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Physics · Ch 3 — Motion in a Plane

Vector Addition – Analytical Method

3.6

Vector Addition – Analytical Method

Analytical Method of Vector Addition

The graphical method of vector addition, while visually clear, becomes impractical when vectors are not perpendicular or when high precision is required. The analytical method resolves this by treating vectors through their components — a systematic algebraic approach that works for any number of vectors in any orientation.

The core idea is simple: break each vector into perpendicular components (typically along the xx and yy axes), add the components separately, then recombine the resultants into a single vector. This transforms a geometric problem into an algebraic one.

Resolving a Vector into Components

Any vector A⃗\vec{A} in a plane can be written as the sum of two perpendicular vectors — its rectangular components. If A⃗\vec{A} makes an angle θ\theta with the xx-axis, then:

Ax=Acos⁡θandAy=Asin⁡θA_x = A \cos\theta \quad \text{and} \quad A_y = A \sin\theta

where A=∣A⃗∣A = |\vec{A}| is the magnitude. The vector itself is:

A⃗=Axi^+Ayj^\vec{A} = A_x \hat{i} + A_y \hat{j}

Here i^\hat{i} and j^\hat{j} are unit vectors along the xx and yy axes respectively. The magnitude is recovered by:

A=Ax2+Ay2A = \sqrt{A_x^2 + A_y^2}

and the direction angle θ\theta (measured from the +x+x axis) satisfies:

tan⁡θ=AyAx\tan\theta = \frac{A_y}{A_x}

Watch out

The quadrant of θ\theta must be determined from the signs of AxA_x and AyA_y, not just from the tangent ratio. For example, if Ax<0A_x < 0 and Ay>0A_y > 0, θ\theta lies in the second quadrant (90∘90^\circ to 180∘180^\circ), but tan⁡θ\tan\theta alone would give a negative angle.

Adding Vectors by Components

Consider two vectors A⃗\vec{A} and B⃗\vec{B}. Their sum R⃗=A⃗+B⃗\vec{R} = \vec{A} + \vec{B} can be found by adding components:

R⃗=(Ax+Bx)i^+(Ay+By)j^\vec{R} = (A_x + B_x)\hat{i} + (A_y + B_y)\hat{j}

So the components of the resultant are simply the sums of the corresponding components:

Rx=Ax+Bx,Ry=Ay+ByR_x = A_x + B_x, \quad R_y = A_y + B_y

The magnitude of the resultant is:

R=Rx2+Ry2R = \sqrt{R_x^2 + R_y^2}

and its direction is given by:

tan⁡θ=RyRx\tan\theta = \frac{R_y}{R_x}

This method extends directly to any number of vectors. For nn vectors A⃗1,A⃗2,…,A⃗n\vec{A}_1, \vec{A}_2, \ldots, \vec{A}_n:

Rx=∑i=1nAix,Ry=∑i=1nAiyR_x = \sum_{i=1}^{n} A_{ix}, \quad R_y = \sum_{i=1}^{n} A_{iy}

Tip

When adding many vectors, make a table listing each vector's xx and yy components, sum the columns, then compute the resultant. This keeps the work organised and minimises sign errors.

Properties of Vector Addition by Components

The component method automatically satisfies all the algebraic properties of vector addition. The textbook highlights three key properties:

Property (I): Commutativity

A⃗+B⃗=B⃗+A⃗\vec{A} + \vec{B} = \vec{B} + \vec{A}

Proof:

Let A⃗=Axi^+Ayj^\vec{A} = A_x\hat{i} + A_y\hat{j} and B⃗=Bxi^+Byj^\vec{B} = B_x\hat{i} + B_y\hat{j}. Then:

A⃗+B⃗=(Ax+Bx)i^+(Ay+By)j^\vec{A} + \vec{B} = (A_x + B_x)\hat{i} + (A_y + B_y)\hat{j}

Since ordinary addition of real numbers is commutative, Ax+Bx=Bx+AxA_x + B_x = B_x + A_x and Ay+By=By+AyA_y + B_y = B_y + A_y. Therefore:

A⃗+B⃗=(Bx+Ax)i^+(By+Ay)j^=B⃗+A⃗\vec{A} + \vec{B} = (B_x + A_x)\hat{i} + (B_y + A_y)\hat{j} = \vec{B} + \vec{A}

Property (II): Associativity

(A⃗+B⃗)+C⃗=A⃗+(B⃗+C⃗)(\vec{A} + \vec{B}) + \vec{C} = \vec{A} + (\vec{B} + \vec{C})

Proof:

Write each vector in component form. For the left side:

(A⃗+B⃗)+C⃗=[(Ax+Bx)i^+(Ay+By)j^]+[Cxi^+Cyj^](\vec{A} + \vec{B}) + \vec{C} = [(A_x + B_x)\hat{i} + (A_y + B_y)\hat{j}] + [C_x\hat{i} + C_y\hat{j}]

=(Ax+Bx+Cx)i^+(Ay+By+Cy)j^= (A_x + B_x + C_x)\hat{i} + (A_y + B_y + C_y)\hat{j}

For the right side:

A⃗+(B⃗+C⃗)=[Axi^+Ayj^]+[(Bx+Cx)i^+(By+Cy)j^]\vec{A} + (\vec{B} + \vec{C}) = [A_x\hat{i} + A_y\hat{j}] + [(B_x + C_x)\hat{i} + (B_y + C_y)\hat{j}]

=(Ax+Bx+Cx)i^+(Ay+By+Cy)j^= (A_x + B_x + C_x)\hat{i} + (A_y + B_y + C_y)\hat{j}

Both sides give the same expression, so associativity holds. This follows from the associativity of ordinary addition of real numbers.

Property (III): Additive Identity

There exists a zero vector 0⃗\vec{0} such that A⃗+0⃗=A⃗\vec{A} + \vec{0} = \vec{A} for every vector A⃗\vec{A}.

Proof:

The zero vector has components 0i^+0j^0\hat{i} + 0\hat{j}. Then:

A⃗+0⃗=(Ax+0)i^+(Ay+0)j^=Axi^+Ayj^=A⃗\vec{A} + \vec{0} = (A_x + 0)\hat{i} + (A_y + 0)\hat{j} = A_x\hat{i} + A_y\hat{j} = \vec{A}

Note

The zero vector is unique — it is the only vector with zero magnitude and no definite direction.

Analytical Derivation of the Law of Cosines

The component method also provides a clean algebraic derivation of the law of cosines for vector addition. Let A⃗\vec{A} and B⃗\vec{B} have magnitudes AA and BB, with an angle ϕ\phi between them. Place A⃗\vec{A} along the xx-axis for convenience: …