Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

Let \(S\) be a subset of an \(n\)-dimensional vector space \(V\), and suppose \(V\) contains fewer than \(n\) vectors. Explain why \(S\) cannot span \(V\).

Short Answer

Expert verified

Subset \(S\) cannot span \(V\).

Step by step solution

01

State the spanning set theorem

Let \(S = \left\{ {{{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _p}} \right\}\) be a set in \(V\), and \(H = {\mathop{\rm Span}\nolimits} \left\{ {{{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _p}} \right\}\).

  1. If one of the vectors in \(S\), say \({{\mathop{\rm v}\nolimits} _k}\), is alinear combination of the remaining vectors in \(S\), then the set formed from \(S\) by removing \({{\mathop{\rm v}\nolimits} _k}\) still spans \(H\).
  2. If \(H \ne \left\{ 0 \right\}\), some subset of \(S\) is abasisfor \(H\).
02

Explain why \(S\) cannot span \(V\)

Theorem 10 states that if a vector space \(V\) has a basis of \(n\) vectors, then every basis of \(V\) must consist ofexactly \(n\) vectors.

It should be noted that \(n \ge 1\) since \(S\) cannot have fewer than one vector. \(V \ne 0\) because \(n \ge 1\).

Consider \(S\) spans \(V\) and \(S\) has fewer than \(n\) vectors. According to the spanning set theorem, each subset \(S'\) of \(S\) is a basis for \(V\).

If \(S'\) is a subset of \(S\), then it contains fewer than \(n\) vectors because \(S\) also contains fewer than \(n\) vectors. Therefore, there is a basis \(S'\)for \(V\) contains fewer than \(n\) vectors but this is impossible according to theorem 10since \(\dim V = n\).

Thus, subset \(S\) cannot span \(V\).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Question: Exercises 12-17 develop properties of rank that are sometimes needed in applications. Assume the matrix \(A\) is \(m \times n\).

16. If \(A\) is an \(m \times n\) matrix of rank\(r\), then a rank factorization of \(A\) is an equation of the form \(A = CR\), where \(C\) is an \(m \times r\) matrix of rank\(r\) and \(R\) is an \(r \times n\) matrix of rank \(r\). Such a factorization always exists (Exercise 38 in Section 4.6). Given any two \(m \times n\) matrices \(A\) and \(B\), use rank factorizations of \(A\) and \(B\) to prove that rank\(\left( {A + B} \right) \le {\mathop{\rm rank}\nolimits} A + {\mathop{\rm rank}\nolimits} B\).

(Hint: Write \(A + B\) as the product of two partitioned matrices.)

(M) Show thatis a linearly independent set of functions defined on. Use the method of Exercise 37. (This result will be needed in Exercise 34 in Section 4.5.)

Suppose a \({\bf{4}} \times {\bf{7}}\) matrix A has four pivot columns. Is \({\bf{Col}}\,A = {\mathbb{R}^{\bf{4}}}\)? Is \({\bf{Nul}}\,A = {\mathbb{R}^{\bf{3}}}\)? Explain your answers.

Consider the polynomials \({p_{\bf{1}}}\left( t \right) = {\bf{1}} + {t^{\bf{2}}},{p_{\bf{2}}}\left( t \right) = {\bf{1}} - {t^{\bf{2}}}\). Is \(\left\{ {{p_{\bf{1}}},{p_{\bf{2}}}} \right\}\) a linearly independent set in \({{\bf{P}}_{\bf{3}}}\)? Why or why not?

In Exercise 17, Ais an \(m \times n\] matrix. Mark each statement True or False. Justify each answer.

17. a. The row space of A is the same as the column space of \({A^T}\].

b. If B is any echelon form of A, and if B has three nonzero rows, then the first three rows of A form a basis for Row A.

c. The dimensions of the row space and the column space of A are the same, even if Ais not square.

d. The sum of the dimensions of the row space and the null space of A equals the number of rows in A.

e. On a computer, row operations can change the apparent rank of a matrix.

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free