Chapter 4: Q4.5-25Q (page 191)
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
Subset \(S\) cannot span \(V\).
Chapter 4: Q4.5-25Q (page 191)
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 \(V\).
Subset \(S\) cannot span \(V\).
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Get started for freeQuestion: 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.
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