Difference between revisions of "Open Problems:68"
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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 | + | 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 | + | For a parameter \epsilon > 0, we want to approximate the rank of A to within \pm \epsilon n. |
− | How many queries are needed | + | How many queries are needed to accomplish this task? |
− | When p = 2 and each | + | 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. | ||
− | + | 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, | + | In general, we conjecture that \Omega(n) queries are necessary. |
− | The difficulty | + | 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}} | + | 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 | + | 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 |
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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.