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A Dynamic Working Set Method for Compressed Sensing
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DOI：10.1007/978-981-95-0218-9_12.png)
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Abstract
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We propose a dynamic working set method (DWS) for the problem min(x is an element of Rn) 1/2 parallel to Ax-b parallel to(2) +eta parallel to x parallel to(1) that arises from compressed sensing. DWS manages the working set while iteratively calling a regression solver to generate progressively better solutions. Our experiments show that DWS is more efficient than other state-of-the-art software in the context of compressed sensing. Scale space such that parallel to b parallel to = 1. Let s be the number of non-zeros in the unknown signal. We prove that for any given epsilon > 0, DWS reaches a solution with an additive error epsilon/eta(2) such that each call of the solver uses only O(1/epsilon s log s log 1/epsilon) variables, and each intermediate solution has O(1/epsilon s log s log 1/epsilon) non-zero coordinates.
Keywords:
Compressed sensing
working set
linear regression
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