Linear dependency of Vectors above R and C and the det

In summary: Thus the two vectors (3,2) and (-3,2) are independent if considered over R.In summary, the two vectors v1 = (3i, 2), v2 = (-3, 2i) are dependent above C^2, but independent above R. The determinant method can still be used over R, but the vectors must be expressed in terms of a real basis, which takes 4 basis vectors. The vectors can be written as a 4 vector basis in R and their independence can be seen in the matrix representation. In contrast, complex multiplication allows for vector rotation, making the vectors dependent above C^2.
  • #1
Dank2
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consider the two vectors v1 = (3i, 2), v2 = (-3, 2i). in C^2

Above C we get, v1 * i = v2, therefore they are dependent.

Now above R, we can't see that they are dependent.

Why if i take the determinant of those vectors i get get 0 |v1 v2| = 2x2 matrix = 0 ( which means two column vectors are independent). Does the determinant works only above C in this case because above R they are independent and yet we get same result of the determinant?
 
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  • #2
those vectors have 2 entries over C but 4 entries over R. Then the (real) matrix of the two vectors is 2 x 4, and it will not have all 2x2 sub determinants equal to zero. so the determinant method still works over R, but you have to express the vectors in terms of a real basis, which takes 4 basis vectors, hence 4 real entries for your vectors.
 
  • #3
Your vectors are part of ##ℂ^2## and linearly dependant over ##ℂ## as you correctly said.

What you are next doing is to confuse different concepts.
If you regard the vectors over ##ℝ##, then ##i## is no longer a scalar and ##i^2 = -1## cannot be calculated. Your determinant is therefore ##6i^2 + 6## which is different from ##0 \in ℝ##. ##i## plays the same role as a variable would do,
i.e. ## v_1, v_2 ∈ ℝ^2[x] ≅ ℝ^2[\text{i}] ##.
 
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  • #4
mathwonk said:
those vectors have 2 entries over C but 4 entries over R. Then the (real) matrix of the two vectors is 2 x 4, and it will not have all 2x2 sub determinants equal to zero. so the determinant method still works over R, but you have to express the vectors in terms of a real basis, which takes 4 basis vectors, hence 4 real entries for your vectors.
but 2x4 matrix over R means the columns are are linearly dependent. how do i write the vectors are above as 4 vector basis in R? and how do i see they are linearly independent above R in that matrix?
 
  • #5
maybe i should have said 4x2. then the columns are vectors of length 4. a real basis of C^2 is e.g., (1,0), (i,0), (0,1), (0,i). in that basis (3i,2) has real coordinate (row, since i can't write columns here) vector (0, 3, 2, 0), and (-3,2i) has coordinate vector (-3, 0, 0, 2). then the first 2x2 determinant equals 9 or -9 depending on what order you write the vectors. since there is a non zero 2x2 determinant the vectors are independent over R.
 
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  • #6
Complex multiplication allows one vector to be rotated to another vector. So they will be linearly dependent. Multiplication of vectors in R2 by real numbers can not rotate a vector. So two vectors in R2 can be linearly dependent only if they are parallel.
 

Related to Linear dependency of Vectors above R and C and the det

1. What is linear dependency of vectors?

Linear dependency of vectors is a concept in linear algebra where a set of vectors can be expressed as a linear combination of other vectors. This means that one or more vectors in the set can be written as a combination of the other vectors using scalar coefficients.

2. How is linear dependency determined in vector spaces over R and C?

In vector spaces over R and C, linear dependency is determined by checking if any vector in the set can be written as a linear combination of the other vectors. This can be done by setting up a system of equations and solving for the coefficients using methods such as Gaussian elimination or matrix inversion.

3. What is the significance of the determinant in linear dependency?

The determinant is a measure of the linear dependency of a set of vectors. If the determinant is equal to 0, then the vectors are linearly dependent. If the determinant is not equal to 0, then the vectors are linearly independent.

4. Can linearly dependent vectors still span a vector space?

No, linearly dependent vectors cannot span a vector space. This is because linearly dependent vectors do not add any new information and therefore do not increase the dimensionality of the vector space.

5. How can linear dependency be used in practical applications?

Linear dependency is a fundamental concept in linear algebra and has many practical applications in fields such as physics, engineering, and computer science. It is used to solve systems of linear equations, determine the rank and dimension of a matrix, and understand the relationship between different vectors in a vector space.

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