Linear Dependence and Non-Zero Coefficients

In summary, the statement is false because even though u, v, and w being linearly dependent means that there is at least one non-zero coefficient in the equation c_{1}v_{1}+c_{2}v_{2}+\dots+c_{n}v_{n}, it does not necessarily mean that there is at least one non-zero coefficient in the equation au+bv+cw=0.
  • #1
Euler2718
90
3

Homework Statement



True or False:

If [itex]u[/itex], [itex]v[/itex], and [itex]w[/itex] are linearly dependent, then [itex]au+bv+cw=0[/itex] implies at least one of the coefficients [itex]a[/itex], [itex]b[/itex], [itex]c[/itex] is not zero

Homework Equations



Definition of Linear Dependence:

Vectors are linearly dependent if they are not linearly independent; that is there is an equation of the form [itex]c_{1}v_{1}+c_{2}v_{2}+\dots+c_{n}v_{n}[/itex] with at least one coefficient not zero

The Attempt at a Solution



I said true, but the book says false. It gives the reason, "for any vectors [itex]u[/itex], [itex]v[/itex], [itex]w[/itex] - linearly dependent or not - [itex]0u+0v+0w = 0[/itex]" . But isn't the problem a direct restatement of the definition? Or am I missing something subtle here.
 
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  • #2
Morgan Chafe said:

Homework Statement



True or False:

If [itex]u[/itex], [itex]v[/itex], and [itex]w[/itex] are linearly dependent, then [itex]au+bv+cw=0[/itex] implies at least one of the coefficients [itex]a[/itex], [itex]b[/itex], [itex]c[/itex] is not zero

Homework Equations



Definition of Linear Dependence:

Vectors are linearly dependent if they are not linearly independent; that is there is an equation of the form [itex]c_{1}v_{1}+c_{2}v_{2}+\dots+c_{n}v_{n}[/itex] with at least one coefficient not zero

The Attempt at a Solution



I said true, but the book says false. It gives the reason, "for any vectors [itex]u[/itex], [itex]v[/itex], [itex]w[/itex] - linearly dependent or not - [itex]0u+0v+0w = 0[/itex]" . But isn't the problem a direct restatement of the definition? Or am I missing something subtle here.
You're missing something subtle.
 
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  • #3
If u, v, w linearly independent, au+bv+cw=0 implies a=b=c=0.
Inverting that, if u, v, w linearly dependent, au+bv+cw=0 does not imply a=b=c=0. But they still could be 0.
 
  • #4
Thanks
 

1. What is the definition of linear dependence?

Linear dependence refers to the relationship between two or more vectors where one vector can be expressed as a linear combination of the others. This means that one vector can be written as a sum of scalar multiples of the other vectors.

2. How is linear dependence different from linear independence?

Linear dependence and linear independence are essentially opposite concepts. Linear dependence means that one vector is a combination of others, while linear independence means that no vector can be expressed as a combination of others. In other words, linear independence means that all vectors are unique and necessary, while linear dependence means that some vectors are redundant.

3. What is the importance of understanding linear dependence in mathematics and science?

Linear dependence is a fundamental concept in linear algebra, which has many applications in mathematics and science. It is used to solve systems of equations, understand and analyze data, and model real-world phenomena. Understanding linear dependence allows scientists and mathematicians to better understand relationships and patterns in their data and make more accurate predictions.

4. How can linear dependence be represented visually?

Linear dependence can be represented visually by plotting vectors on a coordinate system. If the vectors are linearly dependent, they will lie on the same line or plane, indicating that one vector is a scalar multiple of the other. If the vectors are linearly independent, they will span a larger area and not lie on the same line or plane.

5. Can linear dependence exist in more than three dimensions?

Yes, linear dependence can exist in any number of dimensions. In fact, the concept of linear dependence is not limited to vectors in three-dimensional space, but can also be applied to matrices and higher-dimensional spaces. The principles and definitions of linear dependence remain the same, regardless of the number of dimensions.

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