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개발로 하는 개발
[LG Aimers] Module 2. Mathematics for ML -1 본문
1. Determinant
- Definition
For $A=\begin{pmatrix}a_{11}a_{12}\\a_{21}a_{22}\end{pmatrix},A^{-1}=\frac{1}{a_{11}a_{12}-a_{21}a_{22}}\cdot\begin{pmatrix}a_{22}-a_{12}\\-a_{21}a_{22}\end{pmatrix}$
A is invertible if $\frac{1}{a_{11}a_{22}-a_{21}a_{12}}\neq 0$
$\therefore det(A) = \begin{vmatrix}a_{11}a_{12}\\a_{21}a_{22}\\\end{vmatrix} = \frac{1}{a_{11}a_{12}-a_{21}a_{22}}$
- for $3\times3$ matrix,
\begin{vmatrix}a_{11}a_{12}a_{13}\\a_{21}a_{22}a_{23}\\a_{31}a_{32}a_{33}\\\end{vmatrix}
$= a_{11}a_{22}a_{33}+a_{21}a_{32}a_{13}+a_{31}a_{12}a_{23}-a_{31}a_{22}a_{13}-a_{11}a_{32}a_{23}-a_{21}a_{12}a_{33}a_{11}$
- Laplace expansion
$\begin{vmatrix}a_{11}a_{12}a_{13}\\a_{21}a_{22}a_{23}\\a_{31}a_{32}a_{33}\\\end{vmatrix}$
$=(-1)^{1+1}det(A_{1,1})+a_{12}(-1)^{1+2}det(A_{1,2})+a_{13}(-1)^{1+3}det(A_{1,3})$
where $A_{k, j}$ is the submatrix of A that we obtain when deleting row k and column j.
- Formal Definition
For a matrix A $\mathbb{R}^{n\times n}$, for all j = 1,...,n,
1. Expansion along column j: $det(A) = \sum_{k=1}^{n}(-1)^{k+j}\cdot a_{kj}\cdot det(A_{k,j})$
2. Expansion along row j: $det(A) = \sum_{k=1}^{n}(-1)^{k+j}\cdot a_{jk}\cdot det(A_{j,k})$
- Properties of Determinant
2. Trace
- Definition
the trace of a square matric $A\in \mathbb{R}^{n\times n}$ is defined as $tr(A)\overset{\underset{\mathrm{def}}{}}{=}\sum_{i=1}^{n}a_{ii}$
- Properties
$tr(A+B)=tr(A) + tr(B)$
$tr(\alpha A) = \alpha tr(A)$
$tr(I_{n}) = n$
3. Eigenvalue and Eigenvector
- Definition
consider a square matrix $A\in \mathbb{R}^{n\times n}$
Then, $\lambda \in \mathbb{R}$ is an eigenvalue of A and
$x\in \mathbb{R}^{n}\setminus \begin{Bmatrix}0\end{Bmatrix}$ is the corresponding eigenvector of A
if $Ax = \lambda x$
- Equivalent statements
$\lambda$ is a eigenvalue
$(A - \lambda I_{n})x = 0$ can be solved non-trivially, i.e., $x\neq 0$
$det(A - \lambda I_{n}) = 0$
- Eigenvectors are not unique
- Properties
1. for $A\in \mathbb{R}^{n\times n}$, n distinct eigenvalues $\Rightarrow $ eigenvectors are linearly independent
$\therefore$ the eigenvectors form a basis of $\mathbb{R}^{n}$
(converse is not true)
2. Determinant calculation
for eigenvalues $\lambda_{i} of A \in\mathbb{R}^{n\times n}$,
$det(A) = \prod_{i=1}^{n}\lambda_{i}$
3. Trace calculation
for eigenvalues $\lambda_{i} of A \in\mathbb{R}^{n\times n}$,
$tr(A) = \sum_{i=1}^{n}\lambda_{i}$
3. Cholesky Decomposition
A real number : decomposition of two identical numbers, e.g., 9 = 3 * 3
For a symmetric, positive definite(all eigenvalue > 0) matrix A, $A = LL^{T}$, where
- L is a lower-triangular matrix with positive diagonals
- Such a L is unique, called Cholesky factor of A
faster determinant computation is available using cholesky decomposition
$\therefore det(A) = det(L)det(L^{T}) = det(L^{2})$, where $det(L) = \prod_{i} I_{ii}$.
Thus, $det(A)=\prod{i} I_{ii}^{2}$.
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