11 citations · 26 across the 6 of their papers we have counts for
9 papers
Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models
Andrei Patrascu, Ciprian Paduraru, Paul Irofti
Stochastic optimization lies at the core of most statistical learning models. The recent great development of stochastic algorithmic tools focused significantly onto proximal gradi…
Stochastic proximal splitting algorithm for composite minimization
Andrei Patrascu, Paul Irofti
Supported by the recent contributions in multiple branches, the first-order splitting algorithms became central for structured nonsmooth optimization. In the large-scale or noisy c…
New nonasymptotic convergence rates of stochastic proximal pointalgorithm for convex optimization problems
Andrei Patrascu
Large sectors of the recent optimization literature focused in the last decade on the development of optimal stochastic first order schemes for constrained convex models under prog…
Randomized projection methods for convex feasibility problems: conditioning and convergence rates
Ion Necoara, Peter Richtarik, Andrei Patrascu
Finding a point in the intersection of a collection of closed convex sets, that is the convex feasibility problem, represents the main modeling strategy for many computational prob…
Nonasymptotic convergence of stochastic proximal point algorithms for constrained convex optimization
Andrei Patrascu, Ion Necoara
A very popular approach for solving stochastic optimization problems is the stochastic gradient descent method (SGD). Although the SGD iteration is computationally cheap and the pr…
Complexity certifications of first order inexact Lagrangian methods for general convex programming
Ion Necoara, Andrei Patrascu, Angelia Nedić
In this chapter we derive computational complexity certifications of first order inexact dual methods for solving general smooth constrained convex problems which can arise in real…