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- W2920004471 abstract "Stochastic iterative algorithms such as the Randomized Kaczmarz and Stochastic Gradient Descent have gained recent popularity for large-scale linear systems due to their simplicity and wide applicability. While these approaches are a popular choice for large-scale data, incomplete or missing data further complicates the problem and is typically addressed independently of solving the linear system. In this work, we adapt the Stochastic Gradient Descent algorithm to handle linear systems with missing data. By doing so, we address the issue of missing and large-scale data simultaneously. Along with deriving our method, the Adapted Stochastic Gradient Descent for Missing Data (mSGD), we also provide theoretical convergence guarantees and present numerical experiments on simulated and real world data to demonstrate the usefulness our approach." @default.
- W2920004471 created "2019-03-11" @default.
- W2920004471 creator A5024153474 @default.
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- W2920004471 date "2018-10-01" @default.
- W2920004471 modified "2023-09-27" @default.
- W2920004471 title "A Gradient Descent Approach for Incomplete Linear Systems" @default.
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- W2920004471 doi "https://doi.org/10.1109/acssc.2018.8645206" @default.
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