Difference between revisions of "Open Problems:50"

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'''Question:''' Is it possible to achieve $m=O(k)$ for some constant $C>0$?  
 
'''Question:''' Is it possible to achieve $m=O(k)$ for some constant $C>0$?  
  
'''Background:''' It is known that a weaker bound of $m=O(k \log(n/k))$ is possible to achieve even if ${\mathcal T}_k$ is replaced by the set of all $k$-subsets of $[n]$ {{cite|CandesRT-06a}}. However, since $|{\mathcal T}_k | =\exp(O(k))$, one can expect a better bound for ${\mathcal T}_k$. By using ''model-based compressive sensing'' framework of {{cite|BaraniukCDH-10}} (cf. {{cite|IndykP-11}}), one can achieve the desired bound of $m=O(k)$ but  with ''superconstant'' $C$.
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'''Background:''' the best bound on $m$ known is $m = O(k \log(n / k) / \log \log (n / k))$ {{cite|IndykR-13}}. If one insists of having $m = O(k)$, then the best $C$ we know how to achieve is $O(\sqrt{\log n})$ {{cite|IndykP-11}}
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(building on {{cite|BaraniukCDH-10}}).

Revision as of 17:20, 17 April 2013

Suggested by Piotr Indyk
Source Bertinoro 2011
Short link https://sublinear.info/50

For any $n=2^{h}-1$, we can identify the coordinates of a vector $v \in \mathbb R^n$ with the nodes of a full binary tree $B_h$ of height $h$ and root $1$. We define a $k$-sparse tree model ${\mathcal T}_k$ to be a set of all $T \subset [n]$ of size $k$ which form a connected subtree in $B_h$ rooted at $1$.

We want to design an $m \times n$ matrix $A$ such that for any $x \in \mathbb R^n$, one can recover from $Ax$ a vector $x^* \in \mathbb R^n$ such that:

\[ \left\|x^*-x\right\|_1 \leq \min_{x'\in\mathbb R^n,\ \operatorname{supp}(x') \subset T \mbox{ for some } T \in {\mathcal T}_k } C \left\|x'-x\right\|_1, \] where $\operatorname{supp}(x')$ is the set of non-zero coefficients of $x'$, and $C>0$ is a constant.

Question: Is it possible to achieve $m=O(k)$ for some constant $C>0$?

Background: the best bound on $m$ known is $m = O(k \log(n / k) / \log \log (n / k))$ [IndykR-13]. If one insists of having $m = O(k)$, then the best $C$ we know how to achieve is $O(\sqrt{\log n})$ [IndykP-11] (building on [BaraniukCDH-10]).