Determine Whether S Is A Basis For P3

Introduction

When you study vector spaces of polynomials, (P_3) – the set of all real‑coefficient polynomials of degree at most three – is a classic example that illustrates the concepts of linear independence, spanning sets, and bases. A typical exercise asks you to determine whether a given set (S) is a basis for (P_3). Answering this question correctly requires checking two fundamental properties:

Linear independence of the vectors (polynomials) in (S).
Spanning of the entire space (P_3) by the vectors in (S).

If both conditions hold, (S) is a basis; otherwise, it is not. This article walks you through the reasoning process step by step, explains the underlying theory, and provides several worked examples that you can adapt to any specific set (S) you encounter.

1. What is (P_3) and Why Does Its Dimension Matter?

(P_3) consists of all polynomials

[ p(x)=a_0 + a_1x + a_2x^2 + a_3x^3,\qquad a_i\in\mathbb{R}. ]

The four coefficients ((a_0,a_1,a_2,a_3)) uniquely determine each polynomial, so the dimension of (P_3) is 4. So naturally, any basis for (P_3) must contain exactly four linearly independent polynomials. The most familiar basis is

[ \mathcal{B}={1,;x,;x^2,;x^3}. ]

If you're are given a set (S) – for example, ({p_1(x),p_2(x),p_3(x),p_4(x)}) – you must verify that it also contains four independent vectors and that every polynomial in (P_3) can be expressed as a linear combination of them.

2. Step‑by‑Step Procedure to Test a Set (S)

2.1 Write the Polynomials in Coordinate Form

Choose the standard basis ({1,x,x^2,x^3}) and express each polynomial of (S) as a coordinate column vector.

Here's a good example: if

[ p_1(x)=2+x,\quad p_2(x)=1-3x^2,\quad p_3(x)=x^3,\quad p_4(x)=4x^2-2x, ]

their coordinates are

[ \begin{aligned} p_1 &\leftrightarrow \begin{bmatrix}2\1\0\0\end{bmatrix},; p_2 \leftrightarrow \begin{bmatrix}1\0\-3\0\end{bmatrix},; p_3 \leftrightarrow \begin{bmatrix}0\0\0\1\end{bmatrix},; p_4 \leftrightarrow \begin{bmatrix}0\-2\4\0\end{bmatrix}. \end{aligned} ]

2.2 Form the Matrix

Place these vectors as columns of a matrix (A). Using the example above:

[ A=\begin{bmatrix} 2 & 1 & 0 & 0\ 1 & 0 & 0 & -2\ 0 & -3 & 0 & 4\ 0 & 0 & 1 & 0 \end{bmatrix}. ]

2.3 Compute the Rank (or Perform Row Reduction)

The rank of (A) tells you how many linearly independent columns it contains.

If rank(=4), the four vectors are independent.
If rank<4, there is a linear dependence relation, and (S) cannot be a basis.

Row‑reduce (A) to its echelon form (or reduced row echelon form, RREF). In the example, the RREF is

[ \begin{bmatrix} 1 & 0 & 0 & 0\ 0 & 1 & 0 & 0\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}, ]

which has rank 4, confirming linear independence.

2.4 Verify the Spanning Property

Because the dimension of (P_3) is 4, any set of four linearly independent vectors automatically spans (P_3). Which means, once you have established independence, the spanning condition is automatically satisfied.

If the set contains more than four vectors, you must still check independence; a set with more than four vectors can be independent only if the extra vectors are linear combinations of the others, which would actually make the set dependent. In that case, you can drop redundant vectors to obtain a basis.

If the set has fewer than four vectors, even if they are independent, they cannot span the whole space, so the answer is “not a basis.”

3. Detailed Example: Determining Whether a Specific Set Is a Basis

3.1 The Set

[ S=\bigl{,1+x,; 1-x,; x^2-1,; x^3+2x,\bigr}. ]

3.2 Convert to Coordinates

[ \begin{aligned} 1+x &\leftrightarrow \begin{bmatrix}1\1\0\0\end{bmatrix},\ 1-x &\leftrightarrow \begin{bmatrix}1\-1\0\0\end{bmatrix},\ x^2-1 &\leftrightarrow \begin{bmatrix}-1\0\1\0\end{bmatrix},\ x^3+2x &\leftrightarrow \begin{bmatrix}0\2\0\1\end{bmatrix}. \end{aligned} ]

3.3 Form the Matrix

[ A=\begin{bmatrix} 1 & 1 & -1 & 0\ 1 & -1 & 0 & 2\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

3.4 Row‑Reduction

Subtract the first column from the second:

[ \begin{bmatrix} 1 & 0 & -1 & 0\ 1 & -2 & 0 & 2\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

Replace the second row with (second row – first row):

[ \begin{bmatrix} 1 & 0 & -1 & 0\ 0 & -2 & 1 & 2\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

Multiply the second row by (-\frac12):

[ \begin{bmatrix} 1 & 0 & -1 & 0\ 0 & 1 & -\tfrac12 & -1\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

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Eliminate the (-1) in the first row using the third row:

[ \begin{bmatrix} 1 & 0 & 0 & 0\ 0 & 1 & -\tfrac12 & -1\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

Finally, clear the (-\tfrac12) in the second row using the third row:

[ \begin{bmatrix} 1 & 0 & 0 & 0\ 0 & 1 & 0 & -1\ 0 & 0 & 1 & 0\ 0 & 0 & 0 & 1 \end{bmatrix}. ]

The RREF is the identity matrix, indicating rank 4.

3.5 Conclusion for This Example

Since the four polynomials are linearly independent and there are exactly four of them, (S) is a basis for (P_3). Any cubic polynomial can be expressed uniquely as a linear combination of the elements of (S).

4. Common Pitfalls and How to Avoid Them

Pitfall	Why It Happens	How to Fix It
Confusing “spanning” with “independence.On the flip side, ”	Students sometimes think that if the vectors are independent they automatically span, forgetting the dimension check.	Always compare the number of vectors to the dimension of the space. If you have fewer vectors than the dimension, spanning fails even with independence.
Leaving a zero column in the matrix.	Forgetting to include the constant term (coefficient of (x^0)) leads to an incomplete coordinate vector.	Write each polynomial fully as (a_0 + a_1x + a_2x^2 + a_3x^3) before extracting coordinates.
Mishandling coefficients of the same degree.	Overlooking that two different polynomials can share the same coefficient pattern, creating hidden dependence. Which means	Perform row reduction carefully; any column that becomes a linear combination of previous columns signals dependence.
Assuming a set with more than four vectors is automatically a basis.Plus,	Extra vectors may be redundant.	Compute the rank; if it is 4, you can drop the dependent vectors and retain a basis.

5. Frequently Asked Questions (FAQ)

Q1: What if the set (S) has five polynomials?
A: In a four‑dimensional space, any set with more than four vectors must be linearly dependent. Compute the rank; if it is 4, you can select any four independent ones to form a basis.

Q2: Can a set with fewer than four polynomials ever be a basis for (P_3)?
A: No. Because the dimension of (P_3) is 4, a basis must contain exactly four linearly independent vectors. Fewer vectors cannot span the whole space.

Q3: Is it necessary to use the standard basis ({1,x,x^2,x^3}) for the coordinate conversion?
A: Not strictly, but using the standard basis simplifies calculations and ensures a consistent coordinate system. Any other basis works as long as you remain consistent throughout the process.

Q4: How do I know if a polynomial like (x^3-2x^2+5) belongs to the span of a given set?
A: Set up the equation

[ c_1p_1(x)+c_2p_2(x)+c_3p_3(x)+c_4p_4(x)=x^3-2x^2+5, ]

translate it into a system of linear equations on the coefficients, and solve for the scalars (c_i). If a solution exists, the polynomial lies in the span.

Q5: Does the field of coefficients matter (real vs. complex)?
A: The procedure is identical; only the underlying field changes. Over (\mathbb{C}) the dimension of (P_3) remains 4, so the same independence/spanning criteria apply.

6. Quick Checklist for Determining a Basis in (P_3)

[ ] Count the vectors in (S).
[ ] Write each polynomial with coefficients of (1, x, x^2, x^3).
[ ] Form the matrix with these coefficient vectors as columns.
[ ] Row‑reduce to RREF or compute the rank.
[ ] Verify rank = 4 (the dimension of (P_3)).
[ ] If rank = 4, declare (S) a basis; otherwise, state why it fails (dependence or insufficient number).

7. Extending the Idea: Bases for (P_n)

The method described scales naturally to any polynomial space (P_n) (degree ≤ (n)). The dimension of (P_n) is (n+1). Therefore:

A set with exactly (n+1) linearly independent polynomials is a basis.
If you have more than (n+1) polynomials, compute the rank; pick any (n+1) independent ones.
With fewer than (n+1) polynomials, you cannot span the whole space.

The same matrix‑rank technique works, only the matrix size grows to ((n+1)\times m) where (m) is the number of polynomials in the set.

8. Conclusion

Determining whether a set (S) is a basis for (P_3) hinges on two clear, testable conditions: linear independence and spanning. By translating each polynomial into its coordinate vector relative to the standard basis, assembling those vectors into a matrix, and examining the matrix’s rank, you obtain a decisive answer. Because the dimension of (P_3) is four, any four independent polynomials automatically form a basis, while any set with fewer than four cannot span, and any set with more than four must contain dependencies.

Mastering this systematic approach not only equips you to solve textbook problems but also builds intuition for higher‑dimensional vector spaces, linear transformations, and the elegant structure underlying polynomial algebra. The next time you encounter a question like “Is (S) a basis for (P_3)?”, you now have a reliable, step‑by‑step toolkit to answer confidently and efficiently.