Can a Symmetric Positive Definite Matrix Always Be LU Factorized?

In summary, to show that a symmetric and positive definite matrix A admits a factorization A=LU, you first need to show that no zero pivot is encountered during the elimination process. Then, you can use the Cholesky factorization to show that the remaining (n-1)x(n-1) matrix is also positive definite and symmetric. This can be done by writing it as L'L', where L' is a lower triangular matrix with positive diagonal elements.
  • #1
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Homework Statement


A is an nxn symmetric and positive definate matrix. Show that A admits a factorization A=LU
In other words, no zero pivot is encountered during the elimination process.

Homework Equations


cholesky factorization

The Attempt at a Solution


I think all I have to show is that after 1 step in G.E. a(1,1) does not equal 0 and that the first row is the same as the first row in A and the first column is all zeros except for a(1,1). then show that the remaining (n-1)x(n-1) matrix is symmetric and positive definate. I showed that a(1,1) is not zero but I don't know how to prove the (n-1)x(n-1) matrix is positive definate and symmetric.
any help or suggestions?
 
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Hello, thank you for your post! You are on the right track with showing that a(1,1) is not equal to 0 in the first step of Gaussian elimination. This is because if a(1,1) is equal to 0, then the matrix A is not positive definite.

To show that the remaining (n-1)x(n-1) matrix is symmetric and positive definite, you can use the Cholesky factorization. This factorization states that a symmetric positive definite matrix A can be written as A = LL^T, where L is a lower triangular matrix with positive diagonal elements.

Using this factorization, you can show that the remaining (n-1)x(n-1) matrix is also positive definite and symmetric by showing that it can be written as L'L', where L' is also a lower triangular matrix with positive diagonal elements.

I hope this helps! Let me know if you need any further clarification. Good luck with your proof!
 

FAQ: Can a Symmetric Positive Definite Matrix Always Be LU Factorized?

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A system of linear equations is a set of two or more equations with two or more variables that are connected by the same set of operations. These equations form a system because they must be satisfied simultaneously in order to find a solution.

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There are multiple methods for solving a system of linear equations, such as substitution, elimination, and graphing. These methods involve manipulating the equations to eliminate one variable and solve for the remaining variables.

Can a system of linear equations have more than one solution?

Yes, a system of linear equations can have one, infinitely many, or no solutions. This depends on the number of equations and variables in the system, and whether the equations are consistent (have a solution) or inconsistent (have no solution).

What is the difference between a consistent and inconsistent system of linear equations?

A consistent system of linear equations has at least one solution, while an inconsistent system has no solution. In other words, a consistent system has a point or set of points where all equations in the system are true, while an inconsistent system has no such point or set of points.

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Systems of linear equations are used in various fields such as engineering, economics, and physics to model real-life situations. They can be used to determine the optimal solution for a problem or to analyze the behavior of a system. For example, a system of linear equations can be used to find the cheapest combination of ingredients for a recipe or to predict the trajectory of a projectile.

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