Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 172 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 29 tok/s Pro
GPT-5 High 27 tok/s Pro
GPT-4o 94 tok/s Pro
Kimi K2 194 tok/s Pro
GPT OSS 120B 451 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

PRIME: Phase Retrieval via Majorization-Minimization (1511.01669v2)

Published 5 Nov 2015 in cs.IT and math.IT

Abstract: This paper considers the phase retrieval problem in which measurements consist of only the magnitude of several linear measurements of the unknown, e.g., spectral components of a time sequence. We develop low-complexity algorithms with superior performance based on the majorization-minimization (MM) framework. The proposed algorithms are referred to as PRIME: Phase Retrieval vIa the Majorization-minimization techniquE. They are preferred to existing benchmark methods since at each iteration a simple surrogate problem is solved with a closed-form solution that monotonically decreases the original objective function. In total, four algorithms are proposed using different majorization-minimization techniques. Experimental results validate that our algorithms outperform existing methods in terms of successful recovery and mean square error under various settings.

Citations (54)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.