as the Trace of $A$ (that is, the sum of the diagonal elements of $A$).
• The product of the $n$ eigenvalues of $A$ is the determinate of $A$
• The roots of the Characteristic Equation $\det(A-\lambda I)$ are the eigenvalues of A.
• The norm of A is at least as large as the largest absolute value of itâ€™s eigenvalues.
• The eigenvalues of $A^{-1}$ are the inverses of the eigenvalues of $A$
• for square matrices, the eigenvalues of $A^T$ are the eigenvalues of $A$
• For a square matrix $A$ there can exist an Inverse Matrix $A^{-1}$, with $A A^{-1} = I$
• A invertible square matrix is called Nonsingular. Nonsingular matrices have the following properties, and the following properties imply a matrix is nonsingular:
• There exists itâ€™s inverse, $A^{-1} : A A^{-1} = I$
• The rows and columns of $A$ are linearly independent.
• The system of linear equations $A x = \mathbf{v}$ has a unique solution for all $\mathbf{v} \in \mathbb{R}^n$.
• The homogenous system of equations $A x = 0^{n}$ has only one solution, $x = 0^n \in \mathbb{R}^n$.
• $A^T$ is nonsingular.
• $\det(A) \neq 0$ .
• None of the eigenvalues of $A$ are 0.
• The set of all nonsingular $n \times n$ square matrices forms a Ring. This ring is non-commutative, and is therefore closed under addition and matrix multiplication.
• We call $A$ Symmetric if and only $A^T = A$. Symmetric matrices have the following properties:
• $A_{ij} = A_{ji}$ .
• Eigenvalues of symmetric matrices are real.
• Eigenvectors corresponding to distinct Eigenvalues are orthogonal to each other.
• The norm of a symmetric matrix equals the largest absolute value of itâ€™s eigenvalues.
• Even if $X$ is a non-square matrix, $X^T X$ and $X X^T$ are square symmetric matrices.
• If the columns of $X$ are linearly independent, then $X^T X$ is nonsingular.
• If the columns of $X^T$ are linearly independent, then $X X^T$ is nonsingular.
• The eigenvalues of $X^T X$ and $X X^T$ are the same, and are non-negative.
• We call $A$ a Positive Definite matrix if and only if for all $\mathbf{v} \in \mathbb{R}^n$, $\mathbf{v} \cdot A \mathbf{v} > 0$. We represent this with $A \succ 0$
• Respectively, we call $A$ a Negative Definite matrix, and $A \prec 0$ if and only if $% $
• Respectively, we call $A$ a Negative Semidefinite matrix, and $A \preceq 0$ if and only if $\mathbf{v} \cdot A \mathbf{v} \mathbf{\leq} 0$
• Respectively, we call $A$ a Positive Semidefinite matrix if, and $A \succeq 0$ and only if $\mathbf{v} \cdot A \mathbf{v} \mathbf{\geq} 0$
• $A$ is positive definite if and only if it has positive eigenvalues
• Respectively $A$ is negative definite if and only if it has negative eigenvalues
• Respectively $A$ is positive semidefinite if and only if it has non-negative eigenvalues
• Respectively $A$ is negative semidefinite if and only if it has non-positive eigenvalues
• For symmetric matrices $A$ and $B$, $A \succeq B$ if and only if $A - B \succeq 0$
• Respectively $A \succ B$ if and only if $A - B \succ 0$
• The sum of two positive definite matrices is also positive definite.
• Every positive definite matrix is nonsingular and invertible, and itâ€™s inverse is also positive definite
• If $A$ is positive definite and real $r > 0$, then $rA$ is positive definite
• Let $A\in\mathbb{R}^{n \times n}$ be a symmetric matrix. Then the determinant

for $1 \leq k \leq n$ is called the k-th principal minor of A.

• If $A\in\mathbb{R}^{n \times n}$ is a symmetric matrix, and if $\bigtriangleup_k$ is the k-th principal minor for $k = 1, \dots, n$ then
• $A$ is positive definite if $\bigtriangleup_k > 0$
• $A$ is negative definite if $(-1)^k \bigtriangleup_k > 0$. That is, the principal minors alternate sign starting with $% $
• If either of the above conditions applied for all but $\bigtriangleup_n = 0$, then $A$ is semidefinite.

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