A continuous-time mirror descent approach to sparse phase retrieval
We analyze continuous-time mirror descent applied to sparse phase retrieval, which is the problem of recovering sparse signals from a set of magnitude-only measurements. We apply mirror descent to the unconstrained empirical risk minimization problem (batch setting), using the square loss and square...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation, Inc.
2020
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