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Suppose is a linearly dependent spanning set for a \(\left\{ {{{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _4}} \right\}\)vector space V. Show that each w in \(V\) can be expressed in more than one way as a linear combination of \({{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _4}\).(Hint: Let \({\mathop{\rm w}\nolimits} = {k_1}{{\mathop{\rm v}\nolimits} _1} + ... + {k_4}{{\mathop{\rm v}\nolimits} _4}\) be an arbitrary vector in \(V\). Use the linear dependence of \(\left\{ {{{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _4}} \right\}\) to produce another representation of w as a linear combination of \({{\mathop{\rm v}\nolimits} _1},...,{{\mathop{\rm v}\nolimits} _4}\).)

Short Answer

Expert verified

It is proved that w in \(V\) can be expressed in more than one way as a linear combination of \({{\mathop{\rm v}\nolimits} _1},{{\mathop{\rm v}\nolimits} _2},{{\mathop{\rm v}\nolimits} _3},\) and \({{\mathop{\rm v}\nolimits} _4}\).

Step by step solution

01

Show that each w in \(V\) can be expressed in more than one way as a linear combination

There are scalars \({k_1},{k_2},{k_3},\) and \({k_4}\) for \(w\)in \(V\) such that

\({\mathop{\rm w}\nolimits} = {k_1}{{\mathop{\rm v}\nolimits} _1} + {k_2}{{\mathop{\rm v}\nolimits} _2} + {k_3}{{\mathop{\rm v}\nolimits} _3} + {k_4}{{\mathop{\rm v}\nolimits} _4}\). …(1)

Since \(\left\{ {{{\mathop{\rm v}\nolimits} _1},{{\mathop{\rm v}\nolimits} _2},{{\mathop{\rm v}\nolimits} _3},{{\mathop{\rm v}\nolimits} _4}} \right\}\) spans \(V\) and the set is linearly dependent, scalars \({c_1},{c_2},{c_3},\) and \({c_4}\) are not all zero such that

\(0 = {c_1}{{\mathop{\rm v}\nolimits} _1} + {c_2}{{\mathop{\rm v}\nolimits} _2} + {c_3}{{\mathop{\rm v}\nolimits} _3} + {c_4}{{\mathop{\rm v}\nolimits} _4}\). …(2)

Add equations (1) and (2).

\(\begin{array}{c}{\mathop{\rm w}\nolimits} = {\mathop{\rm w}\nolimits} + 0\\ = \left( {{k_1} + {c_1}} \right){{\mathop{\rm v}\nolimits} _1} + \left( {{k_2} + {c_2}} \right){{\mathop{\rm v}\nolimits} _2} + \left( {{k_3} + {c_3}} \right){{\mathop{\rm v}\nolimits} _3} + \left( {{k_4} + {c_4}} \right){{\mathop{\rm v}\nolimits} _4}\end{array}\) …(3)

At least one of the weights in equation (3) is different from the corresponding weight in equation (1) since at least one of the \({c_i}\) is non-zero.

Therefore, \({\mathop{\rm w}\nolimits} \) can be expressed in more than one way as a linear combination of \({{\mathop{\rm v}\nolimits} _1},{{\mathop{\rm v}\nolimits} _2},{{\mathop{\rm v}\nolimits} _3},\) and \({{\mathop{\rm v}\nolimits} _4}\).

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Most popular questions from this chapter

Let \(B = \left\{ {\left( {\begin{array}{*{20}{c}}{\bf{1}}\\{ - {\bf{4}}}\end{array}} \right),\,\left( {\begin{array}{*{20}{c}}{ - {\bf{2}}}\\{\bf{9}}\end{array}} \right)\,} \right\}\). Since the coordinate mapping determined by B is a linear transformation from \({\mathbb{R}^{\bf{2}}}\) into \({\mathbb{R}^{\bf{2}}}\), this mapping must be implemented by some \({\bf{2}} \times {\bf{2}}\) matrix A. Find it. (Hint: Multiplication by A should transform a vector x into its coordinate vector \({\left( {\bf{x}} \right)_B}\)).

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

  1. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} A\). (Hint: Explain why every vector in the column space of \(AB\) is in the column space of \(A\).
  2. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} B\). (Hint: Use part (a) to study rank\({\left( {AB} \right)^T}\).)

Define by \(T\left( {\mathop{\rm p}\nolimits} \right) = \left( {\begin{array}{*{20}{c}}{{\mathop{\rm p}\nolimits} \left( 0 \right)}\\{{\mathop{\rm p}\nolimits} \left( 1 \right)}\end{array}} \right)\). For instance, if \({\mathop{\rm p}\nolimits} \left( t \right) = 3 + 5t + 7{t^2}\), then \(T\left( {\mathop{\rm p}\nolimits} \right) = \left( {\begin{array}{*{20}{c}}3\\{15}\end{array}} \right)\).

  1. Show that \(T\) is a linear transformation. (Hint: For arbitrary polynomials p, q in \({{\mathop{\rm P}\nolimits} _2}\), compute \(T\left( {{\mathop{\rm p}\nolimits} + {\mathop{\rm q}\nolimits} } \right)\) and \(T\left( {c{\mathop{\rm p}\nolimits} } \right)\).)
  2. Find a polynomial p in \({{\mathop{\rm P}\nolimits} _2}\) that spans the kernel of \(T\), and describe the range of \(T\).

If a\({\bf{6}} \times {\bf{3}}\)matrix A has a rank 3, find dim Nul A, dim Row A, and rank\({A^T}\).

Define a linear transformation by \(T\left( {\mathop{\rm p}\nolimits} \right) = \left( {\begin{array}{*{20}{c}}{{\mathop{\rm p}\nolimits} \left( 0 \right)}\\{{\mathop{\rm p}\nolimits} \left( 0 \right)}\end{array}} \right)\). Find \(T:{{\mathop{\rm P}\nolimits} _2} \to {\mathbb{R}^2}\)polynomials \({{\mathop{\rm p}\nolimits} _1}\) and \({{\mathop{\rm p}\nolimits} _2}\) in \({{\mathop{\rm P}\nolimits} _2}\) that span the kernel of T, and describe the range of T.

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