Doubly stochastic primal dual splitting algorithm with variance reduction for saddle point problems

The (structured) saddle-point problem involving the infimal convolution in real Hilbert spaces finds applicability in many applied mathematics disciplines. For this purpose, we develop a stochastic primal-dual splitting (PDS) algorithm with loopless variance-reduction (VR) for solving this generic problem. A PDS algorithm aims to overcome the well-known shortcomings of common splitting methods by solving the … Read more

Gradient methods for convex minimization: better rates under weaker conditions

The convergence behavior of gradient methods for minimizing convex differentiable functions is one of the core questions in convex optimization. This paper shows that their well-known complexities can be achieved under conditions weaker than the commonly assumed ones. We relax the common gradient Lipschitz-continuity condition and strong convexity condition to ones that hold only over … Read more