  Traditional methods for solving linear systems have quickly become
impractical due to an increase in the size of available data. Utilizing massive
amounts of data is further complicated when the data is incomplete or has
missing entries. In this work, we address the obstacles presented when working
with large data and incomplete data simultaneously. In particular, we propose
to adapt the Stochastic Gradient Descent method to address missing data in
linear systems. Our proposed algorithm, the Stochastic Gradient Descent for
Missing Data method (mSGD), is introduced and theoretical convergence
guarantees are provided. In addition, we include numerical experiments on
simulated and real world data that demonstrate the usefulness of our method.
