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Linear Independence

    


Definition. A set \(S=\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}\) of vectors of a vector space \(V\) is linearly independent if the only linear combination of vectors in \(S\) that produces \(\overrightarrow{0}\) is a trivial linear combination., i.e., \[c_1\overrightarrow{v_1}+c_2\overrightarrow{v_2}+\cdots+c_k\overrightarrow{v_k}=\overrightarrow{0} \implies c_1=c_2=\cdots=c_k=0.\] \(S=\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}\) is linearly dependent if \(S\) is not linearly independent, i.e., there are scalars \(c_1,c_2,\ldots,c_k\), not all zero, such that \[c_1\overrightarrow{v_1}+c_2\overrightarrow{v_2}+\cdots+c_k\overrightarrow{v_k}=\overrightarrow{0}.\]

Example.

  1. \(\{\overrightarrow{v}\}\) is linearly independent in \(V\) if and only if \(\overrightarrow{v}\neq \overrightarrow{0_V}\).

  2. \(\{\overrightarrow{e_1},\overrightarrow{e_2},\ldots,\overrightarrow{e_n}\}\) is a linearly independent set of vectors in \(\mathbb R^n\).

  3. \(\{\overrightarrow{1},\overrightarrow{t},\overrightarrow{t^2},\ldots,\overrightarrow{t^n}\}\) is a linearly independent set of vectors in \(P_n\).

  4. \(\{\overrightarrow{e_1},\overrightarrow{e_2},\ldots,\overrightarrow{e_n},\ldots\}\) is a linearly independent set of vectors in \(\mathbb{R}^{\infty}\) where \(\overrightarrow{e_i}\) is the infinite sequence with \(1\) in the \(i\)th place and \(0\) elsewhere.

  5. \(B=\{\overrightarrow{E_{i,j}}: 1 \leq i \leq m,1 \leq j \leq n\}\) is a linearly independent set of vectors in \(M_{m, n}(\mathbb R)\) where \(\overrightarrow{E_{i,j}}\) is the \(m\times n\) matrix with \((i,j)\)-entry 1 and \(0\) elsewhere.

  6. Consider the following three polynomials in \(P_2\): \(\overrightarrow{p_1}(t)=t+2t^2\), \(\overrightarrow{p_2}(t)=2+2t^2\), and \(\overrightarrow{p_3}(t)=1-t-t^2\). Show that \(\{\overrightarrow{p_1},\;\overrightarrow{p_2},\;\overrightarrow{p_3}\}\) is a linearly dependent set in \(P_2\).

    Solution. Suppose \(c_1\overrightarrow{p_1}+c_2\overrightarrow{p_2}+c_3\overrightarrow{p_3}=\overrightarrow{0}\) for some scalars \(c_1,c_2,c_3\). Then for all \(t\), \[\begin{align*} (c_1\overrightarrow{p_1}+c_2\overrightarrow{p_2}+c_3\overrightarrow{p_3})(t)&=0\\ c_1\overrightarrow{p_1}(t)+c_2\overrightarrow{p_2}(t)+c_3\overrightarrow{p_3}(t)&=0\\ c_1(t+2t^2)+c_2(2+2t^2)+c_3(1-t-t^2)&=0\\ (2c_2+c_3)+(c_1-c_3)t+(2c_1+2c_2-c_3)t^2&=0. \end{align*}\] Thus \(2c_2+c_3=0,\;c_1-c_3=0,\;2c_1+2c_2-c_3=0\). One solution is \((c_1,c_2,c_3)=(2,-1,2)\). So \(2\overrightarrow{p_1}-\overrightarrow{p_2}+2\overrightarrow{p_3}=\overrightarrow{0}\) and \(\{\overrightarrow{p_1},\;\overrightarrow{p_2},\;\overrightarrow{p_3}\}\) is a linearly dependent set in \(P_2\).

Theorem. A set \(S=\{\overrightarrow{v_1},\overrightarrow{v_2},\ldots,\overrightarrow{v_k}\}\) of \(k\geq 2\) vectors in a vector space \(V\) is linearly dependent if and only if there exists a vector in \(S\) that is a linear combination of the other vectors in \(S\).

Similar to the proof of the last theorem in Linear Independence in \(\mathbb R^n\).


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