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MHT-CET Maths · Vectors

Linear Combinations, Collinearity, and Coplanarity

Building one vector out of others as m·a + n·b, and the structural conditions hiding inside that idea — collinear vectors need ONE scalar, coplanar vectors need TWO, and a dependent set is exactly a coplanar one.

Why this matters

25 PYQs sit here, and they lean hard: 10 HARD, 12 MODERATE, 3 EASY. Two recurring shapes dominate: the collinearity / coplanarity conditions (one-scalar vs two-scalar, and the 'no two collinear' chain systems), and the linear-system shapes — express a vector as m·b + n·c, find components against a transformed basis, or test linear dependence by a determinant. Get the counting right (collinear ⇒ 1 scalar, coplanar / dependent ⇒ 2 scalars) and almost every question here reduces to a small system you can solve by equating components.

Concept 1 of 10: Linear combination of vectors

A linear combination is just \"scale each vector by a number, then add them up.\" If you have vectors a⃗\vec{a} and b⃗\vec{b}, then ma⃗+nb⃗m\vec{a} + n\vec{b} — for any real numbers m,nm, n — is a linear combination of them. Every expression you'll meet in this subtopic (2a⃗−b⃗+3c⃗2\vec{a} - \vec{b} + 3\vec{c}, 3a⃗+b⃗−2c⃗3\vec{a} + \vec{b} - 2\vec{c}, …) is one of these. The whole topic is about what happens when ONE vector turns out to BE a linear combination of others.

Definition

A vector r⃗\vec{r} is a linear combination of vectors a⃗1,a⃗2,…,a⃗k\vec{a}_1, \vec{a}_2, \dots, \vec{a}_k if there exist scalars c1,c2,…,ckc_1, c_2, \dots, c_k such that r⃗=c1a⃗1+c2a⃗2+⋯+cka⃗k\vec{r} = c_1\vec{a}_1 + c_2\vec{a}_2 + \dots + c_k\vec{a}_k. In the standard basis you build a combination componentwise: ma⃗+nb⃗m\vec{a} + n\vec{b} has i^\hat{i}-component ma1+nb1ma_1 + nb_1, j^\hat{j}-component ma2+nb2ma_2 + nb_2, and so on. The scalars are called the coefficients of the combination.

Linear combination

r⃗=ma⃗+nb⃗+pc⃗ri=mai+nbi+pci\vec{r} = m\vec{a} + n\vec{b} + p\vec{c} \qquad r_i = ma_i + nb_i + pc_i
  • m,n,pm, n, preal scalar coefficients (any sign, including zero)
  • r⃗\vec{r}the combined vector — built componentwise

Diagram · component form (drag to rotate)

xyzv

Step along x (2.4 î), then y ( 1.6 ĵ), then z ( 2.0 k̂) to reach the tip: v = 2.4 î + 1.6 ĵ + 2.0 k̂. Any vector is the sum of its axis components, and |v| = √(x² + y² + z²).

Worked example

For a⃗=i^+k^\vec{a} = \hat{i} + \hat{k}, b⃗=2i^−j^\vec{b} = 2\hat{i} - \hat{j} and c⃗=j^+3k^\vec{c} = \hat{j} + 3\hat{k}, compute the linear combination 2a⃗−b⃗+c⃗2\vec{a} - \vec{b} + \vec{c} in component form.
Practise this concept4 quick reps

A linear combination is built componentwise — three sums, not one

When you form ma⃗+nb⃗+pc⃗m\vec{a} + n\vec{b} + p\vec{c} in 3-D you are doing three independent scalar sums: one for i^\hat{i}, one for j^\hat{j}, one for k^\hat{k}. Combine the wrong components and the answer is silently wrong — keep the three columns separated.

Concept 2 of 10: Collinear vectors and collinear points (one scalar)

Two vectors are collinear (parallel) when they point along the SAME line of direction — same way or exactly opposite. That happens exactly when one is a scalar multiple of the other: a⃗=kb⃗\vec{a} = k\vec{b}. ONE scalar settles it. For three POINTS, collinearity is the point-version: A,B,CA, B, C lie on one line when the displacement AC⃗\vec{AC} is a scalar multiple of AB⃗\vec{AB}.

Definition

  • Collinear vectors: a⃗\vec{a} and b⃗\vec{b} (b⃗≠0⃗\vec{b} \neq \vec{0}) are collinear iff a⃗=kb⃗\vec{a} = k\vec{b} for some scalar kk. In components, this means the components are proportional: a1b1=a2b2=a3b3\dfrac{a_1}{b_1} = \dfrac{a_2}{b_2} = \dfrac{a_3}{b_3}.
  • Collinear points: A,B,CA, B, C (position vectors a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c}) are collinear iff AB⃗∥AC⃗\vec{AB} \parallel \vec{AC}, i.e. b⃗−a⃗=k(c⃗−a⃗)\vec{b} - \vec{a} = k(\vec{c} - \vec{a}) for some scalar kk.
  • \"is collinear with\": the phrase u⃗\vec{u} is collinear with v⃗\vec{v} means u⃗=tv⃗\vec{u} = t\vec{v} — introduce ONE unknown scalar and solve.

Collinearity (one scalar)

a⃗=kb⃗a1b1=a2b2=a3b3\vec{a} = k\vec{b} \qquad \dfrac{a_1}{b_1} = \dfrac{a_2}{b_2} = \dfrac{a_3}{b_3}
  • kkthe single scalar; k>0k > 0 same direction, k<0k < 0 opposite

Worked example

Are a⃗=2i^−3j^+4k^\vec{a} = 2\hat{i} - 3\hat{j} + 4\hat{k} and b⃗=−4i^+6j^−8k^\vec{b} = -4\hat{i} + 6\hat{j} - 8\hat{k} collinear? If so, find kk with b⃗=ka⃗\vec{b} = k\vec{a}.
Practise this conceptself-check · 4 quick reps

Collinear needs ONE scalar; coplanar needs TWO — keep the count straight

Whenever a question says \"collinear / parallel,\" introduce ONE unknown scalar (u⃗=tv⃗\vec{u} = t\vec{v}). If it says \"coplanar / lies in the plane of,\" introduce TWO (u⃗=mv⃗+nw⃗\vec{u} = m\vec{v} + n\vec{w}). Mixing up the count is the single biggest source of wrong setups in this subtopic.

Parallel VECTORS vs collinear POINTS

Vectors are parallel when they share a direction; points are collinear when they share a LINE. Test points by displacements: A,B,CA, B, C collinear iff AB⃗∥AC⃗\vec{AB} \parallel \vec{AC} (they share point AA), not merely \"some pair of vectors is parallel.\"

Concept 3 of 10: Coplanar vectors (two scalars)

Three vectors are coplanar when you can draw all three flat in one plane. If b⃗\vec{b} and c⃗\vec{c} are not collinear, they span a plane; a third vector a⃗\vec{a} lies in that plane exactly when it's a linear combination of them: a⃗=mb⃗+nc⃗\vec{a} = m\vec{b} + n\vec{c}. TWO scalars, because the plane is 2-dimensional. A vector that lies in / bisects / is perpendicular within the plane of b⃗,c⃗\vec{b}, \vec{c} is always of this form.

Definition

Three vectors a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} are coplanar iff one is a linear combination of the other two: a⃗=mb⃗+nc⃗\vec{a} = m\vec{b} + n\vec{c} for some scalars m,nm, n (assuming b⃗,c⃗\vec{b}, \vec{c} non-collinear). Equivalently, their scalar triple product vanishes: [a⃗ b⃗ c⃗]=0[\vec{a}\ \vec{b}\ \vec{c}] = 0, i.e. the determinant of their components is zero. \"Lies in the plane of b⃗\vec{b} and c⃗\vec{c}\" is the phrase that signals this: write the unknown vector as mb⃗+nc⃗m\vec{b} + n\vec{c} and solve for m,nm, n.

Coplanarity (two scalars)

a⃗=mb⃗+nc⃗[a⃗ b⃗ c⃗]=∣a1a2a3b1b2b3c1c2c3∣=0\vec{a} = m\vec{b} + n\vec{c} \qquad [\vec{a}\ \vec{b}\ \vec{c}] = \begin{vmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3 \end{vmatrix} = 0
  • m,nm, nthe two scalars — one per spanning vector
  • [a⃗ b⃗ c⃗][\vec{a}\ \vec{b}\ \vec{c}]scalar triple product; zero ⟺ coplanar

Diagram · orthonormal triple î, ĵ, k̂ (drag to rotate)

îĵk̂

î, ĵ, k̂ are mutually perpendicular unit vectors: each pair has dot product 0 (î·ĵ = ĵ·k̂ = k̂·î = 0) and each has length 1. They form the standard basis — any vector is a unique combination x î + y ĵ + z k̂.

Worked example

Show that a⃗=3i^+j^\vec{a} = 3\hat{i} + \hat{j} is coplanar with b⃗=i^+j^\vec{b} = \hat{i} + \hat{j} and c⃗=i^−j^\vec{c} = \hat{i} - \hat{j} by writing a⃗=mb⃗+nc⃗\vec{a} = m\vec{b} + n\vec{c}.
Practise this concept4 quick reps

Coplanar / dependent ⇒ TWO scalars and a vanishing determinant

For coplanarity you have two equivalent tests: express the vector as mb⃗+nc⃗m\vec{b} + n\vec{c} (good when you need m,nm, n), or set the 3×33\times 3 determinant of the components to zero (good for a yes/no or for finding an unknown component). Pick the test that matches what the question asks for.

Concept 4 of 10: Forming a combination, then normalising / measuring it

The most common EASY-to-MODERATE shape: you're handed a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} and asked about a combination like 2a⃗−b⃗+3c⃗2\vec{a} - \vec{b} + 3\vec{c} — find a unit vector along it, a parallel vector of given magnitude, or the angle it makes with another combination. The skill is the same every time: BUILD the combination componentwise first, then apply magnitude / unit-vector / angle as a second step.

Definition

Once a linear combination r⃗=ma⃗+nb⃗+pc⃗\vec{r} = m\vec{a} + n\vec{b} + p\vec{c} is built componentwise, the follow-up is routine:

  • Unit vector along r⃗\vec{r}: r^=r⃗/∣r⃗∣\hat{r} = \vec{r}/|\vec{r}|.
  • Vector of magnitude MM parallel to r⃗\vec{r}: M r^=M∣r⃗∣r⃗M\,\hat{r} = \dfrac{M}{|\vec{r}|}\vec{r}.
  • Angle between two combinations u⃗,v⃗\vec{u}, \vec{v}: cos⁡θ=u⃗⋅v⃗∣u⃗∣ ∣v⃗∣\cos\theta = \dfrac{\vec{u}\cdot\vec{v}}{|\vec{u}|\,|\vec{v}|}.

Scale a combination to a target magnitude

M r^=M∣r⃗∣ r⃗r^=r⃗∣r⃗∣M\,\hat{r} = \dfrac{M}{|\vec{r}|}\,\vec{r} \qquad \hat{r} = \dfrac{\vec{r}}{|\vec{r}|}
  • r⃗\vec{r}the linear combination, built first
  • MMthe required magnitude of the parallel vector

Worked example

Given a⃗=i^+2j^\vec{a} = \hat{i} + 2\hat{j}, b⃗=3i^−j^\vec{b} = 3\hat{i} - \hat{j}, find a vector of magnitude 1010 parallel to a⃗+b⃗\vec{a} + \vec{b}.
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2023 · 10th May Shift 2 · Q124Easy

Example 4 · Vectors · Linear Combinations, Collinearity, and Coplanarity

Given a⃗=i^+j^+k^\vec{a} = \hat{i}+\hat{j}+\hat{k}, b⃗=4i^−2j^+3k^\vec{b} = 4\hat{i}-2\hat{j}+3\hat{k}, c⃗=i^−2j^+k^\vec{c} = \hat{i}-2\hat{j}+\hat{k}, then a vector of magnitude 6 units, which is parallel to the vector 2a⃗−b⃗+3c⃗2\vec{a}-\vec{b}+3\vec{c}, is

Build the combination BEFORE you normalise — don't normalise the parts

To find a unit vector along 3a⃗+b⃗−2c⃗3\vec{a} + \vec{b} - 2\vec{c} you must combine first, get one vector, THEN divide by its magnitude. Normalising a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} separately and combining the unit vectors gives a different (wrong) direction.

\"Parallel of magnitude MM\" has TWO answers — ±Mr^\pm M\hat{r}

A vector parallel to r⃗\vec{r} can point the same way OR the opposite way, so both +Mr^+M\hat{r} and −Mr^-M\hat{r} qualify. MCQs usually list only one; if your computed direction isn't an option, check its negative before assuming an error.

Concept 5 of 10: Collinearity of three points (and who lies between)

Three points P,Q,RP, Q, R are collinear when the segments between them lie on one straight line. Test it by displacements: compute PQ⃗\vec{PQ} and QR⃗\vec{QR} (or PR⃗\vec{PR}); if one is a scalar multiple of the other AND they share a common point, the points are collinear. The sign and size of the multiplier even tells you the ORDER — who sits between whom.

Definition

Points P,Q,RP, Q, R with position vectors p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} are collinear iff QR⃗=k PQ⃗\vec{QR} = k\,\vec{PQ} (or any two displacements are proportional), where PQ⃗=q⃗−p⃗\vec{PQ} = \vec{q} - \vec{p}, QR⃗=r⃗−q⃗\vec{QR} = \vec{r} - \vec{q}. Reading the multiplier: if QR⃗=k PQ⃗\vec{QR} = k\,\vec{PQ} with k>0k > 0, the line goes P→Q→RP \to Q \to R so QQ lies between PP and RR; a negative kk means RR doubles back, putting a different point in the middle.

Three-point collinearity

QR⃗=k PQ⃗wherePQ⃗=q⃗−p⃗,  QR⃗=r⃗−q⃗\vec{QR} = k\,\vec{PQ} \quad\text{where}\quad \vec{PQ} = \vec{q} - \vec{p},\ \ \vec{QR} = \vec{r} - \vec{q}
  • kkthe scalar; its sign/size fixes the order of the points

Worked example

Are P(i^+j^)P(\hat{i} + \hat{j}), Q(3i^+3j^)Q(3\hat{i} + 3\hat{j}), R(6i^+6j^)R(6\hat{i} + 6\hat{j}) collinear? Who lies between?
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2023 · 15th May Shift 2 · Q125Moderate

Example 5 · Vectors · Linear Combinations, Collinearity, and Coplanarity

If the points P,QP, Q and RR are with the position vectors i^−2j^+3k^, −2i^+3j^+2k^\hat{i}-2\hat{j}+3\hat{k},\,-2\hat{i}+3\hat{j}+2\hat{k} and −8i^+13j^-8\hat{i}+13\hat{j} respectively, then these points are

Use displacements that SHARE a point

To conclude collinearity from QR⃗=kPQ⃗\vec{QR} = k\vec{PQ} the two displacements must share a common point (here QQ). Two parallel displacements that don't share a point only say the segments are parallel — not that all the points lie on ONE line.

Concept 6 of 10: Express a vector as a combination of two others

Given a⃗=mb⃗+nc⃗\vec{a} = m\vec{b} + n\vec{c}, the question hands you a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} and asks for the scalars m,nm, n. Equate components and you get a small linear system — usually two equations from two of the three components are enough; the third is a free consistency check. Solve, then read off whatever the question wants (often m+nm + n or mnmn).

Definition

To write a⃗=mb⃗+nc⃗\vec{a} = m\vec{b} + n\vec{c}: equate each component to get the system a1=mb1+nc1a_1 = mb_1 + nc_1, a2=mb2+nc2a_2 = mb_2 + nc_2, a3=mb3+nc3a_3 = mb_3 + nc_3. Pick the two simplest equations, solve for mm and nn, then verify with the remaining one. If the third equation also holds, the representation is valid (the three vectors are coplanar); if it fails, no such m,nm, n exist.

Two-equation linear system

{a1=mb1+nc1a2=mb2+nc2 ⇒ m, n\begin{cases} a_1 = mb_1 + nc_1 \\ a_2 = mb_2 + nc_2 \end{cases}\ \Rightarrow\ m,\ n
  • m,nm, ncoefficients to solve for; the 3rd component is the check

Worked example

Write a⃗=5i^+j^\vec{a} = 5\hat{i} + \hat{j} as mb⃗+nc⃗m\vec{b} + n\vec{c} with b⃗=i^+j^\vec{b} = \hat{i} + \hat{j}, c⃗=i^−j^\vec{c} = \hat{i} - \hat{j}. Find m+nm + n.
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2024 · 10th May Shift 2 · Q136Moderate

Example 6 · Vectors · Linear Combinations, Collinearity, and Coplanarity

If a⃗=mb⃗+nc⃗\vec{a}=m\vec{b}+n\vec{c}, where a⃗=4i^+13j^−18k^\vec{a}=4\hat{i}+13\hat{j}-18\hat{k}, b⃗=i^−2j^+3k^\vec{b}=\hat{i}-2\hat{j}+3\hat{k}, c⃗=2i^+3j^−4k^\vec{c}=2\hat{i}+3\hat{j}-4\hat{k}, then m+n=m+n=

Solve from two equations, but ALWAYS verify with the third

Two component-equations pin down m,nm, n — but in 3-D there's a third equation. If it doesn't hold, no valid m,nm, n exist (the vectors aren't coplanar). Skipping the check can hand you scalars that don't actually reproduce a⃗\vec{a}.

Concept 7 of 10: Linear dependence vs independence (the determinant test)

A set of vectors is dependent when one of them is \"redundant\" — expressible from the others, so they don't truly fill the space. In 3-D, three vectors are dependent exactly when they're coplanar (one lies in the plane of the other two), and that shows up as a ZERO determinant. Three vectors are independent when only the all-zero combination gives the zero vector — nothing is redundant.

Definition

Vectors a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} are linearly dependent if there exist scalars x,y,zx, y, z, not all zero, with xa⃗+yb⃗+zc⃗=0⃗x\vec{a} + y\vec{b} + z\vec{c} = \vec{0}. They are independent if xa⃗+yb⃗+zc⃗=0⃗x\vec{a} + y\vec{b} + z\vec{c} = \vec{0} forces x=y=z=0x = y = z = 0. Test in 3-D: dependent ⟺ coplanar ⟺ the determinant of their components is zero:

  • [a⃗ b⃗ c⃗]=0[\vec{a}\ \vec{b}\ \vec{c}] = 0 ⇒ dependent / coplanar.
  • [a⃗ b⃗ c⃗]≠0[\vec{a}\ \vec{b}\ \vec{c}] \neq 0 ⇒ independent / non-coplanar (they form a basis).

Dependence ⟺ vanishing determinant

xa⃗+yb⃗+zc⃗=0⃗ (not all 0)  ⟺  ∣a1a2a3b1b2b3c1c2c3∣=0x\vec{a} + y\vec{b} + z\vec{c} = \vec{0}\ (\text{not all }0) \iff \begin{vmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3 \end{vmatrix} = 0
  • x,y,zx, y, zscalars; a non-trivial solution means dependent

Worked example

For what value of λ\lambda are a⃗=i^+j^+k^\vec{a} = \hat{i} + \hat{j} + \hat{k}, b⃗=2i^+3j^+4k^\vec{b} = 2\hat{i} + 3\hat{j} + 4\hat{k}, c⃗=i^+j^+λk^\vec{c} = \hat{i} + \hat{j} + \lambda\hat{k} linearly dependent?
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2024 · 11th May Shift 2 · Q114Moderate

Example 7 · Vectors · Linear Combinations, Collinearity, and Coplanarity

If a⃗=i^+j^+k^\vec{a} = \hat{i}+\hat{j}+\hat{k}, b⃗=4i^+3j^+4k^\vec{b} = 4\hat{i}+3\hat{j}+4\hat{k} and c⃗=i^+αj^+βk^\vec{c} = \hat{i}+\alpha\hat{j}+\beta\hat{k} are linearly dependent vectors and ∣c⃗∣=3|\vec{c}|=\sqrt{3}, then the value of α\alpha and β\beta are respectively.

Linearly dependent = coplanar = zero determinant — three names, one idea

In 3-D these are the SAME condition. A question may phrase it as \"linearly dependent,\" \"coplanar,\" or \"scalar triple product is zero\" — all three send you to the same 3×33\times 3 determinant set to zero.

A second condition (like ∣c⃗∣=3|\vec{c}| = \sqrt{3}) is part of the same problem

Dependence often fixes only ONE unknown (e.g. β=1\beta = 1). A magnitude condition supplies the OTHER (e.g. 1+α2+1=3⇒α=±11 + \alpha^2 + 1 = 3 \Rightarrow \alpha = \pm 1). Use both givens — don't stop after the determinant.

Concept 8 of 10: Chained-collinearity systems (\"no two collinear\")

The signature HARD shape: a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} are three vectors, no two collinear, and you're told two collinearity facts — \"a⃗+2b⃗\vec{a} + 2\vec{b} is collinear with c⃗\vec{c}\" and \"b⃗+3c⃗\vec{b} + 3\vec{c} is collinear with a⃗\vec{a}.\" Each fact gives ONE scalar equation; equate coefficients of the (independent) a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} on both sides, solve the little system, and the result is forced.

Definition

Turn each \"collinear with\" into a scalar multiple, then use independence to match coefficients:

  • a⃗+2b⃗=tc⃗\vec{a} + 2\vec{b} = t\vec{c} (one scalar tt).
  • b⃗+3c⃗=λa⃗\vec{b} + 3\vec{c} = \lambda\vec{a} (one scalar λ\lambda).

Substitute one into the other and compare coefficients of the linearly-independent a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} (each side's coefficient of a given basis vector must match). This pins down t,λt, \lambda — typically t=−6, λ=−12t = -6,\ \lambda = -\tfrac{1}{2} — and yields the target, e.g. a⃗+2b⃗=−6c⃗\vec{a} + 2\vec{b} = -6\vec{c}, so a⃗+2b⃗+6c⃗=0⃗\vec{a} + 2\vec{b} + 6\vec{c} = \vec{0}.

Chained collinearity

a⃗+2b⃗=tc⃗,b⃗+3c⃗=λa⃗  ⇒  t=−6, λ=−12\vec{a} + 2\vec{b} = t\vec{c}, \quad \vec{b} + 3\vec{c} = \lambda\vec{a} \;\Rightarrow\; t = -6,\ \lambda = -\tfrac{1}{2}
  • t,λt, \lambdaone scalar per collinearity fact; matched via coefficient comparison

Worked example

a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c}: no two collinear. a⃗+2b⃗\vec{a} + 2\vec{b} is collinear with c⃗\vec{c}, and b⃗+3c⃗\vec{b} + 3\vec{c} is collinear with a⃗\vec{a}. Find a⃗+2b⃗\vec{a} + 2\vec{b} in terms of c⃗\vec{c}.
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2024 · 12th May Shift 2 · Q144Moderate

Example 8 · Vectors · Linear Combinations, Collinearity, and Coplanarity

If a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} are three non-zero vectors, no two of them are collinear, a⃗+2b⃗\vec{a}+2\vec{b} is collinear with c⃗\vec{c}, b⃗+3c⃗\vec{b}+3\vec{c} is collinear with a⃗\vec{a}, then a⃗+2b⃗\vec{a}+2\vec{b} is

Match coefficients only because the vectors are independent

\"No two collinear\" (in fact, all three independent) is the hypothesis that LETS you equate coefficients of a⃗,b⃗,c⃗\vec{a}, \vec{b}, \vec{c} on both sides. Without independence you couldn't conclude that equal vector-expressions force equal coefficients.

Read the target carefully: a⃗+2b⃗\vec{a}+2\vec{b} vs a⃗+2b⃗+6c⃗\vec{a}+2\vec{b}+6\vec{c}

The same setup answers two different MCQ targets. If a⃗+2b⃗=−6c⃗\vec{a}+2\vec{b} = -6\vec{c}, then the value of a⃗+2b⃗\vec{a}+2\vec{b} is −6c⃗-6\vec{c}, but the value of a⃗+2b⃗+6c⃗\vec{a}+2\vec{b}+6\vec{c} is 0⃗\vec{0}. Answer the one actually asked.

Concept 9 of 10: A vector lying in the plane of two others

When a question says a vector \"lies in the plane of b⃗\vec{b} and c⃗\vec{c}\" (or is coplanar with them), write it as v⃗=b⃗+λc⃗\vec{v} = \vec{b} + \lambda\vec{c} (or mb⃗+nc⃗m\vec{b} + n\vec{c}) — TWO scalars. Then a SECOND condition (it bisects the angle, is perpendicular to one of them, has a given projection or magnitude) pins down the scalar(s). Coplanarity supplies the FORM; the extra condition supplies the NUMBERS.

Definition

A vector v⃗\vec{v} coplanar with non-collinear b⃗,c⃗\vec{b}, \vec{c} has the form v⃗=mb⃗+nc⃗\vec{v} = m\vec{b} + n\vec{c}. Common second conditions and how to use them:

  • Bisects the angle between b⃗,c⃗\vec{b}, \vec{c}: v⃗\vec{v} is parallel to b^+c^\hat{b} + \hat{c} (sum of the UNIT vectors).
  • Perpendicular to q⃗\vec{q}: impose v⃗⋅q⃗=0\vec{v}\cdot\vec{q} = 0.
  • Projection on c⃗\vec{c} equals kk: impose v⃗⋅c⃗∣c⃗∣=k\dfrac{\vec{v}\cdot\vec{c}}{|\vec{c}|} = k.
  • Magnitude MM: scale the resulting direction to length MM.

Coplanar form + a second condition

v⃗=mb⃗+nc⃗withv⃗⋅q⃗=0  or  v⃗⋅c⃗∣c⃗∣=k\vec{v} = m\vec{b} + n\vec{c} \quad\text{with}\quad \vec{v}\cdot\vec{q} = 0 \ \text{ or }\ \dfrac{\vec{v}\cdot\vec{c}}{|\vec{c}|} = k
  • m,nm, ntwo scalars from coplanarity
  • second conditionbisector / perpendicular / projection / magnitude — fixes the scalars

Diagram · orthonormal triple î, ĵ, k̂ (drag to rotate)

îĵk̂

î, ĵ, k̂ are mutually perpendicular unit vectors: each pair has dot product 0 (î·ĵ = ĵ·k̂ = k̂·î = 0) and each has length 1. They form the standard basis — any vector is a unique combination x î + y ĵ + z k̂.

Worked example

Find a vector v⃗\vec{v} in the plane of b⃗=i^+j^\vec{b} = \hat{i} + \hat{j} and c⃗=j^+k^\vec{c} = \hat{j} + \hat{k} that is perpendicular to c⃗\vec{c}.
Practise this conceptself-check · 4 quick reps

The same idea in a real exam question:

MHT-CET · 2023 · 9th May Shift 2 · Q111Hard

Example 9 · Vectors · Linear Combinations, Collinearity, and Coplanarity

The vector a⃗=αi^+2j^+βk^\vec{a} = \alpha\hat{i} + 2\hat{j} + \beta\hat{k} lies in the plane of the vectors b⃗=i^+j^\vec{b} = \hat{i}+\hat{j} and c⃗=j^+k^\vec{c} = \hat{j}+\hat{k} and bisects the angle between b⃗\vec{b} and c⃗\vec{c}. Then which one of the following gives possible values of α\alpha and β\beta?

Angle bisector uses UNIT vectors, not the raw vectors

The internal bisector of b⃗\vec{b} and c⃗\vec{c} is along b^+c^=b⃗∣b⃗∣+c⃗∣c⃗∣\hat{b} + \hat{c} = \dfrac{\vec{b}}{|\vec{b}|} + \dfrac{\vec{c}}{|\vec{c}|}. Adding b⃗+c⃗\vec{b} + \vec{c} directly only bisects when ∣b⃗∣=∣c⃗∣|\vec{b}| = |\vec{c}| — otherwise you get the wrong direction.

Coplanar form first, condition second

Always WRITE the coplanar form mb⃗+nc⃗m\vec{b} + n\vec{c} before imposing the perpendicular / projection / magnitude condition. Trying to satisfy the condition without restricting to the plane gives a vector that doesn't actually lie where the question requires.

Concept 10 of 10: Components of a vector against a transformed basis

Three non-coplanar vectors form a basis, so any vector s⃗\vec{s} has UNIQUE components along them. The HARD twist: you're given s⃗\vec{s}'s components along p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r}, and asked for its components along COMBINATIONS of them (like −p⃗+q⃗+r⃗-\vec{p}+\vec{q}+\vec{r}). Write s⃗\vec{s} the new way, substitute, and match coefficients of p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} — a clean linear system.

Definition

If p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} are non-coplanar and s⃗=4p⃗+3q⃗+5r⃗\vec{s} = 4\vec{p} + 3\vec{q} + 5\vec{r} (the given components), then writing s⃗=x(−p⃗+q⃗+r⃗)+y(p⃗−q⃗+r⃗)+z(−p⃗−q⃗+r⃗)\vec{s} = x(-\vec{p}+\vec{q}+\vec{r}) + y(\vec{p}-\vec{q}+\vec{r}) + z(-\vec{p}-\vec{q}+\vec{r}) and collecting like terms gives a system: the coefficient of p⃗\vec{p} must equal 44, of q⃗\vec{q} equal 33, of r⃗\vec{r} equal 55. Solve the 3×33\times 3 system for x,y,zx, y, z; independence of p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} is what makes the coefficient-matching valid.

Match coefficients of a basis

{−x+y−z=4  x−y−z=3  x+y+z=5\begin{cases} -x + y - z = 4 \\ \ \ x - y - z = 3 \\ \ \ x + y + z = 5 \end{cases}
  • rowscoefficients of p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} equated to the given components

Worked example

p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} non-coplanar; s⃗=4p⃗+3q⃗+5r⃗\vec{s} = 4\vec{p} + 3\vec{q} + 5\vec{r}. Find x,y,zx, y, z so s⃗=x(−p⃗+q⃗+r⃗)+y(p⃗−q⃗+r⃗)+z(−p⃗−q⃗+r⃗)\vec{s} = x(-\vec{p}+\vec{q}+\vec{r}) + y(\vec{p}-\vec{q}+\vec{r}) + z(-\vec{p}-\vec{q}+\vec{r}), and evaluate 2x+y+z2x + y + z.
Practise this concept4 quick reps

The same idea in a real exam question:

MHT-CET · 2023 · 9th May Shift 1 · Q130Hard

Example 10 · Vectors · Linear Combinations, Collinearity, and Coplanarity

Suppose that p⃗\vec{p}, q⃗\vec{q} and r⃗\vec{r} are three non-coplanar vectors in R3\mathbb{R}^3. Let the components of a vector s⃗\vec{s} along p⃗\vec{p}, q⃗\vec{q} and r⃗\vec{r} be 4, 3 and 5 respectively. If the components of this vector s⃗\vec{s} along (−p⃗+q⃗+r⃗)(-\vec{p}+\vec{q}+\vec{r}), (p⃗−q⃗+r⃗)(\vec{p}-\vec{q}+\vec{r}) and (−p⃗−q⃗+r⃗)(-\vec{p}-\vec{q}+\vec{r}) are xx, yy and zz respectively, then the value of 2x+y+z2x+y+z is

Equating components needs an INDEPENDENT basis

Matching coefficients of p⃗,q⃗,r⃗\vec{p}, \vec{q}, \vec{r} on both sides is valid ONLY because they are non-coplanar (independent), giving a unique representation. If they were coplanar the components wouldn't be unique and the method collapses.

Answer the asked combination, not the raw x,y,zx, y, z

Many of these end by asking for a combination like 2x+y+z2x + y + z, not the individual scalars. Solve the system fully, then plug into the requested expression — a fraction in yy and zz often cancels cleanly there.

Summary — formulas & gotchas at a glance

A revision cheat-sheet for the formulas and gotchas above. Click any concept name to jump back to its full explanation.

Formulas (10)

  • Linear combination of vectors

    Linear combination

    r⃗=ma⃗+nb⃗+pc⃗ri=mai+nbi+pci\vec{r} = m\vec{a} + n\vec{b} + p\vec{c} \qquad r_i = ma_i + nb_i + pc_i
  • Collinear vectors and collinear points (one scalar)

    Collinearity (one scalar)

    a⃗=kb⃗a1b1=a2b2=a3b3\vec{a} = k\vec{b} \qquad \dfrac{a_1}{b_1} = \dfrac{a_2}{b_2} = \dfrac{a_3}{b_3}
  • Coplanar vectors (two scalars)

    Coplanarity (two scalars)

    a⃗=mb⃗+nc⃗[a⃗ b⃗ c⃗]=∣a1a2a3b1b2b3c1c2c3∣=0\vec{a} = m\vec{b} + n\vec{c} \qquad [\vec{a}\ \vec{b}\ \vec{c}] = \begin{vmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3 \end{vmatrix} = 0
  • Forming a combination, then normalising / measuring it

    Scale a combination to a target magnitude

    M r^=M∣r⃗∣ r⃗r^=r⃗∣r⃗∣M\,\hat{r} = \dfrac{M}{|\vec{r}|}\,\vec{r} \qquad \hat{r} = \dfrac{\vec{r}}{|\vec{r}|}
  • Collinearity of three points (and who lies between)

    Three-point collinearity

    QR⃗=k PQ⃗wherePQ⃗=q⃗−p⃗,  QR⃗=r⃗−q⃗\vec{QR} = k\,\vec{PQ} \quad\text{where}\quad \vec{PQ} = \vec{q} - \vec{p},\ \ \vec{QR} = \vec{r} - \vec{q}
  • Express a vector as a combination of two others

    Two-equation linear system

    {a1=mb1+nc1a2=mb2+nc2 ⇒ m, n\begin{cases} a_1 = mb_1 + nc_1 \\ a_2 = mb_2 + nc_2 \end{cases}\ \Rightarrow\ m,\ n
  • Linear dependence vs independence (the determinant test)

    Dependence ⟺ vanishing determinant

    xa⃗+yb⃗+zc⃗=0⃗ (not all 0)  ⟺  ∣a1a2a3b1b2b3c1c2c3∣=0x\vec{a} + y\vec{b} + z\vec{c} = \vec{0}\ (\text{not all }0) \iff \begin{vmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3 \end{vmatrix} = 0
  • Chained-collinearity systems (\"no two collinear\")

    Chained collinearity

    a⃗+2b⃗=tc⃗,b⃗+3c⃗=λa⃗  ⇒  t=−6, λ=−12\vec{a} + 2\vec{b} = t\vec{c}, \quad \vec{b} + 3\vec{c} = \lambda\vec{a} \;\Rightarrow\; t = -6,\ \lambda = -\tfrac{1}{2}
  • A vector lying in the plane of two others

    Coplanar form + a second condition

    v⃗=mb⃗+nc⃗withv⃗⋅q⃗=0  or  v⃗⋅c⃗∣c⃗∣=k\vec{v} = m\vec{b} + n\vec{c} \quad\text{with}\quad \vec{v}\cdot\vec{q} = 0 \ \text{ or }\ \dfrac{\vec{v}\cdot\vec{c}}{|\vec{c}|} = k
  • Components of a vector against a transformed basis

    Match coefficients of a basis

    {−x+y−z=4  x−y−z=3  x+y+z=5\begin{cases} -x + y - z = 4 \\ \ \ x - y - z = 3 \\ \ \ x + y + z = 5 \end{cases}

Watch out for (16)

Test yourself on Vectors

20 past MHT-CET questions from this chapter, timed at 36 minutes and marked the way the exam marks it. You see your score and every answer the moment you finish. Free to start.

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