  We analyze and evaluate an online gradient descent algorithm with adaptive
per-coordinate adjustment of learning rates. Our algorithm can be thought of as
an online version of batch gradient descent with a diagonal preconditioner.
This approach leads to regret bounds that are stronger than those of standard
online gradient descent for general online convex optimization problems.
Experimentally, we show that our algorithm is competitive with state-of-the-art
algorithms for large scale machine learning problems.
