An LDV decomposition of a matrix.

In summary, we need to prove that if A is symmetric and invertible, and A=LDV, when L is lower triangular matrix with ones on it's diagonal and V is an upper triangular matrix also with ones on the diagonal and D is a diagonal matrix then V=L^T. To do this, we can use the equations (V^T)^{-1}LD = DL^TV^{-1} and (V^T)^{-1}LDV = DL^TV^{-1} to show that V=L^T. We can also use the fact that L and V are invertible and row equivalent to the identity matrix to prove that V=L^T.
  • #1
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i need to prove that if A is symmetric and invertible (i.e A^-1 exists), and A=LDV, when L is lower triangular matrix with ones on it's diagonal and V is an upper triangualr matrix also with ones on the diagonal and D is a diagonal matrix then V=L^t.

what i did is:
i know that V^t is an LTM and L^t is an UTM, and that D=D^t, but from i haven't seucceded in getting V=L^t.
 
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  • #2
Hint 1: prove that the equation [itex](V^T)^{-1}LD = DL^TV^{-1}[/itex] makes sense, and then that it's true.

Hint 2: If L is lower triangular with 1's on the diagonal, then what of L-1?







Bonus Hint (highlight, only if you're stuck): If X and Y are lower triangular and D is diagonal, what of XYD?
 
  • #3
from LDV=V^tDL^t and V^-1D^-1L^-1=(L^T)^-1D^-1(V^T)^-1
(V^T)^-1=DL^TV^-1D^-1L^-1
(V^T)^-1LD=DL^TV^-1.

for the second hint i think that it's also an LTM.
and because V is a UTM V^t is an LTM so we have in the last equation in the lhs that we have product of two LTM which is also an LTM, time D which makes another LTM, which in return equals a product of a UTM, so we must have that V=L^T, is this correct?
 
  • #4
You need to first show that L and V are invertible, otherwise your equations don't even make sense. Once you've done it, it's somewhat less complicated than what you've done, although what you've done is right:

[tex]LDV=V^tDL^t[/tex]
[tex](V^t)^{-1}LDV = DL^t[/tex]
[tex](V^t)^{-1}LD = DL^tV^{-1}[/tex]

You could even do those last two steps in one. V is a UTM, so VT is an LTM. Can you prove that (VT)-1 is also an LTM? L is an LTM, and so is D, so can you prove that the whole left hand side is an LTM? Likewise, can you prove that the whole right hand side is a UTM? This means that both sides are both LTMs and UTMs, so they're diagonals. But D is a diagonal, so you in fact know that (VT)-1L and LTV-1 are both diagonal. What can you say about the entries on the diagonal? Well you know that L and V have 1's on the diagonal, so you can prove that (VT)-1L and LTV-1 have 1's on the diagonal. In short, these matrices are the same, and they are the identity matrix. But if LTV-1 = I, then of course LT = V, as desired.
 
  • #5
L and V are row equivalent to I, so if they are nxn matrices then they rank is n, and thus they are invertible.
 

What is an LDV decomposition of a matrix?

An LDV decomposition of a matrix is a way to break down a matrix into three simpler matrices: a lower triangular matrix, a diagonal matrix, and an upper triangular matrix. It is also known as an LDU decomposition.

Why is an LDV decomposition useful?

An LDV decomposition can be used to solve systems of linear equations, calculate determinants, and find the inverse of a matrix. It can also help simplify calculations and make them more efficient.

How is an LDV decomposition calculated?

The LDV decomposition is calculated using a process called Gaussian elimination, which involves using row operations to transform the original matrix into the three simpler matrices. This process can be done by hand or with the help of a computer program.

What are the properties of an LDV decomposition?

One of the main properties of an LDV decomposition is that the diagonal matrix has all non-zero elements on its main diagonal. This means that the determinant of the original matrix can be easily calculated by multiplying the elements on the diagonal. Additionally, if the original matrix is invertible, then the diagonal matrix will also be invertible.

Are there any limitations to using an LDV decomposition?

One limitation of an LDV decomposition is that it can only be used for square matrices. Additionally, not all matrices have an LDV decomposition. For example, if the original matrix is singular (has a determinant of 0), then it does not have an LDV decomposition.

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