Unifying mirror descent and dual averaging

Understand

We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging.

• Within the framework of this family, we define new algorithms for constrained optimization that combines the advantages of mirror descent and dual averaging.
• Our preliminary simulation study shows that these new algorithms significantly outperform available methods in some situations.