Subspaces

At a Glance

Why This Chapter Matters

Subspaces appear in Section A every few years — 6 of 13 years, always for 3–15 marks — and the questions divide cleanly into two archetypes that each have a mechanical procedure. The first archetype is entirely driven by one formula: dim⁡(U+W)=dim⁡U+dim⁡W−dim⁡(U∩W)\dim(U+W) = \dim U + \dim W - \dim(U\cap W); once you know it, dimension questions become arithmetic. The second archetype is a three-step proof that is identical every time: show 0∈S\mathbf{0}\in S, closure under addition, closure under scalar multiplication. Master these two tools and subspace questions are free marks.

Minimum Theory

Subspace definition and test. A non-empty subset SS of a vector space VV over R\mathbb{R} is a subspace if and only if for all u,v∈S\mathbf{u},\mathbf{v}\in S and all α,β∈R\alpha,\beta\in\mathbb{R}, αu+βv∈S.\alpha\mathbf{u}+\beta\mathbf{v}\in S. Equivalently (and most useful for exam proofs): (i) 0∈S\mathbf{0}\in S, (ii) SS is closed under addition (u+v∈S\mathbf{u}+\mathbf{v}\in S), and (iii) SS is closed under scalar multiplication (cu∈Sc\mathbf{u}\in S). A subset defined by a system of homogeneous linear equations always passes this test — the zero vector satisfies every homogeneous equation, and linearity ensures closure.

Kernels are subspaces. The solution set of Ax=0A\mathbf{x}=\mathbf{0} (the null space / kernel of AA) is always a subspace of Rn\mathbb{R}^n. This is the key structural fact: any set of constraints of the form L(x)=0L(\mathbf{x})=\mathbf{0} for a linear map LL is a subspace. UPSC regularly disguises subspaces as solution sets of systems.

Intersection and sum. If U,WU,W are subspaces of VV then U∩WU\cap W (vectors in both) is a subspace, and U+W={u+w:u∈U,w∈W}U+W = \{\mathbf{u}+\mathbf{w}: \mathbf{u}\in U, \mathbf{w}\in W\} (all sums) is a subspace. Their dimensions satisfy the dimension formula: dim⁡(U+W)=dim⁡U+dim⁡W−dim⁡(U∩W).\dim(U+W) = \dim U + \dim W - \dim(U\cap W). Rearranging gives bounds on the intersection: dim⁡U+dim⁡W−dim⁡V  ≤  dim⁡(U∩W)  ≤  min⁡(dim⁡U,dim⁡W).\dim U + \dim W - \dim V \;\le\; \dim(U\cap W) \;\le\; \min(\dim U,\dim W).

Dimension formula geometry

Question Archetypes

ArchetypeRecognition
intersection-sum-dimensionGiven subspaces U,WU,W; find or bound dim⁡(U∩W)\dim(U\cap W) or dim⁡(U+W)\dim(U+W)
subspace-verification”Prove SS is a subspace”, “show the set is a vector space”, find a basis and dimension
direct-sum-complementGiven a subspace HH, construct KK with V=H⊕KV=H\oplus K

intersection-sum-dimension (3 question(s); 2014, 2016, 2017)

Compute or bound dim of intersection and sum of subspaces via the dimension formula

Recognition Cues

Solution Template

  1. Identify dim⁡U\dim U and dim⁡W\dim W directly or by row-reduction.
  2. Find dim⁡(U+W)\dim(U+W) by checking whether U+WU+W fills VV, or by row-reducing the combined spanning set.
  3. Apply the formula: dim⁡(U∩W)=dim⁡U+dim⁡W−dim⁡(U+W)\dim(U\cap W) = \dim U + \dim W - \dim(U+W).
  4. For abstract problems: use the bounds max⁡(dim⁡U,dim⁡W)≤dim⁡(U+W)≤dim⁡V\max(\dim U,\dim W)\le\dim(U+W)\le\dim V to enumerate possible values, then eliminate values ruled out by extra conditions (e.g. distinctness).
  5. Exhibit an example for each remaining value to confirm it is actually attained.

Worked Example 1

2014 Paper 1, 2014-P1-Q1a (10 marks)

VV is the xyxy-plane in R3\mathbb{R}^3 and W=span⁡{(1,2,3),(1,−1,1)}W = \operatorname{span}\{(1,2,3),(1,-1,1)\}. Find a vector that generates V∩WV\cap W.

Step 1 — Describe WW explicitly. A general element of WW is w=a(1,2,3)+b(1,−1,1)=(a+b,  2a−b,  3a+b).\mathbf{w} = a(1,2,3)+b(1,-1,1) = (a+b,\;2a-b,\;3a+b).

Step 2 — Impose membership in VV. VV is the xyxy-plane, so membership requires the zz-coordinate to be zero: 3a+b=0  ⟹  b=−3a.3a+b=0 \;\Longrightarrow\; b=-3a.

Step 3 — Substitute back. With b=−3ab=-3a: w=(a−3a,  2a+3a,  0)=a(−2,5,0).\mathbf{w} = (a-3a,\;2a+3a,\;0) = a(-2,5,0).

Conclusion. V∩W=span⁡{(−2,5,0)}V\cap W = \operatorname{span}\{(-2,5,0)\} is one-dimensional, generated by (−2,5,0)(-2,5,0).

Dimension check: dim⁡V=2\dim V = 2, dim⁡W=2\dim W = 2, dim⁡(V+W)=3\dim(V+W)=3 (since WW has a non-zero zz-component while VV does not, so V+W=R3V+W=\mathbb{R}^3). The formula gives dim⁡(V∩W)=2+2−3=1\dim(V\cap W)=2+2-3=1. Consistent.

Worked Example 2

2016 Paper 1, 2016-P1-Q1b-ii (3 marks)

W1: x+y−z=0W_1:\,x+y-z=0, W2: 3x+y−2z=0W_2:\,3x+y-2z=0, W3: x−7y+3z=0W_3:\,x-7y+3z=0. Find dim⁡(W1∩W2∩W3)\dim(W_1\cap W_2\cap W_3) and dim⁡(W1+W2)\dim(W_1+W_2).

Form the matrix of normal vectors and row-reduce: M=(11−131−21−73)→R2−3R1, R3−R1(11−10−210−84)→R3−4R2(11−10−21000).M = \begin{pmatrix}1&1&-1\\3&1&-2\\1&-7&3\end{pmatrix} \xrightarrow{R_2-3R_1,\,R_3-R_1} \begin{pmatrix}1&1&-1\\0&-2&1\\0&-8&4\end{pmatrix} \xrightarrow{R_3-4R_2} \begin{pmatrix}1&1&-1\\0&-2&1\\0&0&0\end{pmatrix}.

rank⁡(M)=2\operatorname{rank}(M)=2, so the solution space of all three equations simultaneously has dim⁡(W1∩W2∩W3)=3−2=1\dim(W_1\cap W_2\cap W_3) = 3-2 = \boxed{1}.

For W1+W2W_1+W_2: their normal vectors (1,1,−1)(1,1,-1) and (3,1,−2)(3,1,-2) are non-parallel, so W1≠W2W_1\ne W_2; both are 2-dimensional planes in R3\mathbb{R}^3 that share a line, hence dim⁡(W1+W2)=3\dim(W_1+W_2) = \boxed{3}.

Worked Example 3

2017 Paper 1, 2017-P1-Q2d (10 marks)

UU and WW are distinct 4-dimensional subspaces of a 6-dimensional vector space VV. Find all possible values of dim⁡(U∩W)\dim(U\cap W).

Step 1 — Apply the dimension formula. dim⁡(U∩W)=dim⁡U+dim⁡W−dim⁡(U+W)=4+4−dim⁡(U+W)=8−dim⁡(U+W).\dim(U\cap W) = \dim U + \dim W - \dim(U+W) = 4+4-\dim(U+W) = 8-\dim(U+W).

Step 2 — Bound dim⁡(U+W)\dim(U+W). Since U+W⊆VU+W\subseteq V and dim⁡V=6\dim V=6: 4  ≤  dim⁡(U+W)  ≤  6⟹2  ≤  dim⁡(U∩W)  ≤  4.4 \;\le\; \dim(U+W) \;\le\; 6 \qquad\Longrightarrow\qquad 2\;\le\;\dim(U\cap W)\;\le\; 4.

Step 3 — Eliminate the value 4. If dim⁡(U∩W)=4\dim(U\cap W)=4, then U∩WU\cap W is a 4-dimensional subspace of UU; but dim⁡U=4\dim U=4, so U∩W=UU\cap W = U, forcing U⊆WU\subseteq W. Similarly W⊆UW\subseteq U, giving U=WU=W. This contradicts the hypothesis that UU and WW are distinct. So dim⁡(U∩W)≠4\dim(U\cap W)\ne 4.

Step 4 — Exhibit subspaces for the remaining values.

Conclusion. dim⁡(U∩W)∈{2,3}\dim(U\cap W)\in\{2,3\}.

Common Traps

subspace-verification (3 question(s); 2020, 2021, 2022)

Apply the subspace test (zero vector + closure); find bases and dimension

Recognition Cues

Solution Template

  1. Identify the ambient space (usually Rn\mathbb{R}^n or Mn(R)M_n(\mathbb{R})).
  2. Check 0∈S\mathbf{0}\in S: substitute the zero element and verify all conditions are satisfied.
  3. Check closure under addition: take arbitrary u,v∈S\mathbf{u},\mathbf{v}\in S (satisfying the defining condition) and verify u+v\mathbf{u}+\mathbf{v} satisfies the same condition. Use linearity.
  4. Check closure under scalar multiplication: take u∈S\mathbf{u}\in S and c∈Rc\in\mathbb{R}; verify cu∈Sc\mathbf{u}\in S.
  5. Conclude SS is a subspace by the subspace criterion.
  6. Find a basis: write a general element of SS as a linear combination of a minimal spanning set; identify the vectors in the combination.
  7. State the dimension as the number of vectors in the basis.

Worked Example 1

2020 Paper 1, 2020-P1-Q1a (10 marks)

Show that the set MM of all n×nn\times n real magic squares is a vector space over R\mathbb{R}. Give two distinct 2×22\times 2 magic squares.

A magic square (in the linear-algebra sense used here) is an n×nn\times n real matrix in which all row sums and all column sums are equal. The set of all such matrices is a subset of Mn(R)M_n(\mathbb{R}); it suffices to show it is a subspace.

Subspace proof. Let A,B∈MA,B\in M (both are magic with equal line sum sAs_A and sBs_B respectively) and c∈Rc\in\mathbb{R}.

Since MM is a non-empty subset of the vector space Mn(R)M_n(\mathbb{R}) closed under addition and scalar multiplication, MM is a subspace — hence a vector space over R\mathbb{R}.

Two distinct 2×22\times 2 magic squares. For n=2n=2, a magic square requires all four row and column sums to be equal. Let the common sum be kk; then every entry can be taken equal to k/2k/2, giving the constant matrix k2J\frac{k}{2}J where JJ is the all-ones matrix. Thus every 2×22\times 2 magic square is a constant matrix:

A=(1111),B=(3333).A = \begin{pmatrix}1&1\\1&1\end{pmatrix}, \qquad B = \begin{pmatrix}3&3\\3&3\end{pmatrix}.

Both have equal line sums (22 and 66 respectively), and A≠BA\ne B.

Worked Example 2

2021 Paper 1, 2021-P1-Q2c (15 marks)

Let S={(x,2y,3x):x,y∈R}S = \{(x,2y,3x) : x,y\in\mathbb{R}\}. Show that SS is a subspace of R3\mathbb{R}^3, find two distinct bases for SS, and determine its dimension.

Part (i) — SS is a subspace.

Hence SS is a subspace of R3\mathbb{R}^3.

Part (ii) — Spanning set. Every element of SS has the form (x,2y,3x)=x(1,0,3)+y(0,2,0).(x,2y,3x) = x(1,0,3) + y(0,2,0). The two vectors (1,0,3)(1,0,3) and (0,2,0)(0,2,0) span SS and are clearly linearly independent (neither is a scalar multiple of the other). Hence

B1={(1,0,3),  (0,2,0)}\mathbf{B}_1 = \{(1,0,3),\;(0,2,0)\}

is a basis for SS.

Part (iii) — A second basis. Any two linearly independent vectors in SS form a basis. Take x=2,y=0x=2,y=0: gives (2,0,6)(2,0,6); take x=0,y=1x=0,y=1: gives (0,2,0)(0,2,0). These are independent (not scalar multiples), so

B2={(2,0,6),  (0,1,0)}\mathbf{B}_2 = \{(2,0,6),\;(0,1,0)\}

is a second basis for SS. (Note (0,1,0)=12(0,2,0)∈S(0,1,0) = \frac{1}{2}(0,2,0)\in S since x=0,y=12x=0,y=\frac{1}{2}.)

Part (iv) — Dimension. Both bases contain exactly 2 vectors, so dim⁡S=2\dim S = 2.

Worked Example 3

2022 Paper 1, 2022-P1-Q3a-i (10 marks)

Let P={x∈R3:x−y−z=0,  2x−y+z=0}P = \{\mathbf{x}\in\mathbb{R}^3 : x-y-z=0,\; 2x-y+z=0\}. Prove that PP is a subspace of R3\mathbb{R}^3.

PP is the solution set of the homogeneous linear system Ax=0A\mathbf{x}=\mathbf{0} with A=(1−1−12−11).A = \begin{pmatrix}1&-1&-1\\2&-1&1\end{pmatrix}. We apply the three-part subspace test directly.

Zero vector. (0,0,0)(0,0,0) satisfies both equations (0−0−0=00-0-0=0 and 2⋅0−0+0=02\cdot 0-0+0=0), so 0∈P\mathbf{0}\in P. ✓\checkmark

Closure under addition. Let u=(x1,y1,z1)∈P\mathbf{u}=(x_1,y_1,z_1)\in P and v=(x2,y2,z2)∈P\mathbf{v}=(x_2,y_2,z_2)\in P, so x1−y1−z1=0,2x1−y1+z1=0,x2−y2−z2=0,2x2−y2+z2=0.x_1-y_1-z_1=0,\quad 2x_1-y_1+z_1=0,\quad x_2-y_2-z_2=0,\quad 2x_2-y_2+z_2=0. Consider u+v=(x1+x2, y1+y2, z1+z2)\mathbf{u}+\mathbf{v}=(x_1+x_2,\,y_1+y_2,\,z_1+z_2): (x1+x2)−(y1+y2)−(z1+z2)=(x1−y1−z1)+(x2−y2−z2)=0+0=0,(x_1+x_2)-(y_1+y_2)-(z_1+z_2) = (x_1-y_1-z_1)+(x_2-y_2-z_2) = 0+0=0, 2(x1+x2)−(y1+y2)+(z1+z2)=(2x1−y1+z1)+(2x2−y2+z2)=0+0=0.2(x_1+x_2)-(y_1+y_2)+(z_1+z_2) = (2x_1-y_1+z_1)+(2x_2-y_2+z_2) = 0+0=0. So u+v∈P\mathbf{u}+\mathbf{v}\in P. ✓\checkmark

Closure under scalar multiplication. Let u=(x1,y1,z1)∈P\mathbf{u}=(x_1,y_1,z_1)\in P and c∈Rc\in\mathbb{R}; consider cu=(cx1,cy1,cz1)c\mathbf{u}=(cx_1,cy_1,cz_1): cx1−cy1−cz1=c(x1−y1−z1)=c⋅0=0,cx_1-cy_1-cz_1 = c(x_1-y_1-z_1) = c\cdot 0 = 0, 2cx1−cy1+cz1=c(2x1−y1+z1)=c⋅0=0.2cx_1-cy_1+cz_1 = c(2x_1-y_1+z_1) = c\cdot 0 = 0. So cu∈Pc\mathbf{u}\in P. ✓\checkmark

Since PP satisfies all three conditions, PP is a subspace of R3\mathbb{R}^3. ■\blacksquare

Common Traps

direct-sum-complement (1 question(s); 2026)

Recognition Cues

Solution Template

  1. Find dim⁡H\dim H — for a single non-trivial linear condition it is dim⁡V−1\dim V-1.
  2. Pick any vector v∉Hv\notin H; set K=span⁡{v}K=\operatorname{span}\{v\}. Choose the simplest such vv.
  3. Prove H∩K={0}H\cap K=\{0\}: a general element of KK is cvcv; the condition defining HH forces c=0c=0.
  4. Prove H+K=VH+K=V by dimension: dim⁡H+dim⁡K=dim⁡V\dim H+\dim K=\dim V together with H∩K={0}H\cap K=\{0\} gives the direct sum.
  5. Say explicitly that KK is not unique.

Worked Example(s)

2026 Paper 1, 2026-P1-Q3a-ii (7 marks)

H={p∈P3[x] : ∫−11p(x) dx=0}H=\left\{p\in P_3[x]\ :\ \int_{-1}^{1}p(x)\,dx=0\right\}. Find KK with P3[x]=H⊕KP_3[x]=H\oplus K.

For p=a0+a1x+a2x2+a3x3p=a_0+a_1x+a_2x^2+a_3x^3, the odd powers integrate to zero over [−1,1][-1,1], so ∫−11p dx=2a0+23a2.\int_{-1}^{1}p\,dx=2a_0+\tfrac23a_2 . The condition 2a0+23a2=02a_0+\tfrac23a_2=0 is one non-trivial linear equation on a 4-dimensional space, so dim⁡H=3\dim H=3, with basis { x, x3, x2−13 }\{\,x,\ x^3,\ x^2-\tfrac13\,\}.

Take K=span⁡{1}K=\operatorname{span}\{1\}. Then for p=c∈Kp=c\in K, ∫−11c dx=2c\int_{-1}^{1}c\,dx=2c, which vanishes only if c=0c=0; hence H∩K={0}H\cap K=\{0\}. Since dim⁡H+dim⁡K=3+1=4=dim⁡P3[x]\dim H+\dim K=3+1=4=\dim P_3[x],   P3[x]=H⊕K,K=span⁡{1}.  \boxed{\;P_3[x]=H\oplus K,\qquad K=\operatorname{span}\{1\}.\;}

Any polynomial with non-zero integral would serve equally — K=span⁡{x2}K=\operatorname{span}\{x^2\} works too. Say so.

Common Traps

Marks-Aware Writing

For a 10-mark subspace proof: Show all three parts of the subspace test — zero vector, closure under addition, closure under scalar multiplication — as separate, labelled steps. Each step should display the algebra explicitly (do not say “by linearity” and skip the calculation). A correct conclusion with no intermediate algebra earns at most 3–4 marks.

For finding a basis and dimension (part of a 10- or 15-mark question): Write the general element in the form xv1+yv2+…x\mathbf{v}_1 + y\mathbf{v}_2 + \ldots, identify v1,v2,…\mathbf{v}_1,\mathbf{v}_2,\ldots as your spanning set, then state why they are linearly independent. Dimension = number of basis vectors — state this explicitly.

For a 15-mark question: The extra marks reward completeness. Write all parts in full: the subspace proof, both bases with explicit independence arguments, and the dimension statement. A one-sentence basis identification without the independence check will lose marks.

For a 3-mark dimension question: Be concise. Row-reduce the matrix, read off the rank, apply nullity =n−rank⁡= n - \operatorname{rank}. A single well-annotated row-reduction with the final answer is sufficient.

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