Composite objective mirror descent

We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known first-order algorithms, such as the projected gradient method, mirror descent, and forward-backward splitting, our method yields new analysis and algorithms. We also derive specific instantiations of our method for commonly used regularization functions, such as `1, mixed norm, and trace-norm.