Difference between revisions of "Open Problems:68"

From Open Problems in Sublinear Algorithms
Jump to: navigation, search
m (Krzysztof Onak moved page Waiting:Yuichi Yoshida to Open Problems:68)
 
(5 intermediate revisions by 3 users not shown)
Line 1: Line 1:
 
{{Header
 
{{Header
|title=Approximating Rank in the Bounded-Degree Model
+
|source=bertinoro14
|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 is given as a query access:
+
The matrix A can be accessed via the following types of queries.
 
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 whithin \pm \epsilon n.
+
For a parameter \epsilon > 0, we want to approximate the rank of A to within \pm \epsilon n.
How many queries are needed for this task?
+
How many queries are needed to accomplish this task?
  
When p = 2 and each column has exactly two ones, A can be seen as an incidence matrix,
+
When p = 2 and each row has exactly two ones, A can be seen as the incidence matrix of a graph,
 
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.
So, we can approximate the rank with O(1/\epsilon^2) queries.
+
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}}.
  
In general, the conjecture is \Omega(n).
+
In general, we conjecture that \Omega(n) queries are necessary.
The difficulty is that not so many techniques are known to show \Omega(n) lower bound for the bounded-degree model.
+
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.
{{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.
+
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.
 
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 use the construction for the rank problem as we only have A.
+
Hence, we cannot directly apply the construction to the rank problem as we only have A.

Latest revision as of 23:03, 13 June 2014

Suggested by Yuichi Yoshida
Source Bertinoro 2014
Short link https://sublinear.info/68

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. 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. For a parameter \epsilon > 0, we want to approximate the rank of A to within \pm \epsilon n. How many queries are needed to accomplish this task?

When p = 2 and each row has exactly two ones, A can be seen as the incidence matrix of a graph, 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 [ChazelleRT-05].

In general, we conjecture that \Omega(n) queries are necessary. 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. Bogdanov, Obata, and Trevisan [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. 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.