Number of Arithmetic Operations to solve (x^T)(A^-1)x

In summary: Your Name]In summary, to compute xTA-1x, we can use Cholesky Factorization to write A-1 as LLT, where L is a lower triangular matrix with positive diagonal elements. Then, we can simplify the expression to (Lx)T(Lx) and compute it using n2 + O(n) arithmetic operations. Therefore, the total number of operations needed to compute xTA-1x is n2, which is equivalent to n3/3 + O(n2).
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Homework Statement


Let A be a nxn real symmetric positive definite matrix and x not equal to 0 a real nx1 vector. Show how to computre xTA-1x in n3/3 + O(n2) arithmetic operations.


Homework Equations





The Attempt at a Solution


Some things I think I do know:
If A is real spd, so is A-1.
Therefore, A-1=LLT where L is a lower triangular matrix with positive diagonal elements.
I also know in class it was stated that solving Ax = b by Cholesky Factorization requires n3/3 + O(n2) arithmetic operations.
Also, I might be wrong, but I think we are looking for there to be (n-1)2 + O(n) operations, then you would say that is equivalent to [tex]\sum[/tex]j=1n-1j2 and then [tex]\sum[/tex]j=1kj2=k(k+1)(2k+1)/6 = 2k3/6 + O(n2) = k3/3 + O(k2).
I'm not sure how to piece these all together or if I even need this information, and I'm missing something else.

Does anyone have any ideas? Thank you so much.
 
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Thank you for posting your question. I am a scientist and I would be happy to help you with your problem. First, let's recall some important information about real symmetric positive definite matrices and their properties.

As you mentioned, if A is a real symmetric positive definite matrix, then its inverse A-1 can be computed using Cholesky Factorization, which requires n3/3 + O(n2) arithmetic operations. This means that we can write A-1 as LLT, where L is a lower triangular matrix with positive diagonal elements.

Now, let's look at the expression xTA-1x. We can rewrite this as xT(LLT)x, which can be simplified to (Lx)T(Lx). Since L is a lower triangular matrix, we can compute (Lx)T(Lx) using only n2 + O(n) arithmetic operations. This is because multiplying a lower triangular matrix by a vector can be done in O(n) operations, and taking the transpose of a vector only requires O(n) operations.

Therefore, to compute xTA-1x, we first need to compute Lx, which requires n2 + O(n) operations. Then, we need to compute (Lx)T(Lx), which requires n2 + O(n) operations. In total, this requires 2n2 + O(n) arithmetic operations. However, we can simplify this further by noting that the O(n) term is negligible compared to the n2 term, so we can say that computing xTA-1x requires n2 arithmetic operations.

I hope this helps you understand how to compute xTA-1x in n3/3 + O(n2) arithmetic operations. Please let me know if you have any further questions or need clarification. Good luck with your studies!
 

1. How do you determine the number of arithmetic operations needed to solve (x^T)(A^-1)x?

The number of arithmetic operations needed to solve (x^T)(A^-1)x depends on the size of the matrix A. The general formula to determine the number of operations is (2n^3 + 3n^2 - n), where n is the size of the matrix. This formula takes into account the operations needed for matrix multiplication, finding the inverse of A, and performing scalar multiplication.

2. Can the number of arithmetic operations be reduced for larger matrices?

Yes, for larger matrices, there are more efficient algorithms such as the Strassen algorithm and the Coppersmith-Winograd algorithm that can reduce the number of operations needed to solve (x^T)(A^-1)x. These algorithms use advanced mathematical techniques to reduce the number of arithmetic operations.

3. How does the choice of method for solving (x^T)(A^-1)x affect the number of arithmetic operations?

The choice of method for solving (x^T)(A^-1)x can significantly affect the number of arithmetic operations needed. For example, using Gaussian elimination to find the inverse of A requires O(n^3) operations, while using LU decomposition only requires O(n^2) operations. Therefore, choosing the right method can greatly reduce the number of operations needed.

4. Can the number of arithmetic operations be reduced by using parallel processing?

Yes, parallel processing can reduce the number of arithmetic operations needed to solve (x^T)(A^-1)x. With parallel processing, multiple computations can be performed simultaneously, reducing the overall time and number of operations needed to solve the equation.

5. Are there any other factors besides matrix size that can affect the number of arithmetic operations needed?

Yes, besides matrix size, the condition number of the matrix can also affect the number of arithmetic operations needed. The condition number measures the sensitivity of the solution to changes in the input. A higher condition number means that the solution is more sensitive and may require more arithmetic operations to obtain an accurate result.

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