MATDSC-253: Linear Algebra FYUG Even Semester Exam, 2025

Programme: FYUG (4th Semester)

Course Code: MATDSC-253

Full Marks: 70

Subject: Mathematics

UNIT I: Vector Spaces

Question 1 (a)

[2 Marks]

Show that αx = 0 implies α = 0 or x = 0 for α in F and x in V.

Solution:

Assume αx = 0. If α = 0, the condition is satisfied. If α ≠ 0, then its multiplicative inverse α⁻¹ exists in the field F. Multiplying both sides by α⁻¹:

α⁻¹(αx) = α⁻¹(0)

(α⁻¹α)x = 0

1x = 0 ⇒ x = 0.

Thus, either α = 0 or x = 0.

Question 1 (b)

[2 Marks]

Justify whether W = {(x, y, 2) | x, y in R} is a subspace of R³.

Solution:

For W to be a subspace, it must contain the zero vector (0, 0, 0). In this set, the third component is fixed at 2. Since (0, 0, 0) does not belong to W (because 0 ≠ 2), W is not a subspace of R³.

Question 2 (b)

[4 Marks]

Define linearly dependent/independent sets. Show {(1,0,0), (0,1,0), (0,0,1), (1,1,1)} is linearly dependent.

Definitions:

Justification:

Notice that (1,1,1) = 1(1,0,0) + 1(0,1,0) + 1(0,0,1). Since one vector can be expressed as a linear combination of the others, the set is linearly dependent.

UNIT II: Basis and Dimension

Question 4 (b)

[6 Marks]

Prove dim(W₁ + W₂) = dim W₁ + dim W₂ - dim(W₁ ∩ W₂).

Proof Strategy:

  1. Let {u₁, ..., uₖ} be a basis for W₁ ∩ W₂.
  2. Extend this to a basis {u₁, ..., uₖ, v₁, ..., vₘ} for W₁.
  3. Extend the intersection basis to {u₁, ..., uₖ, w₁, ..., wₙ} for W₂.
  4. Prove that {u₁, ..., uₖ, v₁, ..., vₘ, w₁, ..., wₙ} is a basis for W₁ + W₂.
  5. Count: dim(W₁ + W₂) = k + m + n = (k+m) + (k+n) - k = dim W₁ + dim W₂ - dim(W₁ ∩ W₂).

UNIT III: Linear Transformations

Question 5 (c)

[2 Marks]

Define nullity and rank of a linear transformation.

Rank: The dimension of the range (image) of a linear transformation T, denoted as ρ(T).
Nullity: The dimension of the kernel (null space) of T, denoted as ν(T).

Question 6 (c)

[5 Marks]

State and prove rank-nullity law.

Statement: If V is a finite-dimensional vector space and T: V → W is linear, then rank(T) + nullity(T) = dim(V).

Proof: Let {v₁, ..., vₖ} be a basis for Ker(T), so nullity(T) = k. Extend this to a basis {v₁, ..., vₖ, vₖ₊₁, ..., vₙ} for V, where n = dim(V). It can be shown that {T(vₖ₊₁), ..., T(vₙ)} is a basis for Range(T). Thus, rank(T) = n - k. Therefore, rank(T) + nullity(T) = (n-k) + k = n.

UNIT IV: Eigenvalues and Eigenvectors

Question 8 (b)

[5 Marks]

State and prove Cayley-Hamilton theorem.

Statement: Every square matrix satisfies its own characteristic equation.

Proof Outline: Let p(λ) = det(A - λI) be the characteristic polynomial. Using the property that B * adj(B) = det(B) * I, where B = (A - λI), and equating coefficients of the powers of λ, we show that p(A) = O (zero matrix).

UNIT V: Inner Product Spaces

Question 9 (a)

[4 Marks]

Define an inner product space. Show ||x|| ≥ 0 and ||x|| = 0 iff x = 0.

An inner product space is a vector space V over F with a function (u, v) satisfying:

  • (u + v, w) = (u, w) + (v, w)
  • (αu, v) = α(u, v)
  • (u, v) = conjugate of (v, u)
  • (u, u) ≥ 0 and (u, u) = 0 iff u = 0.

Norm property: Since ||x|| = √(x, x) and (x, x) ≥ 0 by definition, ||x|| ≥ 0. Also, ||x|| = 0 implies √(x, x) = 0 ⇒ (x, x) = 0, which by the last axiom means x = 0.