Sample Solution

MMT-006 Solved Assignment 2025

  1. State whether the following statements True or False? Justify your answers:
    a) The function ||*||\|\cdot\| defined on R n R n R^(n)\mathbb{R}^nRn as:
x = j = 1 n | a j | for x = ( a , a 2 , , a n ) R n x = j = 1 n a j for x = a , a 2 , , a n R n ||x||=sum_(j=1)^(n)|a_(j)|” for “x=(a,a_(2),dots,a_(n))inR^(n)\|x\|=\sum_{j=1}^n\left|a_j\right| \text { for } x=\left(a, a_2, \ldots, a_n\right) \in \mathbb{R}^nx=j=1n|aj| for x=(a,a2,,an)Rn
is a norm.
Answer:
Let’s determine whether the function ||*||\|\cdot\| defined on R n R n R^(n)\mathbb{R}^nRn by x = j = 1 n | a j | x = j = 1 n | a j | ||x||=sum_(j=1)^(n)|a_(j)|\|x\| = \sum_{j=1}^n |a_j|x=j=1n|aj| for x = ( a 1 , a 2 , , a n ) x = ( a 1 , a 2 , , a n ) x=(a_(1),a_(2),dots,a_(n))x = (a_1, a_2, \ldots, a_n)x=(a1,a2,,an) is a norm. A norm must satisfy three properties: positivity (including x = 0 x = 0 ||x||=0\|x\| = 0x=0 if and only if x = 0 x = 0 x=0x = 0x=0), scalability (homogeneity), and the triangle inequality. We’ll check each briefly to see if this holds.
1. Positivity:
  • x = j = 1 n | a j | 0 x = j = 1 n | a j | 0 ||x||=sum_(j=1)^(n)|a_(j)| >= 0\|x\| = \sum_{j=1}^n |a_j| \geq 0x=j=1n|aj|0 since | a j | 0 | a j | 0 |a_(j)| >= 0|a_j| \geq 0|aj|0.
  • If x = 0 x = 0 ||x||=0\|x\| = 0x=0, then j = 1 n | a j | = 0 j = 1 n | a j | = 0 sum_(j=1)^(n)|a_(j)|=0\sum_{j=1}^n |a_j| = 0j=1n|aj|=0, and since each | a j | 0 | a j | 0 |a_(j)| >= 0|a_j| \geq 0|aj|0, we must have | a j | = 0 | a j | = 0 |a_(j)|=0|a_j| = 0|aj|=0 for all j j jjj, so a j = 0 a j = 0 a_(j)=0a_j = 0aj=0, hence x = ( 0 , 0 , , 0 ) x = ( 0 , 0 , , 0 ) x=(0,0,dots,0)x = (0, 0, \ldots, 0)x=(0,0,,0).
  • If x = 0 x = 0 x=0x = 0x=0, then x = j = 1 n | 0 | = 0 x = j = 1 n | 0 | = 0 ||x||=sum_(j=1)^(n)|0|=0\|x\| = \sum_{j=1}^n |0| = 0x=j=1n|0|=0.
    This holds.
2. Scalability:
For c R c R c inRc \in \mathbb{R}cR and x R n x R n x inR^(n)x \in \mathbb{R}^nxRn, compute:
c x = j = 1 n | c a j | = j = 1 n | c | | a j | = | c | j = 1 n | a j | = | c | x . c x = j = 1 n | c a j | = j = 1 n | c | | a j | = | c | j = 1 n | a j | = | c | x . ||cx||=sum_(j=1)^(n)|ca_(j)|=sum_(j=1)^(n)|c||a_(j)|=|c|sum_(j=1)^(n)|a_(j)|=|c|||x||.\|c x\| = \sum_{j=1}^n |c a_j| = \sum_{j=1}^n |c| |a_j| = |c| \sum_{j=1}^n |a_j| = |c| \|x\|.cx=j=1n|caj|=j=1n|c||aj|=|c|j=1n|aj|=|c|x.
This satisfies homogeneity.
3. Triangle Inequality:
For x = ( a 1 , , a n ) x = ( a 1 , , a n ) x=(a_(1),dots,a_(n))x = (a_1, \ldots, a_n)x=(a1,,an) and y = ( b 1 , , b n ) y = ( b 1 , , b n ) y=(b_(1),dots,b_(n))y = (b_1, \ldots, b_n)y=(b1,,bn), check:
x + y = j = 1 n | a j + b j | j = 1 n ( | a j | + | b j | ) = j = 1 n | a j | + j = 1 n | b j | = x + y , x + y = j = 1 n | a j + b j | j = 1 n ( | a j | + | b j | ) = j = 1 n | a j | + j = 1 n | b j | = x + y , ||x+y||=sum_(j=1)^(n)|a_(j)+b_(j)| <= sum_(j=1)^(n)(|a_(j)|+|b_(j)|)=sum_(j=1)^(n)|a_(j)|+sum_(j=1)^(n)|b_(j)|=||x||+||y||,\|x + y\| = \sum_{j=1}^n |a_j + b_j| \leq \sum_{j=1}^n (|a_j| + |b_j|) = \sum_{j=1}^n |a_j| + \sum_{j=1}^n |b_j| = \|x\| + \|y\|,x+y=j=1n|aj+bj|j=1n(|aj|+|bj|)=j=1n|aj|+j=1n|bj|=x+y,
using the triangle inequality for absolute values in R R R\mathbb{R}R: | a j + b j | | a j | + | b j | | a j + b j | | a j | + | b j | |a_(j)+b_(j)| <= |a_(j)|+|b_(j)||a_j + b_j| \leq |a_j| + |b_j||aj+bj||aj|+|bj|. This holds.
All three properties are satisfied, so the statement is true. This is the 1 1 ℓ^(1)\ell^11-norm on R n R n R^(n)\mathbb{R}^nRn.
Justification: The function satisfies positivity ( x = 0 x = 0 ||x||=0\|x\| = 0x=0 iff x = 0 x = 0 x=0x = 0x=0), scalability ( c x = | c | x c x = | c | x ||cx||=|c|||x||\|c x\| = |c| \|x\|cx=|c|x), and the triangle inequality ( x + y x + y x + y x + y ||x+y|| <= ||x||+||y||\|x + y\| \leq \|x\| + \|y\|x+yx+y), as shown above. Thus, it is a norm.
b) C 0 C 0 quadC_(0)\quad C_0C0 is a Banach space.
Answer:
To determine whether C 0 C 0 C_(0)C_0C0 is a Banach space, we first need to clarify what C 0 C 0 C_(0)C_0C0 denotes, as it’s not explicitly defined in the question. In mathematical contexts, C 0 C 0 C_(0)C_0C0 typically refers to the space C 0 ( R ) C 0 ( R ) C_(0)(R)C_0(\mathbb{R})C0(R), the space of continuous functions on R R R\mathbb{R}R that vanish at infinity (i.e., f ( x ) 0 f ( x ) 0 f(x)rarr0f(x) \to 0f(x)0 as | x | | x | |x|rarr oo|x| \to \infty|x|), equipped with the supremum norm f = sup x R | f ( x ) | f = sup x R | f ( x ) | ||f||_(oo)=s u p_(x inR)|f(x)|\|f\|_\infty = \sup_{x \in \mathbb{R}} |f(x)|f=supxR|f(x)|. A Banach space is a complete normed vector space, so we’ll check if C 0 ( R ) C 0 ( R ) C_(0)(R)C_0(\mathbb{R})C0(R) with this norm is complete. Let’s assume this standard interpretation and proceed.
Proof of Truth:
  • Vector Space: C 0 ( R ) C 0 ( R ) C_(0)(R)C_0(\mathbb{R})C0(R) is a vector space under pointwise addition and scalar multiplication, and if f , g C 0 ( R ) f , g C 0 ( R ) f,g inC_(0)(R)f, g \in C_0(\mathbb{R})f,gC0(R), then f + g f + g f+gf + gf+g and c f c f cfc fcf (for c R c R c inRc \in \mathbb{R}cR or C C C\mathbb{C}C) vanish at infinity, as | f ( x ) | + | g ( x ) | 0 | f ( x ) | + | g ( x ) | 0 |f(x)|+|g(x)|rarr0|f(x)| + |g(x)| \to 0|f(x)|+|g(x)|0 and | c | | f ( x ) | 0 | c | | f ( x ) | 0 |c||f(x)|rarr0|c| |f(x)| \to 0|c||f(x)|0.
  • Normed Space: The supremum norm is well-defined, since continuous functions vanishing at infinity are bounded (if | f ( x ) | 0 | f ( x ) | 0 |f(x)|↛0|f(x)| \not\to 0|f(x)|0, there’s a sequence x n x n x_(n)rarr oox_n \to \inftyxn with | f ( x n ) | ϵ | f ( x n ) | ϵ |f(x_(n))| >= epsilon|f(x_n)| \geq \epsilon|f(xn)|ϵ, contradicting the definition).
  • Completeness: Take a Cauchy sequence { f n } { f n } {f_(n)}\{ f_n \}{fn} in C 0 ( R ) C 0 ( R ) C_(0)(R)C_0(\mathbb{R})C0(R): for every ϵ > 0 ϵ > 0 epsilon > 0\epsilon > 0ϵ>0, there exists N N NNN such that for m , n > N m , n > N m,n > Nm, n > Nm,n>N, f n f m = sup x | f n ( x ) f m ( x ) | < ϵ f n f m = sup x | f n ( x ) f m ( x ) | < ϵ ||f_(n)-f_(m)||_(oo)=s u p _(x)|f_(n)(x)-f_(m)(x)| < epsilon\|f_n – f_m\|_\infty = \sup_x |f_n(x) – f_m(x)| < \epsilonfnfm=supx|fn(x)fm(x)|<ϵ. For each fixed x x xxx, { f n ( x ) } { f n ( x ) } {f_(n)(x)}\{ f_n(x) \}{fn(x)} is a Cauchy sequence in R R R\mathbb{R}R or C C C\mathbb{C}C, which is complete, so f n ( x ) f ( x ) f n ( x ) f ( x ) f_(n)(x)rarr f(x)f_n(x) \to f(x)fn(x)f(x) pointwise. Since f n f n f_(n)f_nfn is Cauchy in the sup norm, f n f n f_(n)f_nfn converges uniformly to f f fff (standard result: uniform limit of continuous functions is continuous). Now, f C 0 ( R ) f C 0 ( R ) f inC_(0)(R)f \in C_0(\mathbb{R})fC0(R): given ϵ > 0 ϵ > 0 epsilon > 0\epsilon > 0ϵ>0, choose N N NNN so f n f m < ϵ / 2 f n f m < ϵ / 2 ||f_(n)-f_(m)||_(oo) < epsilon//2\|f_n – f_m\|_\infty < \epsilon/2fnfm<ϵ/2 for m , n > N m , n > N m,n > Nm, n > Nm,n>N, and since f N C 0 f N C 0 f_(N)inC_(0)f_N \in C_0fNC0, there’s a compact K K KKK such that | f N ( x ) | < ϵ / 2 | f N ( x ) | < ϵ / 2 |f_(N)(x)| < epsilon//2|f_N(x)| < \epsilon/2|fN(x)|<ϵ/2 for x K x K x!in Kx \notin KxK. Then, | f ( x ) | | f ( x ) f N ( x ) | + | f N ( x ) | < ϵ / 2 + ϵ / 2 = ϵ | f ( x ) | | f ( x ) f N ( x ) | + | f N ( x ) | < ϵ / 2 + ϵ / 2 = ϵ |f(x)| <= |f(x)-f_(N)(x)|+|f_(N)(x)| < epsilon//2+epsilon//2=epsilon|f(x)| \leq |f(x) – f_N(x)| + |f_N(x)| < \epsilon/2 + \epsilon/2 = \epsilon|f(x)||f(x)fN(x)|+|fN(x)|<ϵ/2+ϵ/2=ϵ outside K K KKK, so f C 0 ( R ) f C 0 ( R ) f inC_(0)(R)f \in C_0(\mathbb{R})fC0(R).
Thus, C 0 ( R ) C 0 ( R ) C_(0)(R)C_0(\mathbb{R})C0(R) is complete and a Banach space. The statement is true.
c) If A A AAA is the right shift operator on l 2 l 2 l^(2)l^2l2, then the eigen spectrum is non-empty.
Answer:
Let’s determine whether the statement “If A A AAA is the right shift operator on 2 2 ℓ^(2)\ell^22, then the eigen spectrum is non-empty” is true. The eigen spectrum typically refers to the set of eigenvalues, i.e., λ C λ C lambda inC\lambda \in \mathbb{C}λC such that A x = λ x A x = λ x Ax=lambda xA x = \lambda xAx=λx for some non-zero x 2 x 2 x inℓ^(2)x \in \ell^2x2. We’ll define the operator, analyze its eigenvalues, and justify the conclusion.
The right shift operator A A AAA on 2 = { ( x 1 , x 2 , x 3 , ) x n C , | x n | 2 < } 2 = { ( x 1 , x 2 , x 3 , ) x n C , | x n | 2 < } ℓ^(2)={(x_(1),x_(2),x_(3),dots)∣x_(n)inC,sum|x_(n)|^(2) < oo}\ell^2 = \{ (x_1, x_2, x_3, \dots) \mid x_n \in \mathbb{C}, \sum |x_n|^2 < \infty \}2={(x1,x2,x3,)xnC,|xn|2<} is defined by:
A ( x 1 , x 2 , x 3 , ) = ( 0 , x 1 , x 2 , x 3 , ) . A ( x 1 , x 2 , x 3 , ) = ( 0 , x 1 , x 2 , x 3 , ) . A(x_(1),x_(2),x_(3),dots)=(0,x_(1),x_(2),x_(3),dots).A (x_1, x_2, x_3, \dots) = (0, x_1, x_2, x_3, \dots).A(x1,x2,x3,)=(0,x1,x2,x3,).
It’s a bounded linear operator with A = 1 A = 1 ||A||=1\|A\| = 1A=1 (since A e n = e n + 1 = 1 A e n = e n + 1 = 1 ||Ae_(n)||=||e_(n+1)||=1\|A e_n\| = \|e_{n+1}\| = 1Aen=en+1=1).
Eigenvalue Check:
Suppose λ λ lambda\lambdaλ is an eigenvalue with eigenvector x = ( x 1 , x 2 , x 3 , ) 0 x = ( x 1 , x 2 , x 3 , ) 0 x=(x_(1),x_(2),x_(3),dots)!=0x = (x_1, x_2, x_3, \dots) \neq 0x=(x1,x2,x3,)0, so:
A x = λ x or ( 0 , x 1 , x 2 , ) = ( λ x 1 , λ x 2 , λ x 3 , ) . A x = λ x or ( 0 , x 1 , x 2 , ) = ( λ x 1 , λ x 2 , λ x 3 , ) . Ax=lambda x quad”or”quad(0,x_(1),x_(2),dots)=(lambdax_(1),lambdax_(2),lambdax_(3),dots).A x = \lambda x \quad \text{or} \quad (0, x_1, x_2, \dots) = (\lambda x_1, \lambda x_2, \lambda x_3, \dots).Ax=λxor(0,x1,x2,)=(λx1,λx2,λx3,).
Equate components:
  • 0 = λ x 1 0 = λ x 1 0=lambdax_(1)0 = \lambda x_10=λx1,
  • x 1 = λ x 2 x 1 = λ x 2 x_(1)=lambdax_(2)x_1 = \lambda x_2x1=λx2,
  • x 2 = λ x 3 x 2 = λ x 3 x_(2)=lambdax_(3)x_2 = \lambda x_3x2=λx3,
  • x 3 = λ x 4 x 3 = λ x 4 x_(3)=lambdax_(4)x_3 = \lambda x_4x3=λx4,
  • and so on.
From 0 = λ x 1 0 = λ x 1 0=lambdax_(1)0 = \lambda x_10=λx1:
  • If λ 0 λ 0 lambda!=0\lambda \neq 0λ0, then x 1 = 0 x 1 = 0 x_(1)=0x_1 = 0x1=0.
  • Then x 1 = λ x 2 x 1 = λ x 2 x_(1)=lambdax_(2)x_1 = \lambda x_2x1=λx2 gives 0 = λ x 2 0 = λ x 2 0=lambdax_(2)0 = \lambda x_20=λx2, so x 2 = 0 x 2 = 0 x_(2)=0x_2 = 0x2=0 (since λ 0 λ 0 lambda!=0\lambda \neq 0λ0).
  • Continue: x 2 = λ x 3 x 2 = λ x 3 x_(2)=lambdax_(3)x_2 = \lambda x_3x2=λx3 gives 0 = λ x 3 0 = λ x 3 0=lambdax_(3)0 = \lambda x_30=λx3, so x 3 = 0 x 3 = 0 x_(3)=0x_3 = 0x3=0, and inductively, x n = 0 x n = 0 x_(n)=0x_n = 0xn=0 for all n n nnn.
Thus, x = 0 x = 0 x=0x = 0x=0, a contradiction since eigenvectors are non-zero. Hence, λ 0 λ 0 lambda!=0\lambda \neq 0λ0 yields no eigenvalues. If λ = 0 λ = 0 lambda=0\lambda = 0λ=0:
( 0 , x 1 , x 2 , ) = ( 0 , 0 , 0 , ) , ( 0 , x 1 , x 2 , ) = ( 0 , 0 , 0 , ) , (0,x_(1),x_(2),dots)=(0,0,0,dots),(0, x_1, x_2, \dots) = (0, 0, 0, \dots),(0,x1,x2,)=(0,0,0,),
so x 1 = 0 x 1 = 0 x_(1)=0x_1 = 0x1=0, x 2 = 0 x 2 = 0 x_(2)=0x_2 = 0x2=0, etc., again x = 0 x = 0 x=0x = 0x=0, no eigenvector. The point spectrum (eigenvalues) is empty.
Spectrum vs. Eigen Spectrum:
The spectrum σ ( A ) = { λ A λ I is not invertible } σ ( A ) = { λ A λ I is not invertible } sigma(A)={lambda∣A-lambda I” is not invertible”}\sigma(A) = \{ \lambda \mid A – \lambda I \text{ is not invertible} \}σ(A)={λAλI is not invertible} is the unit disk | λ | 1 | λ | 1 |lambda| <= 1|\lambda| \leq 1|λ|1 (since A A A^(**)A^*A is the left shift, and residual spectrum analysis shows this), but eigenvalues are a subset. The statement specifies “eigen spectrum,” meaning point spectrum, which is empty.
Conclusion: The statement is false.
Justification: For A x = λ x A x = λ x Ax=lambda xA x = \lambda xAx=λx, λ x 1 = 0 λ x 1 = 0 lambdax_(1)=0\lambda x_1 = 0λx1=0 forces x 1 = 0 x 1 = 0 x_(1)=0x_1 = 0x1=0 if λ 0 λ 0 lambda!=0\lambda \neq 0λ0, then x n = 0 x n = 0 x_(n)=0x_n = 0xn=0 inductively, contradicting x 0 x 0 x!=0x \neq 0x0. No λ λ lambda\lambdaλ yields a non-zero eigenvector, so the eigen spectrum is empty. Example: 2 2 ℓ^(2)\ell^22 with right shift has no eigenvalues.
d) If a normed linear space is reflexive, then so is its dual space.
Answer:
Let’s evaluate the statement: “If a normed linear space is reflexive, then so is its dual space.” A normed linear space X X XXX is reflexive if the canonical embedding J : X X J : X X J:X rarrX^(****)J: X \to X^{**}J:XX, defined by J ( x ) ( f ) = f ( x ) J ( x ) ( f ) = f ( x ) J(x)(f)=f(x)J(x)(f) = f(x)J(x)(f)=f(x) for f X f X f inX^(**)f \in X^*fX, is surjective (and thus an isometric isomorphism in Banach spaces, since it’s always injective). Here, X X X^(**)X^*X is the dual space (continuous linear functionals), and X X X^(****)X^{**}X is the double dual. We need to check if X X XXX being reflexive implies X X X^(**)X^*X is reflexive, i.e., J : X X J : X X J:X^(**)rarrX^(******)J: X^* \to X^{***}J:XX is surjective.
Analysis:
  • In a reflexive Banach space X X XXX, X X X X X~=X^(****)X \cong X^{**}XX via J J JJJ.
  • The dual X X X^(**)X^*X is a Banach space (complete since X X XXX is normed), and its dual is X X X^(******)X^{***}X. Reflexivity of X X X^(**)X^*X means X X X X X^(**)~=X^(******)X^* \cong X^{***}XX.
  • Since X X XXX is reflexive, X X X X X^(****)~=XX^{**} \cong XXX, and X = ( X ) X = ( X ) X^(******)=(X^(**))^(****)X^{***} = (X^*)^{**}X=(X). We need to determine if X X X^(**)X^*X maps onto X X X^(******)X^{***}X.
Key Insight:
For Banach spaces, a deep result (e.g., from functional analysis) states that if X X XXX is reflexive, then X X X^(**)X^*X is reflexive. Why? If X X X X X~=X^(****)X \cong X^{**}XX, the dual of X X X^(**)X^*X is X X X^(******)X^{***}X, and since X X X^(**)X^*X is a Banach space, its reflexivity hinges on the bidual. The map J : X X J : X X J:X rarrX^(****)J: X \to X^{**}J:XX being an isomorphism implies X ( X ) X ( X ) X^(**)~=(X^(****))^(**)X^* \cong (X^{**})^*X(X), and taking duals, X ( X ) X ( X ) X^(******)~=(X^(**))^(****)X^{***} \cong (X^*)^{**}X(X). Reflexivity is preserved under duality in this context.
Short Proof (True):
If X X XXX is reflexive, J : X X J : X X J:X rarrX^(****)J: X \to X^{**}J:XX is an isomorphism. Then X ( X ) X ( X ) X^(**)~=(X^(****))^(**)X^* \cong (X^{**})^*X(X), and X ( X ) X ( X ) X^(******)~=(X^(**))^(****)X^{***} \cong (X^*)^{**}X(X). Since X X X^(**)X^*X is Banach, and reflexivity of X X XXX implies the dual structure aligns (via isomorphism), X ( X ) X ( X ) X^(**)~=(X^(**))^(****)X^* \cong (X^*)^{**}X(X), so X X X^(**)X^*X is reflexive.
Example Check:
  • 2 2 ℓ^(2)\ell^22 is reflexive, and ( 2 ) 2 ( 2 ) 2 (ℓ^(2))^(**)~=ℓ^(2)(\ell^2)^* \cong \ell^2(2)2 is also reflexive.
  • Finite-dimensional spaces are reflexive, and their duals are too.
No counterexamples exist in Banach spaces. The statement is true.
Justification: If X X XXX is reflexive, X X X X X~=X^(****)X \cong X^{**}XX, and duality preserves reflexivity: X ( X ) X ( X ) X^(**)~=(X^(**))^(****)X^* \cong (X^*)^{**}X(X), as the canonical map is surjective. Thus, X X X^(**)X^*X is reflexive.
e) If a normed linear space X X XXX is finite dimensional, then so is X X X^(‘)X^{\prime}X.
Answer:
Let’s evaluate the statement: “If a normed linear space X X XXX is finite dimensional, then so is X X X^(‘)X’X.” Here, X X X^(‘)X’X denotes the dual space of X X XXX, the space of all continuous linear functionals from X X XXX to its scalar field (typically R R R\mathbb{R}R or C C C\mathbb{C}C). We need to determine if finite dimensionality of X X XXX implies finite dimensionality of X X X^(‘)X’X, and justify it concisely.
Analysis:
  • A normed linear space X X XXX of finite dimension, say dim X = n dim X = n dim X=n\dim X = ndimX=n, has a basis { e 1 , e 2 , , e n } { e 1 , e 2 , , e n } {e_(1),e_(2),dots,e_(n)}\{ e_1, e_2, \ldots, e_n \}{e1,e2,,en}.
  • The dual space X X X^(‘)X’X consists of all linear functionals f : X K f : X K f:X rarrKf: X \to \mathbb{K}f:XK (where K = R K = R K=R\mathbb{K} = \mathbb{R}K=R or C C C\mathbb{C}C), and continuity is automatic in finite dimensions since all linear functionals on a finite-dimensional normed space are bounded.
  • Define the dual basis { e 1 , e 2 , , e n } { e 1 , e 2 , , e n } {e_(1)^(**),e_(2)^(**),dots,e_(n)^(**)}\{ e_1^*, e_2^*, \ldots, e_n^* \}{e1,e2,,en} where e i ( e j ) = δ i j e i ( e j ) = δ i j e_(i)^(**)(e_(j))=delta_(ij)e_i^*(e_j) = \delta_{ij}ei(ej)=δij (Kronecker delta). For any x = i = 1 n a i e i x = i = 1 n a i e i x=sum_(i=1)^(n)a_(i)e_(i)x = \sum_{i=1}^n a_i e_ix=i=1naiei, we have e i ( x ) = a i e i ( x ) = a i e_(i)^(**)(x)=a_(i)e_i^*(x) = a_iei(x)=ai, and any functional f X f X f inX^(‘)f \in X’fX is f = i = 1 n f ( e i ) e i f = i = 1 n f ( e i ) e i f=sum_(i=1)^(n)f(e_(i))e_(i)^(**)f = \sum_{i=1}^n f(e_i) e_i^*f=i=1nf(ei)ei.
Proof (True):
If dim X = n < dim X = n < dim X=n < oo\dim X = n < \inftydimX=n<, the dual basis { e 1 , , e n } { e 1 , , e n } {e_(1)^(**),dots,e_(n)^(**)}\{ e_1^*, \ldots, e_n^* \}{e1,,en} spans X X X^(‘)X’X, and it’s linearly independent (if c i e i = 0 c i e i = 0 sumc_(i)e_(i)^(**)=0\sum c_i e_i^* = 0ciei=0, apply to e j e j e_(j)e_jej to get c j = 0 c j = 0 c_(j)=0c_j = 0cj=0). Thus, dim X = n < dim X = n < dim X^(‘)=n < oo\dim X’ = n < \inftydimX=n<, so X X X^(‘)X’X is finite dimensional.
Example:
  • For X = R 2 X = R 2 X=R^(2)X = \mathbb{R}^2X=R2 with Euclidean norm, X R 2 X R 2 X^(‘)~=R^(2)X’ \cong \mathbb{R}^2XR2 (e.g., via f ( x , y ) = a x + b y f ( x , y ) = a x + b y f(x,y)=ax+byf(x, y) = ax + byf(x,y)=ax+by), and dim X = 2 dim X = 2 dim X^(‘)=2\dim X’ = 2dimX=2.
The statement is true.
Justification: If X X XXX is finite dimensional with dim X = n dim X = n dim X=n\dim X = ndimX=n, the dual space X X X^(‘)X’X has a basis of size n n nnn, so dim X = n < dim X = n < dim X^(‘)=n < oo\dim X’ = n < \inftydimX=n<, making X X X^(‘)X’X finite dimensional.
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