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Creators/Authors contains: "Paquette, Courtney"

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  1. ; ; ; ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Free, publicly-accessible full text available May 5, 2026
  2. ; (, SIAM Journal on Optimization)
    null (Ed.)
    Full Text Available 
  3. ; ; ; (, Journal of Optimization Theory and Applications)
    Full Text Available 
  4. ; ; (, IMA Journal of Numerical Analysis)
    Abstract We consider a popular nonsmooth formulation of the real phase retrieval problem. We show that under standard statistical assumptions a simple subgradient method converges linearly when initialized within a constant relative distance of an optimal solution. Seeking to understand the distribution of the stationary points of the problem, we complete the paper by proving that as the number of Gaussian measurements increases, the stationary points converge to a codimension two set, at a controlled rate. Experiments on image recovery problems illustrate the developed algorithm and theory. 
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