Define Subspace
(V, +, ⋅ ): vector space,
(W, +, ⋅ ): vector space
則稱 W 為 V 之子空間(subspace)
Theorem of 子空間的充要條件
已知 V: vector space, W⊆V, W=∅, 則下列敘述等價:
1. W 為 V 的子空間
2. ∀u, v∈W, u + v∈W
3. ∀c∈F, ∀v∈W, cv∈W 4. ∀c, d∈F, ∀u, v∈W, cu + dv∈W
Corollary of 2. & 3.
∀ci∈F, vi∈W, i = 1, 2, ..., k, i=1∑kcivi∈W
Corollary of 4.
→ 在 W⊆V 和 W=∅ 的條件下,只需驗證向量加法及純量積的封閉性即可證明 W 為 V 的子空間
Square of R 的三型子空間
原點: {(0, 0)} = { 0 }: zero space → 點
∀ vector spaces, ∃{ 0 }
過原點直線 → 線
若非直線,則不具純量積封閉性
x−y 平面: R2 → 面
V 為 V 的子空間
Theorem of 兩子空間交集
W1, W2 are subspaces of V, 則 W1∩W2 is a subspace of V
W1⊆V 且 W2⊆V, 則 W1∩W2⊆V
∵0∈W1∩W2, ∴W1∩W2=∅
∀c, d∈F, ∀u, v∈W1∩W2,
∵u, v∈W1 且 W1 is a subspace of V, ∴cu + dv = W1
∵u, v∈W2 且 W2 is a subspace of V, ∴cu + dv = W2
∴cu + dv = W1∩W2
W1, W2 are subspaces of V, W1∩W2 不一定為 V 的 subspace
V = R2 has two subsapces W1 and W2,
W1 = { (x, 0) | x∈R },
x 軸
W2 = { (0, y) | y∈R },
y 軸
but W1∪W2 is not a subspace of V.
ex. (1, 0), (0, 1) ∈W1∪W2, but (1, 0) + (0, 1) = (1, 1) ∈/W1∪W2
Theorem of 兩子空間聯集
W1, W2 are subspaces of V, W1∩W2⇔W1⊆W2 or W2⊆W1
Define 和空間 (Sum Space)
W1, W2 are subspaces of V, W1 + W2 = { w1 + w2 | w1∈W1, w2∈W2 }, 稱為 W1, W2 之和空間(sum space)
Theorem of 和空間為 V 的子空間
W1, W2 are subspaces of V, 則 W1 + W2 is a subspace of V
W1⊆V 且 W2⊆V, 則 W1 + W2⊆V
∵0 = 0 + 0∈W1 + W2, ∴W1 + W2=∅
∀c, d∈F, ∀u, v∈W1 + W2,
u = u1 + u2, u1∈W1, u2∈W2
v = v1 + v2, v1∈W1, v2∈W2
⇒cu + dv = c(u1 + u2) + d(v1 + v2) = (cu1 + dv1) + (cu2 + dv2) ∈W1 + W2
A∈Fm×n,
核空間(kernel of A, nullspace, N(A)):
ker(A) = { x: n×1 | Ax = 0 }
齊次解集,收集 Ax = 0 之 x
行空間(column space):
CS(A) = { Ax: m×1 | x: n×1 }
收集 Ax = y 之 y
左核空間(left kernel of A, left nullspace):
Lker(A) = { x: 1×m | xA = 0 }
收集 xA = 0 之 x
列空間(row space):
RS(A) = { xA: 1×n | x: 1×m }
收集 xA = y 之 y
ex.A=[112132]???=[00] ⇒ ker(A) =⎩⎨⎧aa−a∣a∈R⎭⎬⎫;
ex.B=[112200]???=[bb] ⇒ CS(B) ={[bb]∣b∈R}
Theorem of 四個基本子空間
A∈Fm×n, 1. ker(A) is a subspace of Fn×1 2. CS(A) is a subspace of Fm×1
∀y∈CS(A) ⇔y = Ax, for some x 3. Lker(A) is a subspace of F1×m 4. RS(A) is a subspace of F1×n
∵A⋅0 = 0, ∴0∈ker(A) ⇒ker(A) =∅
∀c, d∈F, ∀x1, x2∈ker(A) ⇒Ax1 = Ax2 = 0
⇒A(cx1 + dx2) = cAx1 + dAx2 = c⋅0 + d⋅0 = 0
⇒cx1 + dx2∈ker(A)
3 同理
0 = A⋅0∈CS(A) ⇒CS(A) =∅
∀c, d∈F, ∀y1, y2∈CS(A) ⇒y1 = Ax1, y2 = Ax2, x1, x2: n×1
⇒cy1 + dy2 = cAx1 + dAx2 = A(cx1 + dx2) ∈CS(A)
4 同理
四個基本子空間 with Invertible Matrix
ker(B) ⊆ker(AB), 當 A is nonsingular 時, ker(B) = ker(AB)
ABx = 0→Bx = 0
Lker(A) ⊆Lker(AB), 當 B is nonsingular 時, Lker(A) = Lker(AB)
CS(AB) ⊆CS(A), 當 B 為可逆時, CS(AB) = CS(A)
RS(AB) = RS(B)
四個基本子空間 with Row and Column Equivalence
A, B∈Fm×n, 1. A 列等價於 B, 則
ker(A) = ker(B)
RS(A) = RS(B)
Lker(A) = Lker(B)
CS(A) = CS(B)