We consider network-based decentralized optimization problems, where eac...
Simple stochastic momentum methods are widely used in machine learning
o...
We consider unconstrained stochastic optimization problems with no avail...
We consider stochastic optimization problems where a smooth (and potenti...
The motivation for this paper stems from the desire to develop an adapti...
We consider stochastic zero-order optimization problems, which arise in
...
The standard L-BFGS method relies on gradient approximations that are no...
In this paper, we propose a stochastic optimization method that adaptive...
The concepts of sketching and subsampling have recently received much
at...
The paper studies the solution of stochastic optimization problems in wh...