Linear Algebra
Contents
- TASHOUN（她笑）
- Spectral Decomposition
- Equivalent Conditions of an Invertible Matrix
- Consistency of Ax=b
- A Motivation of Adjoint Operators
- A Motivation of Normal Operators
- Transpose, Trace, Determinant, Rank, Inverse
- A Geometric Interpretation of Determinant
- Properties of the Determinant
- 遞迴方程式的線性代數觀點recurrence relation (linear algebra aspect)
- Diamond
- The Importance of Strang's Big Picture
- Jordan Canonical Form
TASHOUN（她笑）
| real | complex | ||||||
|---|---|---|---|---|---|---|---|
| transpose \(A^t\) |
adjoint \(A^*\) |
||||||
| orthogonally diagonalizable |
spectral theorem \(\Leftrightarrow\) |
symmetric \(A^t=A\) |
Hermitian (self-adjoint) \(A^*=A\) |
\(\Rightarrow\) | unitarily diagonalizable |
spectral theorem \(\Leftrightarrow\) |
normal |
| orthogonal \(A^tA=I\) |
unitary \(A^* A=I\) |
注意到上面這張圖，美中不足的地方在於Hermitian並不是unitarily diagonalizable的等價條件，讓這張圖並沒有呈現完美的對稱。
Spectral Decomposition
Let \[ \begin{array}{lll} A &=& PDP^t \\ &=& (\mathbf{u}_1|\mathbf{u}_2|\cdots |\mathbf{u}_n)\begin{pmatrix}\lambda_1 & 0 & \cdots & 0 \\ 0 & \lambda_2 & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & \lambda_n \end{pmatrix}\begin{pmatrix}\mathbf{u}_1^t \\ \mathbf{u}_2^t \\ \vdots \\ \mathbf{u}_n^t \end{pmatrix} \\ &=& \lambda_1 \mathbf{u}_1 \mathbf{u}_1^t+\lambda_2 \mathbf{u}_2\mathbf{u}_2^t+\cdots +\lambda_n \mathbf{u}_n \mathbf{u}_n^t \\ \end{array} \] be the orthogonal diagonalization of \(A\). For any \(\mathbf{v}\), \[ \begin{array}{lll} \mathbf{u}_i\mathbf{u}_i^t\mathbf{v} &=& (\mathbf{u}_i^t \mathbf{v})\mathbf{u}_i \\ &=& \langle \mathbf{u}_i, \mathbf{v}\rangle \mathbf{u}_i \\ &=& \langle \mathbf{v}, \mathbf{u}_i\rangle \mathbf{u}_i \\ &=& \frac{\langle \mathbf{v}, \mathbf{u}_i\rangle}{||\mathbf{u}_i||^2}\mathbf{u}_i \\ &=& \text{proj}_{\text{span}(\mathbf{u}_i)}\mathbf{v} \\ &=& \text{the orthogonal projection of }\mathbf{v}\text{ on the subspace }\text{span}(\mathbf{u}_i)\leq E_{\lambda_i} \end{array} \]
Equivalent Conditions of an Invertible Matrix
See Anton's Theorem 6.4.5 in Elementary Linear Algebra.
If \(A\) is an \(n\times n\) matrix, then the following statements are equivalent.
- \(A\) is invertible.
- \(A\mathbf{x}=\mathbf{0}\) has only the trivial solution.
- The reduced row echelon form of \(A\) is \(I_n\).
- \(A\) is expressible as a product of elementary matrices.
- \(A\mathbf{x}=\mathbf{b}\) is consistent for every \(n\times 1\) matrix \(\mathbf{b}\).
- \(A\mathbf{x}=\mathbf{b}\) has exactly one solution for every \(n\times 1\) matrix \(\mathbf{b}\).
- \(\det{(A)}\neq 0\).
- The column vectors of \(A\) are linearly independent .
- The row vectors of \(A\) are linearly indenpendent.
- The column vectors of \(A\) span \(\mathbb{R}^n\).
- The row vectors of \(A\) span \(\mathbb{R}^n\).
- The column vectors of \(A\) form a basis for \(\mathbb{R}^n\).
- The row vectors of \(A\) form a basis for \(\mathbb{R}^n\).
- \(A\) has rank \(n\).
- \(A\) has nullity \(0\).
- The orthogonal complement of the null space of \(A\) is \(\mathbb{R}^n\).
- The orthogonal complement of the row space of \(A\) is \(\{\mathbf{0}\}\).
- The kernel of \(T_A\) is \(\{\mathbf{0}\}\).
- The range of \(T_A\) is \(\mathbb{R}^n\).
- \(T_A\) is one-to-one.
- \(\lambda=0\) is not an eigenvalue of \(A\).
- \(A^T A\) is invertible.
上面這個列表不太好用，因為一般來說不一定有 \(m=n\)，所以我個人比較喜歡用下面的等價關係。下面 \(A\) 為一 \(m\times n\) 的矩陣。點擊箭頭可以看到證理由。
| 線性變換的觀點 | 向量空間的觀點 | 維度的觀點 | 向量的觀點 | |||||
| columns are linearly independent | ||||||||
| ⇗rank = the number of linearly independent columns ⇙rank = the number of linearly independent columns | ||||||||
| \(A:F^n\to F^m\) is one-to-one | ⇔Theorem 8.2.1 | kernel A = 0 | ⇔Definition | nullity A= 0 | ⇔Dimension Theorem | rank A = n | ||
| ⇘rank A^T = n <=> A^T columns span F^n ⇖rank A^T = n <=> A^T columns span F^n | ||||||||
| rows span F^n | ||||||||
| rows are linearly independent | ||||||||
| ⇗rank = the number of linearly independent rows ⇙rank = the number of linearly independent rows | ||||||||
| \(A:F^n\to F^m\) is onto | ⇔Definition | image A = F^m | ⇔rank = dimension of image | rank A = m | ||||
| ⇘column space = image A ⇖column space = image A | ||||||||
| columns span F^m |
Consistency of \(A\mathbf{x}=\mathbf{b}\)
- \(\text{rank}(A)\neq \text{rank}(A|\mathbf{b})\Leftrightarrow A\mathbf{x}=\mathbf{b}\) has no solutions
- \(\text{rank}(A)=\text{rank}(A|\mathbf{b})=n\Leftrightarrow A\mathbf{x}=\mathbf{b}\) has exactly one solution
- \(\text{rank}(A)=\text{rank}(A|\mathbf{b})< n\Leftrightarrow A\mathbf{x}=\mathbf{b}\) has infinitely many solutions
上面這三個敘述可以利用上面的one-to-one及onto的等價敘述得到。另外，\(A\mathbf{x}=\mathbf{b}\) 的解只有三種可能，無解、恰有一組解、無線多組解。證明如下：假設 \(\mathbf{x}_1, \mathbf{x}_2\) 為相異兩組解，則 \(\mathbf{x}_1+c(\mathbf{x}_1-\mathbf{x}_2)\) 皆為解，其中 \(c\) 為任意實數。
注意到，例如空間中的三個平面，既使判斷方程式是無解、無限多組解還是唯一解之後，仍然要再更進一步探討（可以利用法向量）三個平面的相交情況。（平面相交的圖形來自於Anton的書）
A Motivation of Adjoint Operators
A Motivation of Normal Operators
Transpose, Trace, Determinant, Rank, Inverse
| transpose | trace | det | rank | inverse | |
|---|---|---|---|---|---|
| \(cA\) | \((cA)^t=cA^t\) by def. | \(\text{tr }cA=c\cdot \text{tr }A\) by def. | \(\det{cA}=c^n \det{A}\) by def. | \(\text{rank }cA=\text{rank }A\), \(c\neq 0\) | \((cA)^{-1}=c^{-1}A^{-1}\), \(c\neq 0\) by def. |
| \(A+B\) | \((A+B)^t=A^t+B^t\) by def. | \(\text{tr }(A+B)=\text{tr }A+\text{tr }B\) by def. | \(\text{rank }(A+B)\leq \text{rank }A+\text{rank }B\) | ||
| \(AB\) | \((AB)^t=B^t A^t\) by def. | \(\text{tr }AB=\color{red}{\text{tr }BA}\) by def. | \(\det{AB}=\det{A}\cdot \det{B}\) difficult | \(\text{rank }AB\leq \min{\{\text{rank }A, \text{rank }B\}}\) | \((AB)^{-1}=B^{-1}A^{-1}\) by def. |
| \(A^t\) | \((A^t)^t=A\) by def. | \(\text{tr }A^t=\text{tr }A\) by def. | \(\det{A^t}=\det{A}\) by def. | \(\text{rank }A^t=\text{rank }A\) difficult | \((A^t)^{-1}=(A^{-1})^t\) |
A Geometric Interpretation of Determinant
參考這裡，動畫版參考這裡。另一個比較常見的證法是這個，還有一個比較少見的證法，不過我覺得後面兩個都比不上第一個簡單。
Properties of the Determinant
See Friedberg's Linear Algebra
- If \(B\) is a matrix obtained by interchanging any two rows or interchanging any two columns of an \(n\times n\) matrix \(A\), then \(\det{B} = −\det{A}\). (Proof: By the definition.)
- If \(B\) is a matrix obtained by multiplying each entry of some row or column of an \(n\times n\) matrix \(A\) by a scalar \(k\), then \(\det{B} = k\cdot \det{A}\). (Proof: By the definition.)
- If \(B\) is a matrix obtained from an \(n\times n\) matrix \(A\) by adding a multiple of row \(i\) to row \(j\) or a multiple of column \(i\) to column \(j\) for \( i\neq j\), then \(\det{B} = \det{A}\). (Proof: Waiting for proving.)
- The determinant of an upper triangular matrix is the product of its diagonal entries. In particular, \(\det{I} = 1\). (Proof: By the definition.)
- If two rows (or columns) of a matrix are identical, then the determinant of the matrix is zero. (Proof: By 3 and 10.)
- For any \(n\times n\) matrices \(A\) and \(B\), \(\det{AB}=\det{A}\cdot \det{B}\). (Proof: It is difficult to prove.)
- An \(n\times n\) matrix \(A\) is invertible if and only if \(\det{A}\neq 0\). Furthermore, if \(A\) is invertible, then \(\det{A^{-1}}=\frac{1}{\det{A}}\). (Proof: By 4 and 6.)
- For any \(n\times n\) matrix \(A\), the determinants of \(A\) and \(A^t\) are equal. (Proof: By the definition.)
- If \(A\) and \(B\) are similar matrices, then \(\det{A}=\det{B}\). (Proof: By 6 and 7.)
- If \(A\) has a \(0\) row or a \(0\) column, then \(\det{A}=0\). (Proof: By the definition.)
- If \(i\)th row equals a multiple of \(j\)th row, then \(\det{A}=0\). (Proof: 3 and 10.)
- \(\det{A}\) is row linear. (Proof: Waiting for proving.)
遞迴方程式的線性代數觀點recurrence relation (linear algebra aspect)
我們大學時（甚至是高中），即學過遞迴方程式（recurrence relation），例如 \(a_{n+2}=5a_{n+1}-6a_n\)，其中 \(n\) 為大於等於 \(1\) 的正整數，一般書上教我們的解法是，先求出此遞迴方程式的特徵方程式（characteristic equation） \(r^2=5r-6\)，並求出此特徵方程式的根為 \(r=2\) 或是 \(r=3\)，則此遞迴方程式的解為 \(a_n=c_1\cdot 2^n+c_2\cdot 3^n.\)
又例如遞迴方程式 \(a_{n+2}=4a_{n+1}-4a_n.\)，此遞迴方程式的特徵方程式為 \(r^2=4r-4\)，有重根 \(r=2\)，這時候我們要調整遞迴方程式的解為 \(a_n=c_1\cdot 2^n+c_2\cdot n\cdot 2^n\)，學生到這裡可能會有三個問題。
- 特徵方程式是我們在線性代數中學習到的觀念，遞迴方程式是否跟向量空間(vector space)、線性變換（linear transformation）有關係，為何在解遞迴方程式時會用到特徵方程式？
- 我們何以知道當特徵方程式有重根時，其遞迴方程式的解要做如此的調整？
- 一般書上都是給出遞迴方程式的解，然後用代入驗證的方法證明，然而，相信數學家們不是一開始研究遞迴方程式時就知道其解的形式，數學家們當初是如何知道遞迴方程式的解的形式呢？
本文的目的在回答這三個問題。
Diamond
| Singular Value Decomposition |
→ | Pseudoinverse | ||||
| ↗ | ↘ | |||||
| ↗ | ↘ | |||||
| Gram-Schmidt Process |
← | ← | Orthogonal Projection |
→ | → | Least Square Problem |
| ↘ | ↓ | ↗ | ||||
| ↘ | Householder Reflection |
↗ | ||||
| ↘ | ↓ | ↗ | ||||
| QR-decomposition |
No comments:
Post a Comment