HJLS algorithm ( Integer Relation): Difference between revisions

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(Created page with "== Time Complexity == $O(n^{3}(n+k)$) == Space Complexity == $O(n^{2})$ -- but requires infinite precision with large n or else it becomes unstable (Derived: Store Gram-Schmidt basis vectors b_i (n n-dimensional vectors) and Gram-Schmidt numbers \mu_{i,j} (i and j both from 1 to n), not sure how to take into account the "bit complexity" part) == Description == == Approximate? == Exact == Randomized? == No, deterministic == Model of Computation == bit...")
 
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== Time Complexity ==  
== Time Complexity ==  


$O(n^{3}(n+k)$)
$O(n^{3}(n+k))$


== Space Complexity ==  
== Space Complexity ==  


$O(n^{2})$ -- but requires infinite precision with large n or else it becomes unstable  
$O(n^{2})$ -- but requires infinite precision with large n or else it becomes unstable bits


(Derived: Store Gram-Schmidt basis vectors b_i (n n-dimensional vectors) and Gram-Schmidt numbers \mu_{i,j} (i and j both from 1 to n), not sure how to take into account the "bit complexity" part)
(Derived: Store Gram-Schmidt basis vectors b_i (n n-dimensional vectors) and Gram-Schmidt numbers \mu_{i,j} (i and j both from 1 to n), not sure how to take into account the "bit complexity" part)

Latest revision as of 10:24, 28 April 2023

Time Complexity

$O(n^{3}(n+k))$

Space Complexity

$O(n^{2})$ -- but requires infinite precision with large n or else it becomes unstable bits

(Derived: Store Gram-Schmidt basis vectors b_i (n n-dimensional vectors) and Gram-Schmidt numbers \mu_{i,j} (i and j both from 1 to n), not sure how to take into account the "bit complexity" part)

Description

Approximate?

Exact

Randomized?

No, deterministic

Model of Computation

bit complexity

Year

1986

Reference

https://epubs.siam.org/doi/pdf/10.1137/0218059