Take the 2-minute tour ×
Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. It's 100% free, no registration required.

Given an $n \times n$ matrix $A$, and an $n \times 1$ vector $b$, the conventional way of computing an $n \times 1$ vector $x$ such that $x=Ax+b$ is to use the following iterations: $$x_{k+1}=Ax_{k}+b.$$ Then, $x$ can be approximated by $x_k$ when $k$ is sufficiently large.

However, such an iteration would compute all entries of $x$. Now, I only need to compute the $p$ largest entries of $x$ (instead of solving all the entries of $x$), is there a faster way of finding the $p$ largest entries of $x_k$ to approximate those of $x$? Could everyone give some reference to this problem? Thank you!

share|improve this question
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.