Editing Open Problems:68

Jump to: navigation, search

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.
Latest revision Your text
Line 1: Line 1:
 
{{Header
 
{{Header
|source=bertinoro14
+
|title=Approximating Rank in the Bounded-Degree Model
 +
|source=online
 
|who=Yuichi Yoshida
 
|who=Yuichi Yoshida
 
}}
 
}}
 
Let $A:\mathbb{F}_p^{m \times n}$ be a matrix such that each row and column has a constant number of non-zero entries (hence, $m = O(n)$).
 
Let $A:\mathbb{F}_p^{m \times n}$ be a matrix such that each row and column has a constant number of non-zero entries (hence, $m = O(n)$).
The matrix $A$ can be accessed via the following types of queries.
+
The matrix $A$ is given as a query access:
 
If we specify the $i$-th row, then we obtain the indices $j$ for which $A_{i,j} \neq 0$.
 
If we specify the $i$-th row, then we obtain the indices $j$ for which $A_{i,j} \neq 0$.
 
Similarly, if we specify the $j$-th column, then we obtain the indices $i$ for which $A_{i,j}\neq 0$.
 
Similarly, if we specify the $j$-th column, then we obtain the indices $i$ for which $A_{i,j}\neq 0$.
For a parameter $\epsilon > 0$, we want to approximate the rank of $A$ to within $\pm \epsilon n$.
+
For a parameter $\epsilon > 0$, we want to approximate the rank of $A$ to whithin $\pm \epsilon$.
How many queries are needed to accomplish this task?
+
How many queries are needed for this task?
  
When $p = 2$ and each row has exactly two ones, $A$ can be seen as the incidence matrix of a graph,
+
When $p = 2$ and each column has exactly two ones, $A$ can be seen as an incidence matrix,
 
and its rank is equal to $n - c$, where $c$ is the number of connected components.
 
and its rank is equal to $n - c$, where $c$ is the number of connected components.
In this case, the rank can be approximated efficiently, with $\tilde O(1/\epsilon^2)$ queries, because we know how to efficiently approximate $c$ {{cite|ChazelleRT-05}}.
+
So, we can approximate the rank with $O(1/\epsilon^2)$ queries.
  
In general, we conjecture that $\Omega(n)$ queries are necessary.
+
In general, the conjecture is $\Omega(n)$.
The difficulty in showing this lower bound arises from the fact that few techniques for proving $\Omega(n)$ lower bounds for the bounded-degree model are known.
+
The difficulty is that not so many techniques are known to show $\Omega(n)$ lower bound for the bounded-degree model.
Bogdanov, Obata, and Trevisan {{cite|BogdanovOT-02}} show a lower bound of $\Omega(n)$ for the problem of testing the satisfiability of '''E3LIN-2''' instances in the bounded-degree model.
+
{{cite|BogdanovOT-02}} shows a lower bound of $\Omega(n)$ for the problem of testing the satisfiability of E3LIN2 instances in the bounded-degree model.
 
However, the lower bound is obtained by considering a distribution of instances of the form $Ax = b$, where $A$ is fixed and $b$ is random.
 
However, the lower bound is obtained by considering a distribution of instances of the form $Ax = b$, where $A$ is fixed and $b$ is random.
Hence, we cannot directly apply the construction to the rank problem as we only have $A$.
+
Hence, we cannot directly use the construction for the rank problem as we only have $A$.

Please note that all contributions to Open Problems in Sublinear Algorithms may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see Open Problems in Sublinear Algorithms:Copyrights for details). Do not submit copyrighted work without permission!

To edit this page, please answer the question that appears below (more info):

Cancel Editing help (opens in new window)