Vector Space: a vector space is a set of vectors V together with an addition and scalar multiplication operation, satisfying the following 10 axioms:
// QUESTION: why not closed under multiplication // QUESTION: why not 0 * A = 0
Example: For a, b, c, d, ... \in \mathbb{R}
R^{2 \times 2} = \begin{pmatrix} a & b\\ c & d\\ \end{pmatrix} is a vector space.
(a_1x^2 + b_1x + c_1)+(a_2x^2 + b_2x + c_2)=(a_1+a_2)x^2 + (b_1+b_2)x + (c_1 + c_2) is a vector space.
d(a_1x^2 + b_1x + c_1)=da_1x^2 + db_1x + dc_1 is a vector space.
\forall n \in \mathbb{R}, R^n is a subspace (assume without proof)
Scalar Vector Theorem: For any v \in V and scalar c\in\mathbb{R}
Subspace: A subset w \subseteq V is a subspace with the operations on V
w = \{kv | k\in\mathbb{R}\} for a fixed v \neq 0 is a subspace (note: line or plane must go through origin)
w = \{\begin{pmatrix} x\\ y\\ z\\ \end{pmatrix} | ax + by + cz = 0\} is a subspace (note: line or plane must go through origin)
Example:
V = \begin{pmatrix} a & b & c\\ d & e & f\\ g & h & i\\ \end{pmatrix} \in \mathbb{R}^{3 \times 3}
then triangular matrix W = \begin{pmatrix} x & 0 & 0\\ y & z & 0\\ w & p & q\\ \end{pmatrix} is a subspace of V
Subspace Theorem: If V is a vector space, then W is a subspace of V iff
Polynomial Solution Subspace: let A \in \mathbb{R}^{m \times n} then the set of solutions to Ax, \{x \in \mathbb{R}^n | Ax = 0\}, is a subspace of R^n
Nullspace: nullspace / kernel of A \in \mathbb{R}^{m \times n} is N(A) = \{x \in \mathbb{R}^n | Ax = 0\}
Column Space: all possible set of output of a transformation, space where columns vectors live
Trivial Subspace: A subspace W \subseteq V is a trivial subspace of V if W = \{0\} \lor W = V
Linear Combination: A linear combination of a set of vectors V' \subseteq V \subseteq R^{n} (V is a vector space) is an expression of the form C \cdot V' (c_1v_1+c_2v_2+...+c_nv_n) for some scalar vector C \in \mathbb{R}^n
Trivial Linear Combination: \{\text{expression}(C \cdot V) | (\forall c \in C)(c = 0)\} is trivial linear combination
Span: the span of S \subseteq V \subseteq R^{n}, denoted \text{span}(S), (V is a vector space) is the set of all linear combinations of S
Span: the span of s \in S for S \subseteq V is \text{span}(s) = \{cs | c \in \mathbb{R}^n\}.
Hanke's Conjecture of Subspace: Given subspace A \in \mathbb{R}^{n} and (n-1)-many vectors v_1, v_2, ..., v_{n-1} \in A, a non-trivial subspace is the hyper-plane formed by those vectors.
Spanning Set: S is a spanning set of V if V = \text{span}(S). (S spans V)
Column Space: The column space of A \in \mathbb{R}^{m \times n} is C(A) = \text{span}(\{A^T[i] | (\forall i)(1 \leq i \leq n) \}). (the subspace of R^{?} spanned by the columns of A.)
Linear independence: a set of vectors is linearly independent iff c_1v_1 + c_2v_2 + ... + c_nv_n = 0 \implies c_1 = c_2 = ... = c_n = 0 (or CV = 0 \implies C = \overrightarrow{0})
Let w \in \text{span}(V), then \{w\} \cup V is linearly dependent. (Let w \notin \text{span}(V), then \{w\} \cup V is linearly independent.)
If V is linearly dependent, there exists w \in V such that w \in \text{span}(V - \{w\})
v_1, v_2, ..., v_n \in V are linearly independent iff \forall w \in \text{span}(V), w can be expressed as a unique linear combination of V.
If v_1, v_2, ..., v_n are linearly independent, but v_1, v_2, ..., v_n, m are linearly dependent, then m \in \text{span}(\{v_1, ..., v_n\})
Let A \in \mathbb{R}^{n \times n}, then the followings are equivalent:
A is invertible
N(A) = \{0\} (nullspace)
column space of A are linearly independent
rows of A are linearly independent
Ax = b has a unique solution for every b \in \mathbb{R}^n (A is Non-Singular Matrix)
The only solution for Ax = 0 is x = 0
Using row operation on Ax = 0 can reach \begin{pmatrix} x_1 & 0 & 0\\ 0 & x_2 & 0\\ 0 & 0 & x_3\\ \end{pmatrix} = 0
Using row operation on Ax = b can reach \begin{pmatrix} x_1 & 0 & 0\\ 0 & x_2 & 0\\ 0 & 0 & x_3\\ \end{pmatrix} = \text{ something}
A is a product of elementary matrices
Basis (inverse span): B \subseteq V is a bases of V if 1. the vectors in B are linearly independent and 2. \text{span}(B) = V
((\forall b \in B)(|B| = \text{dim}(b)))
B is a basis of V if \forall v \in V can be written as a unique linear combination of the vectors in B ((\forall v \in V)(\exists C \in \mathbb{R}^{n \times n})(CB = v))
Any two basis of a vector V has the same number of vectors (The cardinality of any basis in R^n is n)
Find Basis: choose one variable to be 1, others to be 0 until there is a constraint. Repeat process for many of variables. Or you can write in explicit form.
Explicit Vector Space: write in a way that the number of variables is the dimension.
Minimum spanning set, maximum independent vector in the set.
Rank: how many dimension you get at maximum after applying transformation
Dimension: number of vectors in any of V's basis
\text{dim}(\mathbb{R}^{n \times n}) = n^2
\text{dim}(\{0\}) = 0
Dimensional Theorem:
Any linear independent set of vector can be extended to a basis by adding vectors to it. (keep adding independent vectors increase dimension)
For any spanning set we can always leave vectors to get a bases.
Isomorphism: writing 2 \times 2 matrix in vector space is the same as 4-dimensional vectors. (proof needed)
Four Fundamental Subspace: Let A \in \mathbb{R}^{n \times n}
columnspace: C(A) is the space spanned by the columns of A
rowspace: RS(A) of A is the space spanned by the rows of A
nullspace: N(A) of A is the nullspace of A (Ax = 0 where x \in \mathbb{R}^n)
left nullspace: N(A^T) = \{x | A^T x = 0\} = \{x | x^TA = 0\} where x \in \mathbb{R}^n
Column-Rank: the dimension of A \in \mathbb{R}^{m \times n} column space. (maximum possible number of linear independent columns of A) Row-Rank: Dimension of RS(A) (maximum possible number of linear independent rows of A)
For any A \subseteq \mathbb{R}^{m \times n}, \text{row-rank}(A) = \text{column-rank}(A)
Rank: column-rank r_k(A), \text{rank}(A), r(A)
Gaussian Elimination: let A \rightarrow U \rightarrow R, then RS(A) = RS(U) = RS(R)
Rank Theorems:
Corollary:
Find RS(A): pick non-zero row of rref(A) (begin with 1) Find C(A): pick pivot columns of rref(A) and look up in A
Extension of theorem of non-singular matrices: Let A \in \mathbb{R}^{n \times n}, then te followings are equivalent
Linear Transformation: a linear transformation is a function T: V \rightarrow W where V, W are vector spaces such that
(\forall v \in V, \alpha \in \mathbb{R})(T(\alpha v) = \alpha T(v))
To check if T is linear transformation, check T(\alpha v + \beta w) = \alpha T(v) + \beta T(w)
Simple Linear Transform:
Scaling: \begin{pmatrix} c & 0\\ 0 & c\\ \end{pmatrix}
Reflection: \begin{pmatrix} 0 & 1\\ 1 & 0\\ \end{pmatrix}
Rotation 90 degree: \begin{pmatrix} 0 & -1\\ 1 & 0\\ \end{pmatrix}
Projection on x-axis: \begin{pmatrix} 1 & 0\\ 0 & 0\\ \end{pmatrix}
Projection on x-axis reduce dimension \begin{pmatrix} 1 & 0\\ \end{pmatrix}
Rotation: \begin{pmatrix} \cos \theta & -\sin \theta\\ \sin \theta & \cos \theta\\ \end{pmatrix}
(you have to check if it fits the definition of linear transformation)
Matrix is Transform Theorem: for any A \in R^{n \times n}, then T_A : \mathbb{R}^n \rightarrow \mathbb{R}^n is a linear transformation.
Weak Transform Basis Theorem: If T: V \rightarrow W is a linear transformation and B = \{v_1, ..., v_n\} \subseteq V is a basis for V, and T(v_1), ... T(v_n) are determined such that T(v_1) = w_1, ..., T(v_n) = w_n, then T is determined on the whole V.
Change Basis Transform Theorem: Let \{v_1, ..., v_n\} be basis of V, and arbitrary v = c_1v_1 + ... + c_nv_n, w_1, ..., w_n \in W then T(v) = c_1w_1 + ... c_nw_n is a linear transformation.
Linear Transformation and Matrix:
every matrix A \in \mathbb{R}^{m \times n} describes a linear transformation T : \mathbb{R}^n \rightarrow \mathbb{R}^m.
every linear transformation T : \mathbb{R}^n \rightarrow \mathbb{R}^m can be described as a matrix multiplication with A \in \mathbb{R}^{m \times n}
Coordinate System:
given vector space V
given V's bases B(V) = \{v_1, ..., v_n\}
then coordinate system can be represented by matrix B = \begin{pmatrix} v_1 & ... & v_n\\ \end{pmatrix}
the coordinate vector C is: BC = \text{... the actual numerical vector ...}
Find Transformation:
given B = \{\begin{pmatrix}2\\3\\\end{pmatrix}, \begin{pmatrix}1\\1\\\end{pmatrix}\}, C = \{\begin{pmatrix}1\\1\\2\\\end{pmatrix}, \begin{pmatrix}2\\2\\3\\\end{pmatrix}, \begin{pmatrix}3\\2\\1\\\end{pmatrix}\} and \begin{cases}T(B_1) = 2C_1 + C_2 - C_3\\T(B_2) = C_1 - C_2 + 3C_3\\\end{cases}
to use T in normal coordinate:
to find T for normal coordinate: do the same thing for unit vectors for normal coordinate system
Change of Basis:
given basis B = \{v_1, ..., v_n\}, C = \{w_1, ..., w_n\}
Chang of basis matrix P_{B \rightarrow C} = \begin{pmatrix}(v_1)_c & (v_2)_c & ... & (v_n)_c\\\end{pmatrix}
P_{B \rightarrow B}P_{C \rightarrow B} = I and it is always invertible (since they are the same dimension).
Composing Linear Transformation:
Inverse of Transformation: The inverse of a linear transformation T: V \rightarrow W is T^{-1}: W \rightarrow V.
Transformation is invertible iff matrix is invertible
Projections are generally not invertible
Projections onto line not through origin is not linear transformation
Projection onto rotation angle: \begin{pmatrix} \cos^2 \theta & \sin \theta \cos \theta \\ \sin \theta \cos \theta & \sin^2 \theta \\ \end{pmatrix}
Kernel: \text{Ker}(T) = \{v | T(v) = 0\}
\text{Ker}(T) = \text{Ker}(N) if T is a matrix
\text{Ker}(T) are subspace of T: \mathbb{R}^n \rightarrow \mathbb{R}^n
Image: \text{Img}(T) = \{w \in W | (\exists v \in V)(T(v) = w)\}
\text{Img}(T) = C(T) if T is a matrix
\text{Img}(T) are subspace of T: \mathbb{R}^n \rightarrow \mathbb{R}^n
kernel (line) is orthogonal to image (plane) at origin
Theorem: \text{dim}(\text{Img}(T_A)) + \text{dim}(\text{Ker}(T_A)) = n (where n is the dimension of starting space)
Injective: T(v) = T(w) \iff v = w
rotation, scaling is injective
projection is not injective
Increase dimension is injective, but not surjective
Surjective: transformation T is surjective if (\forall w \in W)(\exists v \in W)(T(v) = w)
rotation, scaling is surjective
projection of same dimension (T: \mathbb{R}^3 \rightarrow \mathbb{R}^3) is not surjective
projection of reduced dimension (T: \mathbb{R}^3 \rightarrow \mathbb{R}^2) is surjective, but not injective
T^{m \times n} : \mathbb{R}^n \rightarrow \mathbb{R}^m | injective | not injective |
---|---|---|
surjective | rank(T) = n | rank(T) = m (m<n) |
not surjective | rank(T) = n (m>n) | rank(T) < m, n |
Orthogonality:
x \perp y \iff x \cdot y = \|x\| \cdot \|y\| \cdot \cos \theta = 0
orthogonal set: (\forall v_i, v_j \in V)(i \neq j \implies v_i \cdot v_j = 0)
orthogonal basis of V: an orthogonal set that is also a basis of V
Orthonormal Basis: an orthogonal basis in which every vector is a unit vector.
Any set \{v_1, ..., v_n\} \subseteq \mathbb{R}^n of orthogonal vectors vectors (with v_i \neq 0) is linearly independent.
v \perp W: v \in \mathbb{R}^n is orthogonal to subspace W \subseteq \mathbb{R}^n if (\forall w \in W)(v \perp w)
V \perp W: V \subseteq \mathbb{R}^n is orthogonal to subspace W \subseteq \mathbb{R}^n if (\forall v \in V)(\forall w \in W)(v \perp w)
(x-y-plane and y-z-plane are not orthogonal because they have intersections that is not \{\overrightarrow{0}\}, converse is not true)
If V = span(\{v_1, ..., v_m\} \in \mathbb{R}^m), W = span(\{w_1, ..., w_n\} \in \mathbb{R}^n) and (\forall 1 \leq i \leq m, 1 \leq j \leq n)(v_i \perp v_j), then V \perp W (proof: v \cdot w = (\sum_{i=1}^m \alpha_i v_i)(\sum_{j=1}^n \beta_i w_j) = \sum_{1 \leq i \leq m, 1 \leq j \leq n} \alpha_i \beta_j v_i \cdot w_j = 0)
Orthogonal Complement: subspace of all vectors \{w \in \mathbb{R}^n | (\forall v \in V \subseteq \mathbb{R}^n)(w \perp v)\}
dimension of subspace and its complement must equal to n. (\text{dim}(V) + \text{dim}(V^\perp) = n)
V^\perp = W \iff W^\perp = V
Theorem: For any matrix A \subseteq \mathbb{R}^{m \times n}
RS(A)^\perp = N(A)
C(A)^\perp = \text{left-nullspace}(A)
Lemma: N(A) \perp RS(A)
Finding Orthogonal Complement:
Projection to Line: project b onto a is T(b) = \frac{a \cdot b}{a \cdot a} a
Projection to plane: let \{b_1, b_2\} be an orthogonal basis of the plane. T = \frac{b_1 \cdot v}{b_1 b_1} b_1 + \frac{b_2 \cdot v}{b_2 b_2} b_2
in general T = \frac{b_1 \cdot v}{b_1 b_1} b_1 + \frac{b_2 \cdot v}{b_2 b_2} b_2 + ... + \frac{b_k \cdot v}{b_k b_k} b_k
in general, for matrix: P_W = P_{b_1} + P_{b_2} + ... + P_{b_k}
Gram-schmidt orthogonal: any subset of \mathbb{R}^n has an orthogonal basis.
Gram-schmidt process
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