If a subset T of S is dependent, then S itself dependent

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Discussion Overview

The discussion revolves around the properties of linear dependence in vector spaces, specifically examining the implications of a subset being dependent on the dependence of the larger set. Participants explore the conditions under which a set is considered dependent, particularly focusing on the role of coefficients in linear combinations.

Discussion Character

  • Technical explanation
  • Debate/contested

Main Points Raised

  • Some participants assert that if a subset \( T \) of a vector space \( S \) is dependent, then \( S \) must also be dependent, citing the existence of a linear combination of elements in \( T \) that sums to zero.
  • Others challenge this assertion by emphasizing the necessity that not all coefficients in the linear combination are zero, indicating that this condition is crucial for establishing dependence.
  • Participants discuss the case where one element in \( S \) is a scalar multiple of another, arguing that this also leads to dependence, but note that the scalar must be non-zero to maintain distinctness of the elements involved.
  • There is a clarification that if the scalar is zero, the set may still be considered dependent due to the presence of the zero vector, which is always dependent.
  • One participant highlights the importance of identifying which coefficient in the linear combination is non-zero, pointing out that in a field, a non-zero coefficient cannot equal zero.

Areas of Agreement / Disagreement

Participants generally agree on the definitions of linear dependence and the role of coefficients, but there is disagreement regarding the implications of these definitions for the dependence of the larger set \( S \) based on the dependence of subset \( T \). The discussion remains unresolved as participants refine their understanding of the conditions required for dependence.

Contextual Notes

Participants note limitations in their arguments, particularly the need to specify conditions on coefficients in linear combinations and the implications of including the zero vector in sets.

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1. Let $V$ be a vector space. Let $S \subset V$, and let $T \subset S$. Show that if $T$ is dependent then $S$ itself dependent.

If $T$ is dependent then there's finite set of distinct elements in $T$, say $y_{1}, \ldots, y_{n}$ and a corresponding set of scalars $a_1, \ldots, a_{n}$ such that $\sum_{i=1}^{n} a_iy_i = 0$. Since $T \subset S$, such $y_{1}, \ldots, y_{n}$ and $a_1, \ldots, a_{n}$ also belong to $S$, therefore $S$ itself dependent.

2. If one element in $S$ is scalar multiple of another, then $S$ is dependent.

Let the elements be $x_{1}$ and $x_2$, and the $c_1$ be the scalar. We have $x_{1} = c x_2 \implies x_1-cx_2 = 0$. Thus $S$ is dependent.

Are my attempts okay?
 
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Guest said:
1. Let $V$ be a vector space. Let $S \subset V$, and let $T \subset S$. Show that if $T$ is dependent then $S$ itself dependent.

If $T$ is dependent then there's finite set of distinct elements in $T$, say $y_{1}, \ldots, y_{n}$ and a corresponding set of scalars $a_1, \ldots, a_{n}$ such that $\sum_{i=1}^{n} a_iy_i = 0$. Since $T \subset S$, such $y_{1}, \ldots, y_{n}$ and $a_1, \ldots, a_{n}$ also belong to $S$, therefore $S$ itself dependent.

This is not quite true, we ALSO require that NOT ALL the $a_i$ are 0.

2. If one element in $S$ is scalar multiple of another, then $S$ is dependent.

Let the elements be $x_{1}$ and $x_2$, and the $c_1$ be the scalar. We have $x_{1} = c x_2 \implies x_1-cx_2 = 0$. Thus $S$ is dependent.

Are my attempts okay?

See my comment above. It's *very* close, which coefficient in your linear combination is non-zero?
 
Deveno said:
This is not quite true, we ALSO require that NOT ALL the $a_i$ are 0.
See my comment above. It's *very* close, which coefficient in your linear combination is non-zero?
Oh, I see I missed the same condition both times. We should have $c \ne 0$, since, as you said, all $a_i$ can't be zero; but also I think because if $c= 0$, then $x_1$ and $x_2$ wouldn't be distinct.
 
Guest said:
Oh, I see I missed the same condition both times. We should have $c \ne 0$, since, as you said, all $a_i$ can't be zero; but also I think because if $c= 0$, then $x_1$ and $x_2$ wouldn't be distinct.

Oh, so close...

In your second example, if $c = 0$ we have a linearly dependent set, since $0x_1 = 0$, and the set consisting of the $0$-vector is always a linearly dependent set (in fact, any set CONTAINING the 0-vector is likewise linearly dependent).

But the non-zero coefficient we have in:

$x_1 - cx_2 = 0$

is the coefficient of $x_1$, which is $1$ and in a field, we can NEVER have (by definition) $1 = 0$.
 

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