JMLR: Workshop and Conference Proceedings vol 35:1–41, 2014 Fast Matrix Completion Without the Condition Number

We give the first algorithm for Matrix Completion that achieves running time and sample com-plexity that is polynomial in the rank of the unknown target matrix, linear in the dimension of the matrix, and logarithmic in the condition number of the matrix. To the best of our knowledge, all previous algorithms either incurred a quadratic dependence on the condition number of the un-known matrix or a quadratic dependence on the dimension of the matrix. Our algorithm is based on a novel extension of Alternating Minimization which we show has theoretical guarantees under standard assumptions even in the presence of noise. 1