11 citations · 46 across the 15 of their papers we have counts for
16 papers
Complexity of Inexact Proximal Point Algorithm for minimizing convex functions with Holderian Growth
Andrei Pătraşcu, Paul Irofti
Several decades ago the Proximal Point Algorithm (PPA) started to gain a long-lasting attraction for both abstract operator theory and numerical optimization communities. Even in m…
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…
Community-Level Anomaly Detection for Anti-Money Laundering
Andra Baltoiu, Andrei Patrascu, Paul Irofti
Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example,…
Quick survey of graph-based fraud detection methods
Paul Irofti, Andrei Patrascu, Andra Baltoiu
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected pr…
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…
Complexity of first order inexact Lagrangian and penalty methods for conic convex programming
Ion Necoara, Andrei Patrascu, Francois Glineur
In this paper we present a complete iteration complexity analysis of inexact first order Lagrangian and penalty methods for solving cone constrained convex problems that have or ma…
Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization
Andrei Patrascu, Ion Necoara, Quoc Tran-Dinh
In this paper we analyze several inexact fast augmented Lagrangian methods for solving linearly constrained convex optimization problems. Mainly, our methods rely on the combinatio…
DuQuad: an inexact (augmented) dual first order algorithm for quadratic programming
Ion Necoara, Andrei Patrascu
In this paper we present the solver DuQuad specialized for solving general convex quadratic problems arising in many engineering applications. When it is difficult to project on th…
Iteration complexity analysis of dual first order methods for conic convex programming
Ion Necoara, Andrei Patrascu
In this paper we provide a detailed analysis of the iteration complexity of dual first order methods for solving conic convex problems. When it is difficult to project on the prima…
Iteration complexity analysis of random coordinate descent methods for regularized convex problems
Andrei Patrascu, Ion Necoara
In this paper we analyze a family of general random block coordinate descent methods for the minimization of regularized optimization problems, i.e. the objective function…
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization
A. Patrascu, I. Necoara
In this paper we analyze several new methods for solving nonconvex optimization problems with the objective function formed as a sum of two terms: one is nonconvex and smooth, and…
A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints
Ion Necoara, Andrei Patrascu
In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization problems with composite objective functions. If the…