# Average-case hardness of RIP certification

@inproceedings{Wang2016AveragecaseHO, title={Average-case hardness of RIP certification}, author={Tengyao Wang and Quentin Berthet and Yaniv Plan}, booktitle={NIPS}, year={2016} }

The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models. It is of high interest in compressed sensing and statistical learning. This property is particularly important for computationally efficient recovery methods. As a consequence, even though it is in general NP-hard to check that RIP holds, there have been substantial efforts to find tractable proxies for it. These would allow the construction of RIP matrices and the… CONTINUE READING

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#### References

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SHOWING 1-10 OF 51 REFERENCES

## Computing performance guarantees for compressed sensing

VIEW 1 EXCERPT

## Certifying the Restricted Isometry Property is Hard

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Computational Barriers in Minimax Submatrix Detection

VIEW 3 EXCERPTS