Every set of k+1 elements is dependent.

In summary, the concept of linear independence and dependence is useful for proofs, but it is helpful to have a firm understanding of what the definitions are before trying to use them.
  • #1
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Let $S = \left\{x_1, \ldots, x_k\right\}$ be independent set consisting of \(\displaystyle k\) elements in a linear space \(\displaystyle V\) and let $l(S)$ be the subspace spanned by $S$. Then every set of \(\displaystyle k+1\) elements in \(\displaystyle l(S)\) is dependent.

I've gotten nowhere with this - but I'm posting my attempt nonetheless if only for the sake showing effort.

Since \(\displaystyle S\) is independent there exist scalars $d_1, \ldots, d_k$ such that $$\sum_{1 \le i \le k}d_ix_i = 0 \implies d_1 = d_2 = \ldots = d_k ~~~~~~ (1)$$

Let $y_1, \ldots, y_{k+1} \in l(S)$, and let $c_1, \ldots, c_k$ be scalars not all zero. Then we can write:

$$\begin{align} & \begin{aligned} c_1y_1 & = \sum_{1 \le i \le k}a_{1, i} x_i, ~ c_2y_2 & = \sum_{1 \le i \le k}a_{2, i} x_i, ~ \ldots, ~ c_{k+1}y_{k+1} = \sum_{1 \le i \le k}a_{k+1, i}x_i \end{aligned} \end{align}$$

Where $a_{1, i}, \ldots, a_{k+1, i}$ are scalars not all zero. Therefore $$\displaystyle \sum_{1 \le i \le k+1}c_iy_i = \sum_{1 \le r \le k+1}\sum_{1 \le i \le k}a_{r,i}x_i ~~~~~~~~~~~ (2)$$

Now if $a_{r,i}$ are all zero, then $\displaystyle \sum_{1 \le i \le k}c_iy_i = 0$ and we're done.

If not, then it's too hard for me. I thought of letting letting $a_{1,i} = -d_1, ~ a_{2,i} = d_{2}, \ldots, a_{k, i} = (-1)^kd_k$ and finally $a_{k+1, i} = 0$ if $k$ is even, and $d_{k}$ if $k$ is odd. Then from $(1)$ and (2) we have $\displaystyle \sum_{1 \le i \le k+1}c_iy_i = \sum_{1 \le r \le k+1}\sum_{1 \le i \le k} (-1)^i d_i x_i$. But nothing cancels, as $x_1, \ldots, x_k$ are distinct.
 
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  • #2
Re: Every set of k+1 elements is independent.

To use linear independence and linear dependence in proofs, it is crucial to have a firm understanding of what the definitions are, and what they mean.

The definition of linear dependence:

Given a set of vectors $\{v_1,\dots,v_n\}$ there exists a non-zero linear combination that equals the 0-vector.

That is, there exists $c_1,\dots,c_n$ NOT ALL 0, such that:

$c_1v_1 + \cdots + c_nv_n = 0$.

We have to make the caveat "not all 0" for the $c_i$, because if they were, it would trivially be true that:

$0v_1 + \cdots + 0v_n = 0$, and EVERY set of vectors would be linear dependent, rendering it useless as a concept.

What it means:

We don't need ALL the vectors $\{v_1,\dots,v_n\}$ to generate the linear span of linear combinations:

$\{a_1v_1 + \cdots + a_nv_n : a_1,\dots,a_n \in F\}$ (here $F$ is the underlying field of our vector space-it many applications this is $\Bbb R$ or $\Bbb C$ (but it doesn't HAVE to be).

In other words, we have some redundancy in our generating set. To see this, suppose we have a linear combination that sums to the 0-vector:

$c_1v_1 +\cdots + c_nv_n = 0$.

Suppose that we know $c_k \neq 0$ ($k$ might be 1, or $n$, but I will pretend it is between them, you can figure out the cases $k = 1$ and $k = n$ on your own) is the non-zero scalar coefficient.

Then:

$v_k = \dfrac{-c_1}{c_k}v_1 +\cdots \dfrac{-c_{k-1}}{c_k}v_{k-1} + \dfrac{-c_{k+1}}{c_k}v_{k+1} + \cdots + \dfrac{-c_n}{c_k}v_n$

(again, this formula actually presupposes $2 < k < n-1$, and again, you can puzzle out on your own these special cases).

So $v_k$ can be expressed as a linear combination of the remaining vectors, it is unnecessary to generate the span.

Now for linear independence.

The definition:

the set $\{v_1,\dots,v_n\}$ of vectors is linearly independent if the ONLY linear combination of these vectors is the 0-combination, that is:

$c_1v_1 + \cdots + c_nv_n = 0 \implies c_1 =\cdots = c_n = 0$ (these are "two different zeros", the first is the 0-vector, the second is the "field/scalar zero").

What it means: we need ALL of the $v_i$ to generate the linear span.

To see this, suppose that we could write:

$v_k = a_1v_1 + \cdots + a_{k-1}v_{k-1} + a_{k+1}v_{k+1} + \cdots + a_nv_n$ for some $k$.

(again, the same caveat about the "special cases of $k$" apply as above, the modifications needed are minor).

Then $a_1v_1 + \cdots + a_{k-1}v_{k-1} + (-1)v_k + a_{k+1}v_{k+1} + \cdots + a_nv_n = 0$

and since $-1 \neq 0$, this is a non-zero linear combination that sums to the 0-vector. But, from the definition, no such linear combination exists, so we have a contradiction. So our supposition that we could write our $v_k$ as a linear combination of the others must be false.

*********************

Will post more later.
 
  • #3
Re: Every set of k+1 elements is independent.

Thank you, I'll study your post throughly.
 

Related to Every set of k+1 elements is dependent.

What does it mean for a set of k+1 elements to be dependent?

When a set of k+1 elements is dependent, it means that one or more of the elements can be expressed as a linear combination of the other elements in the set. This means that the set is not linearly independent, and one or more of the elements can be removed without changing the span of the set.

Can a set of k+1 elements ever be independent?

No, a set of k+1 elements can never be independent. For a set to be independent, it must have exactly k elements. If there are k+1 elements, at least one of them will be dependent on the others.

What are the implications of a set of k+1 elements being dependent?

The main implication of a set of k+1 elements being dependent is that it cannot form a basis for its vector space. This means that it cannot fully span the space and cannot be used to represent all possible vectors in that space.

How can we determine if a set of k+1 elements is dependent?

To determine if a set of k+1 elements is dependent, we can use the determinant test. If the determinant of the matrix formed by the elements is equal to 0, then the set is dependent. Another method is to try to express one element as a linear combination of the others.

Why is the concept of dependent sets important in mathematics?

The concept of dependent sets is important in mathematics because it allows us to understand and analyze vector spaces. It also helps us to find bases for vector spaces and to solve systems of linear equations. Additionally, it is a fundamental concept in linear algebra and has many applications in other areas of mathematics and science.

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