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MA103 · Linear Algebra Reference

Linear Algebra Reference Card

Everything in the MA103 mini block on one page — and thirty drills to make it automatic

WPR 1 provided no formula sheet, so assume WPR 2 will not either. This is the list to memorize. Print it, cover the right-hand side, and see how much you can reproduce from the name alone.

The drills at the bottom are deliberately mechanical. Speed on these is what buys you time for the interpretation questions, which is where the points actually go.

Card 1

Vectors

Lessons 23–26 · Reading M.1

Magnitude (length)

|v⃗| = √v12 + v22 + … + vn2

Always ≥ 0. Squaring destroys sign, so negative components help exactly as much as positive ones.

Dot product

u⃗ · v⃗ = u1v1 + u2v2 + … + unvn

The result is a scalar. Both vectors must have the same number of components.

Cosine similarity

cos θ = u⃗ · v⃗|u⃗| |v⃗|

Range −1 to 1. Measures direction only — immune to scaling either vector by a positive number.

Angle

θ = cos−1(u⃗ · v⃗|u⃗| |v⃗|)

Positive dot product → acute. Negative → obtuse. Zero → exactly 90°.

Orthogonal test

u⃗ ⊥ v⃗  ⇔  u⃗ · v⃗ = 0

Perpendicular, 90°, cos θ = 0. One computation settles it.

Parallel test

u⃗ ∥ v⃗  ⇔  v⃗ = ku⃗

One is a scalar multiple of the other. Equivalently cos θ = ±1. Negative k means antiparallel.

Linear combination

t⃗ = au⃗ + bv⃗

A weighted blend, computed component by component. It lands between the two vectors, nearer the heavier weight.

Normalizing features

xnorm = xxmax

Put every feature on a common 0–1 scale before any distance or similarity work, or the largest-range variable dominates.

Card 2

Matrices

Lessons 27–32 · Reading M.2

Dimensions

m × n = rows × columns

Rows first, always. A column vector is n×1.

Transpose

(AT)ij = Aji  ·  m×n → n×m

Swaps rows and columns. On a data matrix it switches "per observation" to "per variable."

Add, subtract, scale

entry by entry

Requires identical dimensions. Scalar multiplication touches every entry.

Multiplication rule

(m×n)(n×k) = m×k

Inner dimensions must match; the result takes the outer ones. Entry (i,j) = row i of the left · column j of the right.

Not commutative

AB ≠ BA

In general. Often one order is not even defined. Never reorder a product.

Determinant (2×2)

det = ad − bc

For [ a b ; c d ]. Nonzero ⇔ invertible.

Inverse (2×2)

A−1 = 1det [ d  −b ; −c  a ]

Swap the diagonal, negate the off-diagonal, divide by the determinant. Check with AA−1 = I.

Solving a system

Ax⃗ = b⃗  →  x⃗ = A−1b⃗

Only when det ≠ 0. Coefficients become A, constants become b⃗.

Elementary row operations

swap · scale · add a multiple

Ri ↔ Rj; Ri ← kRi with k ≠ 0; Ri ← Ri + kRj. These three, nothing else.

Solution types

pivot / contradiction / free

Pivot in every coefficient column → unique. Row [0 … 0 | nonzero] → none. Fully zero row → infinitely many.

Singular matrix

det = 0  →  no unique solution

Rows are scalar multiples. Geometrically: parallel lines — either never meeting, or the same line. Row reduce to find out which.

Scoring with a data matrix

Fw⃗  ·  (n×k)(k×1) = n×1

Simultaneous dot products — one weighted score per observation, all at once. Scaling w⃗ scales every score and never changes the ranking.

Drills · 30 items

Rapid Drills

Cover the answers. Aim for under five seconds each. Use the Drill answers switch in the bar above to check.

A · Is the product defined?

Given A 2×2, B 2×3, C 3×2, D 3×3, u⃗ 2×1, v⃗ 3×1. State defined or undefined, and the dimensions.

1AB
2BA
3BC
4CB
5Au⃗
6Av⃗
7Bv⃗
8u⃗TA
9CTA
10DC

B · Determinant and invertibility

11[ 1  2 ; 3  4 ]
12[ 2  6 ; 1  3 ]
13[ 5  0 ; 0  5 ]
14[ 3  −1 ; −6  2 ]
15[ 0  1 ; 1  0 ]

C · Orthogonal, parallel, or neither?

16⟨1, 0, 0⟩ and ⟨0, 1, 0⟩
17⟨2, 4, 6⟩ and ⟨1, 2, 3⟩
18⟨1, 1, 0⟩ and ⟨1, −1, 0⟩
19⟨3, 0, 4⟩ and ⟨−6, 0, −8⟩
20⟨1, 2, 3⟩ and ⟨4, 5, 6⟩

D · True or false?

21The dot product of two vectors is a vector.
22If u⃗·v⃗ = 0, the vectors are perpendicular.
23A magnitude can be negative.
24AB = BA for all square matrices.
25det = 0 means no inverse exists.
26Cosine similarity can exceed 1.
27Doubling one vector changes the cosine similarity.
28An m×n matrix transposed is n×m.
29det = 0 means the system has no solution.
30Matrix addition requires identical dimensions.