Research map / Optimization / Nonconvex and smooth optimization
Convex Phase Retrieval without Lifting via PhaseMax
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace non-convex equality constraints |<a_i,x>|=b_i with convex inequalities |<a_i,x>|<=b_i and maximize <x,xhat> | PhaseMax · noiseless Gaussian phase retrieval | exact recovery (success probability) | No clear effect |
| Use truncated spectral initializer (oversampling ~6 or higher) before PhaseMax instead of random guess | PhaseMax · Gaussian phase retrieval | oversampling ratio required for recovery | Mixed |
| Lift problem to n x n matrix (PhaseLift/PhaseCut) instead of working in original dimension | phase retrieval convex relaxation · Gaussian phase retrieval | computational tractability | Hurt |
| Empirical success probability vs theoretical lower bound on PhaseMax recovery as a function of initial guess angle | PhaseMax · random Gaussian problems, n=100 and n=500 | success probability vs oversampling ratio | No clear effect |
Closest papers by meaning
- Blind Deconvolutional Phase Retrieval via Convex Programming (NeurIPS 2018 · similarity 0.90)
- Fast Compressive Phase Retrieval under Bounded Noise (AAAI 2017 · similarity 0.89)
- Universality in Learning from Linear Measurements (NeurIPS 2019 · similarity 0.89)
- A Fast and Provable Algorithm for Sparse Phase Retrieval (ICLR 2024 · similarity 0.89)
- A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval (NeurIPS 2020 · similarity 0.88)
- Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval (ICML 2024 · similarity 0.86)
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