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Efficient Sparse Phase Retrieval Algorithm with Optimal Sampling Complexity


Core Concepts
The Subspace Phase Retrieval (SPR) algorithm can accurately recover an n-dimensional k-sparse complex-valued signal from Ω(k log n) magnitude-only Gaussian samples, attaining the information-theoretic sampling complexity for sparse phase retrieval.
Abstract
The content discusses an efficient algorithm called Subspace Phase Retrieval (SPR) for solving the sparse phase retrieval problem. The key highlights are: Initialization: SPR's initialization only requires capturing a subset of the support indices with sufficient energy, rather than seeking a good estimation that falls into the δ-neighborhood of the true signal or accurate support recovery. This initialization can achieve the information-theoretic sampling complexity of Ω(k log n) under certain conditions on the signal structure. Non-Initial Step: SPR iteratively refines the estimated support set through matching and pruning operations, while estimating the signal itself by minimizing a quartic objective function. The geometric property of this subproblem is shown to be benign, allowing efficient iterative methods to solve it. With the assumption on the minimum nonzero entry of the signal, SPR can exactly recover the original signal with high probability using Ω(k log n) samples, which attains the information-theoretic bound. The analysis demonstrates that SPR can achieve the optimal sampling complexity for sparse phase retrieval, outperforming existing algorithms that suffer from the computational-to-statistical gap.
Stats
m ≥ C max{k log^3 k, k log n, sqrt(k) log^3 n} m ≥ C max{ŝ^2 log(n/ŝ), k log^3 k, k log n, sqrt(k) log^3 n}
Quotes
"SPR does not seek a good estimation that falls into the δ-neighborhood of x, or an accurate support recovery. Instead, it only requires to capture a subset of support indices with sufficient energy." "Capturing sufficient energy in initialization directly leads to a benign geometric property of the target subspace for estimating the sparse signal."

Key Insights Distilled From

by Mengchu Xu,D... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2206.02480.pdf
Subspace Phase Retrieval

Deeper Inquiries

How does the performance of SPR compare to other sparse phase retrieval algorithms in practical scenarios beyond the theoretical analysis

In practical scenarios, the performance of the Subspace Phase Retrieval (SPR) algorithm has shown significant improvements compared to other sparse phase retrieval algorithms. One key advantage of SPR is its ability to accurately recover sparse signals with a reduced number of magnitude-only samples, approaching the information-theoretic sampling complexity. This efficiency is crucial in applications where data acquisition is limited or costly. Additionally, the initialization step of SPR, which focuses on capturing a subset of support indices with sufficient energy, provides a robust starting point for the subsequent iterations, leading to improved convergence and accuracy in signal recovery. Furthermore, the iterative nature of SPR, involving matching, estimation, and pruning operations, allows for the refinement of the estimated support set and the signal estimate, leading to precise recovery of the original signal. The algorithm's ability to handle various signal structures and noise levels makes it versatile and applicable to a wide range of real-world scenarios. Overall, the performance of SPR in practical applications has demonstrated state-of-the-art reconstruction performance compared to existing algorithms.

What are the potential limitations or drawbacks of the SPR algorithm that may arise in real-world applications

While the Subspace Phase Retrieval (SPR) algorithm offers significant advantages in sparse phase retrieval, there are potential limitations and drawbacks that may arise in real-world applications. One limitation is the computational complexity of the algorithm, especially in large-scale applications where the dimensionality of the signal is high. The iterative nature of SPR, involving multiple operations such as matching, estimation, and pruning, can lead to increased computational overhead, especially when dealing with large datasets. Another potential drawback of SPR is its sensitivity to noise and model mismatch. In real-world scenarios, signals may be corrupted by noise or may not strictly adhere to the assumed sparse model, leading to suboptimal performance of the algorithm. Additionally, the theoretical guarantees provided by SPR may not always translate directly to practical scenarios, where additional factors such as measurement noise, model inaccuracies, and computational constraints come into play. Furthermore, the initialization step of SPR, while effective in capturing a subset of support indices with sufficient energy, may still require fine-tuning or additional constraints to handle more complex signal structures or noise levels effectively. Overall, while SPR offers significant advantages in sparse phase retrieval, addressing these limitations and drawbacks is essential for its successful application in real-world settings.

Can the ideas and techniques used in the SPR algorithm be extended to solve other types of non-convex optimization problems involving sparse or structured signals

The ideas and techniques used in the Subspace Phase Retrieval (SPR) algorithm can be extended to solve other types of non-convex optimization problems involving sparse or structured signals. One potential extension is the application of the matching, estimation, and pruning operations in SPR to other optimization problems that require the recovery of sparse signals from noisy or incomplete measurements. By adapting the iterative framework of SPR and incorporating domain-specific constraints or priors, similar algorithms can be developed for a wide range of signal recovery tasks. Additionally, the correlation-promoting initialization strategy used in SPR can be applied to other optimization problems to enhance the efficiency and accuracy of the initial estimation. The concept of iteratively refining the estimated support set and signal estimate can be generalized to various non-convex optimization problems where the underlying signal structure is known or can be inferred. Overall, the principles and methodologies employed in SPR, such as leveraging sparse priors, iterative refinement, and robust initialization strategies, can serve as a foundation for developing innovative algorithms for solving a diverse set of non-convex optimization problems involving sparse or structured signals.
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