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A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation (2010)
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| Venue: | SIAM Journal on Scientific Computing |
| Citations: | 52 - 8 self |
BibTeX
@ARTICLE{Wen10afast,
author = {Zaiwen Wen and Wotao Yin and Donald Goldfarb and Yin Zhang},
title = {A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation},
journal = {SIAM Journal on Scientific Computing},
year = {2010},
pages = {1857}
}
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Abstract
Abstract. We propose a fast algorithm for solving the ℓ1-regularized minimization problem minx∈R n µ‖x‖1 + ‖Ax − b ‖ 2 2 for recovering sparse solutions to an undetermined system of linear equations Ax = b. The algorithm is divided into two stages that are performed repeatedly. In the first stage a first-order iterative method called “shrinkage ” yields an estimate of the subset of components of x likely to be nonzero in an optimal solution. Restricting the decision variables x to this subset and fixing their signs at their current values reduces the ℓ1-norm ‖x‖1 to a linear function of x. The resulting subspace problem, which involves the minimization of a smaller and smooth quadratic function, is solved in the second phase. Our code FPC AS embeds this basic two-stage algorithm in a continuation (homotopy) approach by assigning a decreasing sequence of values to µ. This code exhibits state-of-the-art performance both in terms of its speed and its ability to recover sparse signals. It can even recover signals that are not as sparse as required by current compressive sensing theory.
Keyphrases
fast algorithm subspace optimization current value first-order iterative method linear equation ax 1-regularized minimization problem minx optimal solution quadratic function state-of-the-art performance subspace problem sparse signal undetermined system current compressive sensing theory linear function first stage basic two-stage algorithm shrinkage yield code fpc sparse solution second phase
