ECE 515 - Control System Theory & Design

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Recap

Last time we discussed course logistics, course overview and introduced the state space formulation for linear control systems. In this lecture, as promised, we will do some review of the necessary linear algebra concepts.

Lin. algebra review - Part 1A

We assume almost everyone in class is familiar with the usual notion of vectors, either as geometric objects (common in physics) or as $n$ -tuples of numbers (common in computer science, also called arrays). In this note we make this notion a bit more axiomatic and formal so that we will get used to the mathematical machinery.

Fields

Formally, vectors are elements that belong to a vector space which are typically defined over fields.

Definition 1 (Field)
A field is a collection (or set) $F$ equipped with two binary operations (binary operations are mappings $F \times F \to F$ ) denoted $+$ and $\cdot$ called addition and (scalar) multiplication respectively such that they satisfy the field axioms:

Associativity and commutativity of multiplication and addition: $a + (b + c) = (a + b) + c and a \cdot (b \cdot c) = (a \cdot b) \cdot c$ $a + b = b + a and a \cdot b = b \cdot a$
Distributivity of multiplication over addition $a \cdot (b + c) = a \cdot b + a \cdot c$
Existence of multiplicative and additive identities. $\exists 0, 1 \in F such that a + 0 = a, a \cdot 1 = a \forall a \in F$
Existence of multiplicative and additive inverses $\forall a \in F, \exists y such that a + y = 0$ and $\forall a \in F - {0}, \exists z such that a z = 1$ Typically we write $- a$ for $y$ and $a^{- 1}$ for $z$ .

Familiar examples of fields include $R, C, Q$ , etc.

The integers $Z$ do not form a field, rather they form a ring.

Exercise 1 (Integers modulo p)
For a given prime number $p > 2$ , denote the set of integers modulo $p$ as $Z / p$ . For example, if $p = 5$ then $Z / 5 := {0, 1, 2, 3, 4}$ . Equip such a $Z / p$ with the binary operations $+_{p}$ and $\times_{p}$ where $x +_{p} y := (x + y) \mod p$ and $x \times_{p} y := (x \times y) \mod p$

Show that $Z / p$ forms a field.

Vector space

With the above definition out of the way, now we are in position to formally define a vector space.

Definition 2 (Vector space)
A vector space $V$ over a field $F$ (often denoted $V_{F}$ ) is a set equipped with operations $+$ and $\cdot$ called vector addition & scalar multiplication (by elements of $F$ ) satisfying the vector space axioms:

Vector addition is commutative and associative $a + b = b + a and a + (b + c) = (a + b) + c$
Multiplication is associative and distributive (over vector & field addition) $α \cdot (a + b) = α \cdot a + α \cdot b and α \cdot (β v) = (α β) \cdot v$
Existence of additive inverse and identity $\forall x \in V \exists z \in V such that x + z = x$ we denote such $z$ as $0$ the zero vector. $\forall x \in V \exists y such that x + y = 0$ we denote such $y$ as $- x$ .
Existence of multiplicative identity $\exists 1 \in F such that \forall x \in V, x \cdot 1 = x$

Here familiar examples of vector spaces include:

$n$ tuples of real numbers: $R^{n}$ (with $F = R$ )
$n$ tuples of complex numbers: $C^{n}$ (with $F = C$ )
$C (R, R)$ : The space of real valued continuous functions defined on the real line (what should be the field?)
The space of $n \times n$ matrices: $M_{n \times n}$ (what should be the field?)
$P^{n} [0, 1]$ : The space of polynomials upto order $n$ defined over $[0, 1]$ . That is, $t^{k}$ where $k = 0, 1, \dots, n$ and $t \in [0, 1]$ .
etc.

Subspace: A subspace $Y$ of a vector space $V$ is a subset of $V$ which is itself a vector space. In other words, if $x, y \in Y$ then $α x + β y \in Y$ for all $a, b \in F$ .

Examples: The symmetric $n \times n$ matrices form a subspace of $M_{n \times n}$ and the vectors $e_{1} = [\begin{matrix} 1 \\ 0 \\ 0 \end{matrix}], e_{2} = [\begin{matrix} 0 \\ 1 \\ 0 \end{matrix}]$ span the subspace that is the $x$ - $y$ plane in $R^{3}$ .

The span of a vector or a set of vectors is the space generated by their finite linear combinations.

A concept of paramount importance in linear algebra is that of linear independence and a basis. Informally, a set is linearly independent if no element of the set can be expressed using other elements of that set and a basis is some privileged linearly independent set which can be used to generate every element in the vector space. We formalize these definitions below.

Definition 3 (Linear independence, spanning set & dimension)
Linear independence: A set of vectors ${v_{1}, v_{2}, \dots, v_{n}}$ in a vector space is said to be linearly independent if $\sum_{k}^{n} α_{k} v_{k} = 0, α_{i} \in F ⟹ α_{i} = 0 \forall i$ Otherwise, it is said to be linearly dependent.

Spanning set: A set of vectors $S := {v_{1}, v_{2}, \dots, v_{n}}$ in a vector space is said to form a spanning set if every $x \in V$ can be written as $\sum_{k} α_{k} v_{k} = x where α_{i} \in F$ The set $S$ is said to be minimal if the removal of any element from $S$ causes $S$ to be no longer be a spanning set.

Dimension: The maximal numer of linearly independent vectors in $V$ is called the dimension of $V$ : denoted $\dim V$ .

Example: Consider the following vectors in $R^{n}$ : $e_{1} = [\begin{matrix} 1 \\ 0 \\ ⋮ \\ 0 \end{matrix}], e_{2} = [\begin{matrix} 0 \\ 1 \\ ⋮ \\ 0 \end{matrix}], \dots, e_{n} = [\begin{matrix} 0 \\ 0 \\ ⋮ \\ 1 \end{matrix}]$ They are obviously linearly independent. Moreover any other vector can be written as a linear combination of $e_{k}$ . Thus the dimension of $R^{n}$ is $n$ .

Proposition 1 (Basis proposition)
A set $B \subset V$ is a basis for $V$ if and only if:

It is linearly independent
$B$ is a spanning set for $V$ .

Then, every $v \in V$ can be written uniquely as a linear combination of elements of $B$ .

Infinite dimensional vector spaces?

Consider the vector space $P^{\infty} (R)$ : the space of all polynomials of finite order on $R$ . In this space, the functions $t^{k}, k = 0, 1, 2, \dots$ are obviously linearly independent (why?). However one can let $k$ get arbitrarily large. Thus dimension as defined above ill-suited for this space. For such infinite dimensional vector spaces a proper notion of basis for them is beyond the scope of this course (see ECE 513 Course Notes Chapter 4 if interested).

In this course we will stick with finite dimensional vector spaces.

Example: In $R^{n}$ the $e_{k}$ above form the standard basis. Every vector $v \in R^{n}$ can be written as:

$v = v_{1} e_{1} + v_{2} e_{2} + \dots v_{n} e_{n} = [\begin{matrix} v_{1} \\ v_{2} \\ ⋮ \\ v_{n} \end{matrix}]$

We say that the $v_{i}$ are the coordinates of $v$ with respect to the standard basis. Thus coordinates of a vector depend on a choice of basis.

Linear transformations

It is a common occurence in mathematics that rather than the spaces or objects defined to have some properties, the more interesting object of study is maps between them. The same remains true of vector spaces:

Definition 4 (Linear transformation)

Given two vector spaces $V$ and $W$ defined over the same field $F$ , a linear transformation is a map $A : V \mapsto W$ such that:

$A (α x + β y) = α A (x) + β A (y), x, y \in V α, β \in F$

Some authors (including the class notes) also use the term linear operator. It is a matter of notation, but I prefer to reserve the term operator for maps $f : V \mapsto V$ , i.e. transformations that map from a space to itself, also called endomorphisms.

We say that $V$ is the domain of $A$ and $W$ is the co-domain of $A$ .

Furthermore, the range of $A$ is a subset of $W$ defined as:

$R (A) := {w \in W : \exists v \in V s.t A (v) = w}$

Similarly, the kernel (also called nullspace) of $A$ is the subset of $X$ defined as:

$N (A) := {v \in V : A (v) = 0}$

Prove

R (A)

and

N (A)

are actually subspaces.

See Theorem 2.5.1 and 2.5.2 in the class notes.

We say that the dimension of $R (A)$ is the rank of $A$ .

Example: Consider the projection operator $P$ in $R^{2}$ that discards the $y$ -coordinate:

$\begin{aligned} P & : R^{2} \to R^{2} \\ : (x, y) \mapsto (x, 0) \end{aligned}$

What are $R (P)$ and $N (P)$ ?

Definition 5 (Direct sum)
Given two subspace $X$ and $Y$ we say $V$ is direct sum of $X$ and $Y$ , denoted $V = X \oplus Y$ if and only if,

$X \cap Y = {0}$
each $v \in V$ can be written uniquely as $v = x + y$ for $x \in X$ and $y \in Y$ .

Tip

Can you see that $R^{2} = R (P) \oplus N (P)$ for $P$ the projection operator defined above?

The nice thing about linear transformations in finite dimensional vector spaces is that essentially their study boils down largely to a study of the theory of matrices.

Matrix representation of transformations

Let $A : V \mapsto W$ be a linear transformation between vector space $V$ with basis ${v_{1}, v_{2}, \dots, v_{n}}$ and vector space $W$ with basis ${w_{1}, w_{2}, \dots, w_{m}}$ .

Claim: $A$ can be uniquely (with respect to the chosen bases) represented as a matrix.

To see why this is true, consider the action of $A$ on the basis elements of $V$ . We have $A (v_{1}) \in W$ and thus we can write: $A (v_{1}) = \sum_{j = 1}^{m} a_{j 1} w_{j} = a_{11} w_{1} + a_{21} w_{2} + \dots + a_{m 1} w_{m}$ for some coefficients $a_{j k} \in F$ .

Similarly, $\begin{matrix} (1) & A (v_{2}) = \sum_{j = 1}^{m} a_{j 2} w_{j}, A (v_{3}) = \sum_{j = 1}^{m} a_{j 3} w_{j}, \dots, A (v_{n}) = \sum_{j = 1}^{m} a_{j n} w_{j} \end{matrix}$

We would like to organize the $a_{j k}$ in such a way that the linear transformation $A$ can be identified with a matrix $M$ , such that premultiplying a $v \in V$ by $M$ produces the coordinates of $A (v) \in W$ .

Considering the mechanics of matrix multiplication, we see that the right dimension for $M$ is $m \times n$ . Collecting the coefficients on the RHS for each preceeding equality above as colum vectors we get:

$M = [\begin{matrix} a_{11} & a_{12} & \dots & a_{1 n} \\ a_{21} & a_{22} & \dots & a_{2 n} \\ ⋮ & ⋮ \\ a_{m 1} & a_{m 2} & \dots & a_{m n} \end{matrix}]$

Each column of $M$ above describes the action of $A$ on a basis element $v_{k}$ of $V$ i.e., is the coordinates of $A (v_{k})$ in the basis ${w_{1}, w_{2}, \dots, w_{m}}$ .

Did we accomplish our objective?

Let us check. Suppose $x \in V$ is some arbitrary vector: $x = \sum_{j}^{n} x_{j} v_{j}$

Its image under $A$ is $y = A (x) \in W$ . That is the coordinates of $A (x)$ with respect to the basis ${w_{1}, w_{2}, \dots, w_{m}}$ is given by $y$ :

$y = \sum_{k}^{m} y_{k} w_{k}$

We should make a distinction between $A (x)$ , the linear transformation $A$ acting on the vector $x$ and $M x$ , the premultiplication of vector $x$ by the matrix $M$ . It is a common abuse of notation to associate the linear transformation $A$ with a matrix, also of the same name: $A$ .

Claim: $y = A (x) = M x$ . To see why, write: $A (x) = A (\sum_{k}^{n} x_{k} v_{k}) = \sum_{k}^{n} x_{k} A (v_{k})$ But we know $A (v_{k})$ are the columns of $M$ . Thus, $A (x) = x_{1} [\begin{matrix} a_{11} \\ a_{21} \\ ⋮ \\ a_{m 1} \end{matrix}] + x_{2} [\begin{matrix} a_{12} \\ a_{22} \\ ⋮ \\ a_{m 2} \end{matrix}] + \dots + x_{n} [\begin{matrix} a_{1 n} \\ a_{22} \\ ⋮ \\ a_{m n} \end{matrix}]$ Collecting together we see: $A (x) = [\begin{matrix} a_{11} & a_{12} & \dots & a_{1 n} \\ a_{21} & a_{22} & \dots & a_{2 n} \\ ⋮ & ⋮ \\ a_{m 1} & a_{m 2} & \dots & a_{m n} \end{matrix}] [\begin{matrix} x_{1} \\ x_{2} \\ ⋮ \\ x_{n} \end{matrix}] = M x = y$

Caution

The matrix representation $M$ of some transformation $L$ depends on the basis chosen for its domain and co-domain and is thus coordinate dependent. The next Lecture deals with what happens when a change of basis is undertaken.

Exercise 2 (Matrix representaion for projection)
Choose the standard basis $e_{1}$ and $e_{2}$ for $R^{2}$ . What is the matrix representation of the projection operator $\Pr$ defined last lecture?

$\begin{aligned} \Pr & : R^{2} \to R^{2} \\ : (x,) \mapsto (x, 0) \end{aligned}$

(For reasons that will become obvious later, we are now using $\Pr$ to refer to the projection operator.)

Exercise 3 (Range & kernel in terms of $M$ )
We previously defined the range and kernel of a linear transformation $A$ . What is the range and kernel of $A$ in terms of the matrix $M$ ?

From now on, we will also adopt the familiar abuse of notation and may use the same letter to refer to a linear transformation and its matrix representation interchangeably.

But we will pick up that thread next lecture.

--- Title: Lecture 02 - 01/18 code-tools: source: true toggle: false --- ::: {.callout-note} For students who intend to scribe, you can see the source code for this page by clicking the code button above this callout. The source is compatible with the [Quarto](https://quarto.org/) publishing system. Scribed by: _itabrah2_ ::: ## Recap Last time we discussed course logistics, course overview and introduced the state space formulation for linear control systems. In this lecture, as promised, we will do some review of the necessary linear algebra concepts. ## Lin. algebra review - Part 1A We assume almost everyone in class is familiar with the usual notion of vectors, either as geometric objects (common in physics) or as $n$-tuples of numbers (common in computer science, also called arrays). In this note we make this notion a bit more axiomatic and formal so that we will get used to the mathematical machinery. ### Fields Formally, vectors are elements that belong to a _vector space_ which are typically defined over _fields_. :::{#def-field} ## Field A **field** is a collection (or set) $F$ equipped with two binary operations (binary operations are mappings $F \times F \to F$) denoted $+$ and $\cdot$ called addition and (scalar) multiplication respectively such that they satisfy the _field axioms_: * _Associativity_ and _commutativity_ of multiplication and addition: $$ a + \left( b + c \right) = \left( a + b \right) + c \quad \textrm{and} \quad a \cdot \left( b \cdot c \right) = \left( a \cdot b \right) \cdot c $$ $$ a + b = b + a \qquad \textrm{and} \qquad a \cdot b = b \cdot a $$ * _Distributivity_ of multiplication over addition $$ a \cdot \left(b + c \right) = a \cdot b + a \cdot c $$ * Existence of multiplicative and additive _identities_. $$ \exists 0, 1 \in \mathbb{F} \qquad \textrm{such that} \qquad a + 0 = a, \quad a \cdot 1 = a \quad \forall a \in \mathbb{F}$$ * Existence of multiplicative and additive _inverses_ $$ \forall a \in F, \quad \exists y \quad \textrm{such that} \quad a + y = 0 $$ and $$ \forall a \in F - \{0\}, \quad \exists z \quad \textrm{such that} \quad a z = 1 $$ Typically we write $-a$ for $y$ and $a^{-1}$ for $z$. ::: Familiar examples of fields include $\mathbb{R}, \mathbb{C}, \mathbb{Q}$, etc. ::: {.column-margin} The integers $\mathbb{Z}$ do not form a field, rather they form a [ring](https://en.wikipedia.org/wiki/Ring_(mathematics)). ::: ::: {#exr-integersmodp} ## Integers modulo p For a given prime number $p>2$, denote the set of integers modulo $p$ as $\mathbb{Z}/p$. For example, if $p=5$ then $\mathbb{Z}/5 := \{0, 1, 2, 3, 4\}$. Equip such a $\mathbb{Z}/p$ with the binary operations $+_{p}$ and $\times _p$ where $$x +_p y := (x + y) \mod p$$ and $$x \times_p y := (x \times y ) \mod p$$ Show that $\mathbb{Z}/p$ forms a field. ::: ### Vector space With the above definition out of the way, now we are in position to formally define a vector space. :::{#def-vspace} ## Vector space A **vector space** $V$ over a field $\mathbb{F}$ (often denoted $V_{\mathbb{F}}$) is a set equipped with operations $+$ and $\cdot$ called vector addition & scalar multiplication (by elements of $\mathbb{F}$) satisfying the _vector space axioms_: * Vector addition is commutative and associative $$ a + b = b + a \qquad \textrm{and} \qquad a + \left( b + c \right) = \left( a + b\right) + c $$ * Multiplication is associative and distributive (over vector & field addition) $$ \alpha \cdot \left(a + b \right) = \alpha \cdot a + \alpha \cdot b \qquad \textrm{and} \qquad \alpha \cdot (\beta v) = \left(\alpha \beta \right) \cdot v $$ * Existence of additive inverse and identity $$\forall x \in V \quad \exists \mathbf{z} \in V \quad \textrm{such that} \quad x + z = x $$ we denote such $z$ as $\mathbf{0}$ the zero vector. $$\forall x \in V \quad \exists y \quad \textrm{such that} \quad x + y = \mathbf{0} $$ we denote such $y$ as $-x$. * Existence of multiplicative identity $$ \exists 1 \in \mathbb{F} \quad \textrm{such that} \quad \forall x \in V, \quad x \cdot 1 = x $$ ::: Here familiar examples of vector spaces include: * $n$ tuples of real numbers: $\mathbb{R}^n$ (with $\mathbb{F} = \mathbb{R}$) * $n$ tuples of complex numbers: $\mathbb{C}^n$ (with $\mathbb{F}= \mathbb{C}$) * $\mathbf{C} \left(\mathbb{R}, \mathbb{R}\right)$: The space of real valued continuous functions defined on the real line (what should be the field?) * The space of $n \times n$ matrices: $\mathbf{M}_{n \times n}$ (what should be the field?) * $\mathbb{P}^n[0, 1]$: The space of polynomials upto order $n$ defined over $[0, 1]$. That is, $t^k$ where $k=0, 1, \dots, n$ and $t \in [0, 1]$. * etc. **Subspace:** A subspace $Y$ of a vector space $V$ is a subset of $V$ which is itself a vector space. In other words, if $x, y \in Y$ then $\alpha x + \beta y \in Y$ for all $a, b \in \mathbb{F}$. **Examples:** The symmetric $n \times n$ matrices form a subspace of $\mathbf{M}_{n \times n}$ and the vectors $$ e_1 = \begin{bmatrix} 1 \\ 0 \\\ 0 \end{bmatrix}, \quad e_2 = \begin{bmatrix} 0 \\1 \\ 0 \end{bmatrix} $$ span the subspace that is the $x$-$y$ plane in $\mathbb{R}^3$. ::: {.column-margin} The **span** of a vector or a set of vectors is the space generated by their finite linear combinations. ::: A concept of paramount importance in linear algebra is that of _linear independence_ and a _basis_. Informally, a set is linearly independent if no element of the set can be expressed using other elements of that set and a basis is some privileged linearly independent set which can be used to generate every element in the vector space. We formalize these definitions below. :::{#def-lin_indep} ## Linear independence, spanning set & dimension **Linear independence:** A set of vectors $\{v_1, v_2, \dots, v_n\}$ in a vector space is said to be linearly independent if $$ \sum \limits _{k} ^{n} \alpha_k v_k = 0 , \; \alpha_i \in \mathbb{F} \quad \implies \quad \alpha_i = 0 \; \forall i $$ Otherwise, it is said to be linearly dependent. **Spanning set:** A set of vectors $S:=\{v_1, v_2, \dots, v_n\}$ in a vector space is said to form a spanning set if every $x \in V$ can be written as $$ \sum \limits _k \alpha _k v_k = x \quad \textrm{where} \quad \alpha_i \in \mathbb{F} $$ The set $S$ is said to be minimal if the removal of any element from $S$ causes $S$ to be no longer be a spanning set. **Dimension:** The maximal numer of linearly independent vectors in $V$ is called the _dimension_ of $V$: denoted $\dim V$. ::: **Example:** Consider the following vectors in $\mathbb{R}^n$: $$ e_1 = \begin{bmatrix}1 \\ 0 \\ \vdots \\ 0 \end{bmatrix}, \quad e_2 = \begin{bmatrix}0 \\ 1 \\ \vdots \\ 0 \end{bmatrix}, \quad \dots, \quad e_n = \begin{bmatrix}0 \\ 0 \\ \vdots \\ 1 \end{bmatrix} $$ They are obviously linearly independent. Moreover any other vector can be written as a linear combination of $e_k$. Thus the dimension of $\mathbb{R}^n$ is $n$. ::: {#prp-basislemma} ## Basis proposition A set $B \subset V$ is a **basis** for $V$ _if and only if_: 1. It is linearly independent 2. $B$ is a spanning set for $V$. Then, every $v \in V$ can be written _uniquely_ as a linear combination of elements of $B$. ::: :::{.callout-caution collapse="true"} ## Infinite dimensional vector spaces? Consider the vector space $\mathbb{P}^{\infty}\left(\mathbb{R}\right)$: the space of all polynomials of finite order on $\mathbb{R}$. In this space, the functions $t^k, k = 0, 1, 2, \dots$ are obviously linearly independent (why?). However one can let $k$ get arbitrarily large. Thus _dimension_ as defined above ill-suited for this space. For such _infinite_ dimensional vector spaces a proper notion of basis for them is beyond the scope of this course (see ECE 513 Course Notes Chapter 4 if interested). In this course we will stick with finite dimensional vector spaces. ::: **Example:** In $\mathbb{R}^n$ the $e_k$ above form the _standard basis_. Every vector $v \in \mathbb{R}^n$ can be written as: $$ v = v_1 e_1 + v_2 e_2 + \dots v_n e_n = \begin{bmatrix} v_1 \\ v_2 \\ \vdots \\ v_n \end{bmatrix} $$ We say that the $v_i$ are the **coordinates** of $v$ _with respect to the standard basis_. Thus coordinates of a vector depend on a choice of basis. ### Linear transformations It is a common occurence in mathematics that rather than the spaces or objects defined to have some properties, the more interesting object of study is maps between them. The same remains true of vector spaces: ::: {#def-lin_transform} ## Linear transformation Given two vector spaces $V$ and $W$ defined over the same field $\mathbb{F}$, a **linear transformation** is a map $A: V \mapsto W$ such that: $$ A (\alpha x + \beta y ) = \alpha A(x) + \beta A(y), \quad x, y \in V \quad \alpha, \beta \in \mathbb{F} $$ ::: :::{.column-margin} Some authors (including the class notes) also use the term _linear operator_. It is a matter of notation, but I prefer to reserve the term _operator_ for maps $f: V \mapsto V$, i.e. transformations that map from a space to itself, also called _endomorphisms_. ::: We say that $V$ is the _domain_ of $A$ and $W$ is the _co-domain_ of $A$. Furthermore, the **range** of $A$ is a subset of $W$ defined as: $$ R(A) := \left\{w \in W : \exists v \in V \; \textrm{s.t} \; A(v) = w \right\} $$ Similarly, the **kernel** (also called _nullspace_) of $A$ is the subset of $X$ defined as: $$ N(A) := \left\{ v \in V: \; A(v) =0 \right\} $$ ::: {.callout-tip collapse="true"} ## Prove $R(A)$ and $N(A)$ are actually subspaces. See Theorem 2.5.1 and 2.5.2 in the class notes. ::: We say that the dimension of $R(A)$ is the **rank** of $A$. **Example:** Consider the projection _operator_ $P$ in $\mathbb{R}^2$ that discards the $y$ -coordinate: $$ \begin{align} P &: \mathbb{R}^2 \to \mathbb{R}^2 \\ &: (x, y) \mapsto (x, 0) \end{align} $$ What are $R(P)$ and $N(P)$? ::: {#def-directsum} ## Direct sum Given two subspace $X$ and $Y$ we say $V$ is **direct sum** of $X$ and $Y$, denoted $V=X \oplus Y$ _if and only if_, 1. $X \cap Y = \{0\}$ 2. each $v \in V$ can be written uniquely as $v = x + y$ for $x \in X$ and $y \in Y$. ::: ::: {.callout-tip} Can you see that $\mathbb{R}^2 = R(P) \oplus N(P)$ for $P$ the projection operator defined above? ::: The nice thing about linear transformations in finite dimensional vector spaces is that essentially their study boils down largely to a study of the theory of matrices. ### Matrix representation of transformations Let $A: V \mapsto W$ be a linear transformation between vector space $V$ with basis $\{v_1, v_2, \dots, v_n\}$ and vector space $W$ with basis $\{w_1, w_2, \dots, w_m\}$. **Claim: $A$ can be uniquely (with respect to the chosen bases) represented as a matrix.** To see why this is true, consider the action of $A$ on the basis elements of $V$. We have $A(v_1) \in W$ and thus we can write: $$ A(v_1) = \sum \limits _{j=1}^m a_{j1}w_j = a_{11}w_1 + a_{21}w_2 + \dots + a_{m1}w_m $$ for some coefficients $a_{jk} \in \mathbb{F}$. Similarly, $$ A(v_2) = \sum \limits _{j=1}^m a_{j2}w_j , \quad A(v_3) = \sum \limits _{j=1}^m a_{j3}w_j, \quad \dots, \quad A(v_n) = \sum \limits _{j=1}^m a_{jn}w_j $$ {#eq-1} We would like to organize the $a_{jk}$ in such a way that the linear transformation $A$ can be identified with a matrix $M$, such that premultiplying a $v \in V$ by $M$ produces the coordinates of $A(v) \in W$. Considering the mechanics of matrix multiplication, we see that the right dimension for $M$ is $m \times n$. Collecting the coefficients on the RHS for each preceeding equality above as colum vectors we get: $$ M = \begin{bmatrix} a_{11} & a_{12} & \dots & a_{1n} \\ a_{21} & a_{22} & \dots & a_{2n} \\ \vdots & & & \vdots \\ a_{m1} & a_{m2} & \dots & a_{mn} \end{bmatrix} $$ Each column of $M$ above describes the action of $A$ on a basis element $v_k$ of $V$ i.e., is the coordinates of $A(v_k)$ in the basis $\{w_1, w_2, \dots, w_m\}$. Did we accomplish our objective? Let us check. Suppose $x \in V$ is some arbitrary vector: $$ x = \sum \limits _j ^n x_j v_j $$ Its image under $A$ is $y = A(x) \in W$. That is the coordinates of $A(x)$ with respect to the basis $\{w_1, w_2, \dots, w_m\}$ is given by $y$: $$ y = \sum \limits _k ^m y_k w_k $$ :::{.column-margin} We should make a distinction between $A(x)$, the linear transformation $A$ _acting_ on the vector $x$ and $Mx$, the premultiplication of vector $x$ by the matrix $M$. It is a common abuse of notation to associate the linear transformation $A$ with a matrix, also of the same name: $A$. ::: **Claim: $y = A(x) = Mx$**. To see why, write: $$ A(x) = A\left( \sum \limits _k^n x_kv_k \right) = \sum \limits _k ^n x_k A(v_k) $$ But we know $A(v_k)$ are the columns of $M$. Thus, $$ A(x) = x_1 \begin{bmatrix} a_{11} \\ a_{21} \\ \vdots \\ a_{m1} \end{bmatrix} + x_2 \begin{bmatrix} a_{12} \\ a_{22} \\ \vdots \\ a_{m2} \end{bmatrix} + \dots + x_n \begin{bmatrix} a_{1n} \\ a_{22} \\ \vdots \\ a_{mn} \end{bmatrix} $$ Collecting together we see: $$ A(x) = \begin{bmatrix} a_{11} & a_{12} & \dots & a_{1n} \\ a_{21} & a_{22} & \dots & a_{2n} \\ \vdots & & & \vdots \\ a_{m1} & a_{m2} & \dots & a_{mn} \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \end{bmatrix} = Mx = y $$ ::: {.callout-caution} The matrix representation $M$ of some transformation $L$ depends on the basis chosen for its domain and co-domain and is thus _coordinate dependent_. The next Lecture deals with what happens when a change of basis is undertaken. ::: ::: {#exr-projmat} ## Matrix representaion for projection Choose the standard basis $e_1$ and $e_2$ for $\mathbb{R}^2$. What is the matrix representation of the projection operator $\operatorname{Pr}$ defined last lecture? $$ \begin{align} \operatorname{Pr} &: \mathbb{R}^2 \to \mathbb{R}^2 \\ &: \left(x, \right) \mapsto \left(x, 0 \right) \end{align} $$ (For reasons that will become obvious later, we are now using $\operatorname{Pr}$ to refer to the projection operator.) ::: ::: {#exr-rangeker} ## Range & kernel in terms of $M$ We previously defined the range and kernel of a linear transformation $A$. What is the range and kernel of $A$ in terms of the matrix $M$? ::: From now on, we will also adopt the familiar abuse of notation and may use the same letter to refer to a linear transformation and its matrix representation interchangeably. But we will pick up that thread next lecture.