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Chapter 8: Convex Optimization for Structured Signal Recovery

Chapter 8: Convex Optimization for Structured Signal Recovery

pp. 305-364

Authors

, Columbia University, New York, , University of California, Berkeley
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Summary

In the previous theoretical Part I of the book, we showed that under fairly broad conditions on the number of measurements needed, many important classes of structured signals can be recovered via computationally tractable optimization problems, such as ℓ1 minimization for recovering sparse signals and nuclear norm minimization for recovering low-rank matrices.

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