Linear independence and vector parametrics

In summary, linear independence in a vector space means that the vectors in a set are not dependent on each other and cannot be written as a linear combination of each other. It is determined by checking if the only solution to the linear combination of the vectors is the trivial solution. In vector parametrics, linear independence is important in creating a basis for the vector space. A set of linearly independent vectors can be scaled or multiplied by a constant without affecting their linear independence. This is different from orthogonality, which refers to the angles formed by vectors. While a set of linearly independent vectors can be orthogonal, a set of orthogonal vectors may not necessarily be linearly independent.
  • #1
hadroneater
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Homework Statement


Consider the two lines, given in the paramentric form

L1: x = (0, 1 ,2) + s(1, 0, 2)
L2: x = (4, 2, c) + t(-2, 0, d)

where c and d are constants.

a) For what value of d are the lines parallel?
b) With the value of d above, for what value(s) of c (if any) are the two lines identical? Justify briefly.
c) For the case c = 5 and d = 0 find the point P on L1 and Q on L2 such that the distance between P and Q is as small as possible.

2. A concept question:
Can any vector x with two components be expressed as a linear combination of a and b? Why?

Homework Equations



?

The Attempt at a Solution


a) I think d = -4, but not sure if that's right.
b) I'm not sure how to approach this problem. Do I make 2 + 2s = c - 4t
But what do I do after that?
c) No clue. I'm thinking perhaps using the dot product = 0?

2. I think a vector can only be expressed as a linear combination of a and b if the two vectors are linearly independent. How do I prove that?
 
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  • #2


Thank you for posting your questions. I am a scientist and I would be happy to help you with your concerns.

For part a), your answer of d = -4 is correct. To find this value, you can equate the z-coefficients of the two lines, which gives you the equation 2 = c - 4t. Then, since the lines are parallel, their direction vectors (1, 0, 2) and (-2, 0, d) must be parallel as well. This means their cross product must be equal to 0, which leads to the equation 2d = 0. Solving for d gives you d = -4.

For part b), you can substitute the value of d = -4 into the equation you obtained in part a). This gives you c = 2 + 2s. Since s is a parameter, the lines will be identical for any value of c that satisfies this equation. Therefore, the two lines will be identical for all values of c = 2 + 2s, where s is a real number.

For part c), you are correct in thinking that the dot product can help you find the point P and Q with the smallest distance. The dot product of two vectors is related to the angle between them, and when the angle is 0, the distance between the two points is minimized. You can use this fact to set up an equation for the dot product of the direction vectors of the two lines, and then solve for the values of s and t that minimize the distance between the two points. Once you have the values of s and t, you can substitute them into the equations of the two lines to find the points P and Q.

For the concept question, you are correct in thinking that a vector can only be expressed as a linear combination of two vectors if they are linearly independent. This means that one vector cannot be a multiple of the other, and they must have different directions. This can be proven by setting up a system of equations and solving for the coefficients of the linear combination, showing that there is no unique solution when the vectors are linearly dependent.

I hope this helps clarify your understanding of these concepts. Please let me know if you have any further questions.

 

1. What is linear independence?

Linear independence refers to a set of vectors in a vector space that are not dependent on each other. In other words, no vector in the set can be written as a linear combination of the other vectors in the set.

2. How is linear independence determined?

Linear independence is determined by checking if the only solution to the linear combination of the vectors is the trivial solution (all coefficients equal to 0). If there exists a non-trivial solution, then the vectors are linearly dependent.

3. What is the significance of linear independence in vector parametrics?

In vector parametrics, linear independence is important because it allows us to create a basis for the vector space. A basis is a set of linearly independent vectors that can be used to represent any vector in the space.

4. Can a set of linearly independent vectors be scaled or multiplied by a constant?

Yes, a set of linearly independent vectors can be scaled or multiplied by a constant without affecting their linear independence. This is because scaling a vector does not change its direction or its linear relationship with other vectors.

5. What is the difference between linear independence and orthogonality?

Linear independence refers to the relationship between vectors in a vector space, while orthogonality refers to the relationship between the angles formed by vectors. A set of linearly independent vectors can be orthogonal, but a set of orthogonal vectors may not necessarily be linearly independent.

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