This $100,000 three-year Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to advance numerical methods for large-scale inverse problems, sparse principal component analysis, and their applications. The awardee, Emory University, will collaborate with other institutions to develop novel approaches merging inverse problems, randomized numerical linear algebra, and sparse principal component analysis. This will include investigating advanced iterative methods, randomization and sketching schemes. The project aims to accelerate numerical methods, provide theoretical convergence analysis, and produce user-friendly software. By integrating these advanced tools into application areas like machine learning, geophysics and genetics, the project seeks to significantly advance solutions to large-scale data challenges across various scientific domains. Training opportunities for students in computational and applied mathematics are also part of the effort.
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