4.5.16 · D4Linear Algebra (Full)

Exercises — Span — definition

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Everything here uses only the toolkit from the parent: a linear combination is with real scalars , and the span is the set of all such combinations. We test membership by solving .


L1 — Recognition

These check that you can read the definition without computing much.

Problem 1.1. Is the zero vector in ? Give the scalar that proves it.

Recall Solution 1.1

WHAT we do: find a scalar with . The reasoning: and both force . Choosing works. Answer: Yes, with . This is the general fact from the parent: taking every scalar zero always lands on the origin, so is in every span.

Problem 1.2. True or false: is exactly the two-element set .

Recall Solution 1.2

False. The span is the set of all linear combinations with ranging over all of . That is the entire plane — infinitely many points, not just the two generators.

Problem 1.3. Without computing, why must pass through the origin?

Recall Solution 1.3

Set every scalar to : . The all-zero combination is always legal, so is always reachable. Every span contains the origin — no matter how many generators or which ones.


L2 — Application

Now run the membership recipe: set up and solve.

Problem 2.1. Is in ? Is ?

Recall Solution 2.1

WHAT/WHY: membership means "is there a scalar with ?" For : ; check second coord ✔. Yes, . For : ; check ✗. No — no single scalar satisfies both coordinates.

Problem 2.2. Is in ? Is ?

Recall Solution 2.2

A combination is : the third coordinate is forced to . : take ; third coord is ✔. Yes. : needs third coord , but it can only be . No. Geometrically this span is the -plane sitting inside 3D space (see figure below).

Problem 2.3. Is in ? Find the scalars.

Recall Solution 2.3

Set up: gives the system Solve: from the first, . Substitute: . Then . Answer: Yes, with . (Check: ✔, ✔.)


L3 — Analysis

Reason about structure: does adding vectors change anything?

Problem 3.1. , . What does look like, and why is it not all of ?

Recall Solution 3.1

Observe: , so any combination . Renaming (a single free real number), the span is — just the line through the origin in direction . Why not : the two vectors point the same way (dependent), so the second adds no new direction. To fill the plane you need two genuinely different directions. (See figure: both arrows lie on one line.)

Problem 3.2. Does ? Justify using the Determinant.

Recall Solution 3.2

Stack the vectors as columns: . Then . Since , is invertible, so has a (unique) solution for every target . Every vector is reachable yes, they span .

Problem 3.3. You have (the -axis). Which of these, added, enlarges the span: , , ?

Recall Solution 3.3

The current span is the -axis . A new vector enlarges the span only if it is not already in it.

  • : already on the -axis no change.
  • : already on the -axis no change.
  • : not on the -axis (second coord nonzero) it adds the -direction, and the span jumps to all of . Only enlarges the span.

L4 — Synthesis

Combine membership, dependence, and determinant reasoning.

Problem 4.1. For which value(s) of do and fail to span ?

Recall Solution 4.1

They fail exactly when the columns are dependent, i.e. . . Setting gives . At : , so they span only a line — the span fails to be all of . For every other , and they span .

Problem 4.2. Is in ?

Recall Solution 4.2

Set up: . Match to : First two give , but then . The system is inconsistent. Answer: No is not in the span. (The span is the plane ; the point lies off it.)

Problem 4.3. Show that equals (the -plane), so the third vector is redundant.

Recall Solution 4.3

Third vector is already inside the smaller span: . So it adds no new direction, and by the "enlarge only if outside" rule the span is unchanged. Formally, both spans are the -plane :

  • Any combo of the three has third coord (all three vectors have third coord ), so it lands in the -plane.
  • Conversely any is reachable using just the first two. Hence the two sets have exactly the same span — the third generator is redundant. This is the seed of a minimal spanning set, a Basis.

L5 — Mastery

Prove and generalise.

Problem 5.1. Prove that if , then . (Adding a vector already in the span changes nothing.)

Recall Solution 5.1

Let and . We show by proving each contains the other.

: any can be written as , a combination of the bigger list. So . ✔

: take any . Since , write . Substitute: a linear combination of the alone . ✔

Both inclusions hold, so . The redundant vector never enlarges the span — this is exactly why a Basis can be trimmed down to independent vectors.

Problem 5.2. Prove that any single nonzero vector spans a line (a 1-dimensional subspace), never all of .

Recall Solution 5.2

Span is a line: . As sweeps , the point traces the straight line through the origin in direction — a 1-dimensional flat. It is not all of : pick any vector not parallel to (one exists in 2D — e.g. rotate by ). If for some , then is parallel to , contradiction. So , meaning the span misses at least one point of . Hence it is a proper subset — a line, of Dimension .

Problem 5.3. Let be the matrix with columns . Prove: .

Recall Solution 5.3

The span is the Column space of , and " reachable" means is solvable. () If , then is invertible, so solves for every . Every vector is reachable span . () Contrapositive: if , the columns are dependent, so one is a multiple of the other; their span is a line (or a point), which is a proper subset of span . Combining both directions gives the equivalence. This is precisely the determinant test used throughout the parent note.


Figures

Figure — Span — definition

The -plane as a span inside (Problem 2.2).

Figure — Span — definition

Dependent vectors collapse to a line; independent ones fill the plane (Problems 3.1–3.2).


Active recall

What must a candidate scalar-tuple satisfy for membership?
Every coordinate equation of simultaneously, not just one.
Adding a vector already in the span does what to the span?
Nothing — the span is unchanged (it must already contain that vector).
Two vectors in span the whole plane exactly when?
Their determinant (as matrix columns) is nonzero.
Why is proving a two-part job?
You must show and ; one inclusion alone is not equality.

Connections

  • Linear combination — every membership test is a linear-combination equation.
  • Linear independence — decides whether extra vectors enlarge the span (L3–L4).
  • Determinant — the full-span test in square cases (L3.2, L4.1, L5.3).
  • Column space — the span of a matrix's columns (L5.3).
  • Basis — the minimal spanning set trimmed of redundant vectors (L4.3, L5.1).
  • Dimension — the "size" of the span (L5.2).
  • Subspace — every span is one.