# Difference between revisions of "Open Problems:34"

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It is often not the case that the entire data sits on a single machine and that we are allowed to make one or more passes over it. Instead the data is often distributed across multiple systems. This is one of the reasons why the streaming model does not have more impact in practice for linear algebra computation. It would be great to design new models that address this shortcoming. | It is often not the case that the entire data sits on a single machine and that we are allowed to make one or more passes over it. Instead the data is often distributed across multiple systems. This is one of the reasons why the streaming model does not have more impact in practice for linear algebra computation. It would be great to design new models that address this shortcoming. | ||

Consider also the following problem. Let $A$ be an $m\times n$ matrix and let $k$ be a rank parameter. Let $P_{A,k}$ be the projection matrix on the best rank-$k$ left (or right) singular subspace. The goal is to compute the diagonal of $P_{A,k}$ exactly or approximately in a small number of passes in the streaming model, or even better, in a new model that addresses the aforementioned shortcoming. | Consider also the following problem. Let $A$ be an $m\times n$ matrix and let $k$ be a rank parameter. Let $P_{A,k}$ be the projection matrix on the best rank-$k$ left (or right) singular subspace. The goal is to compute the diagonal of $P_{A,k}$ exactly or approximately in a small number of passes in the streaming model, or even better, in a new model that addresses the aforementioned shortcoming. |

## Revision as of 05:27, 16 November 2012

Suggested by | Michael Mahoney |
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Source | Kanpur 2009 |

Short link | https://sublinear.info/34 |

It is often not the case that the entire data sits on a single machine and that we are allowed to make one or more passes over it. Instead the data is often distributed across multiple systems. This is one of the reasons why the streaming model does not have more impact in practice for linear algebra computation. It would be great to design new models that address this shortcoming.

Consider also the following problem. Let $A$ be an $m\times n$ matrix and let $k$ be a rank parameter. Let $P_{A,k}$ be the projection matrix on the best rank-$k$ left (or right) singular subspace. The goal is to compute the diagonal of $P_{A,k}$ exactly or approximately in a small number of passes in the streaming model, or even better, in a new model that addresses the aforementioned shortcoming.