Andrei Patrascu

According to our database, Andrei Patrascu authored at least 27 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Learning Explicitly Conditioned Sparsifying Transforms.

Andrei Patrascu, Cristian Rusu, Paul Irofti

CoRR, 2024

2022

On finite termination of an inexact Proximal Point algorithm.

Andrei Patrascu, Paul Irofti

Appl. Math. Lett., 2022

Unsupervised Abnormal Traffic Detection through Topological Flow Analysis.

Paul Irofti, Andrei Patrascu, Andrei Iulian Hîji

Proceedings of the 14th International Conference on Communications, 2022

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection.

Paul Irofti, Cristian Rusu, Andrei Patrascu

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Stochastic proximal splitting algorithm for composite minimization.

Andrei Patrascu, Paul Irofti

Optim. Lett., 2021

Computational complexity of Inexact Proximal Point Algorithm for Convex Optimization under Holderian Growth.

Andrei Patrascu, Paul Irofti

CoRR, 2021

2020

Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models.

Andrei Patrascu, Ciprian Paduraru, Paul Irofti

CoRR, 2020

2019

Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates.

Ion Necoara, Peter Richtárik, Andrei Patrascu

SIAM J. Optim., 2019

Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming.

Ion Necoara, Andrei Patrascu, François Glineur

Optim. Methods Softw., 2019

Stochastic proximal splitting algorithm for stochastic composite minimization.

Andrei Patrascu, Paul Irofti

CoRR, 2019

Community-Level Anomaly Detection for Anti-Money Laundering.

Andra Baltoiu, Andrei Patrascu, Paul Irofti

CoRR, 2019

Fraud Detection in Networks: State-of-the-art.

Paul Irofti, Andrei Patrascu, Andra Baltoiu

CoRR, 2019

On convergence rate of stochastic proximal point algorithm without strong convexity, smoothness or bounded gradients.

Andrei Patrascu

CoRR, 2019

2018

On the Convergence of Inexact Projection Primal First-Order Methods for Convex Minimization.

Andrei Patrascu, Ion Necoara

IEEE Trans. Autom. Control., 2018

OR-SAGA: Over-relaxed stochastic average gradient mapping algorithms for finite sum minimization.

Ion Necoara, Andrei Patrascu

Proceedings of the 16th European Control Conference, 2018

2017

Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization.

Andrei Patrascu, Ion Necoara, Quoc Tran-Dinh

Optim. Lett., 2017

Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization.

Andrei Patrascu, Ion Necoara

J. Mach. Learn. Res., 2017

2016

Iteration complexity analysis of dual first-order methods for conic convex programming.

Ion Necoara, Andrei Patrascu

Optim. Methods Softw., 2016

Complexity certifications of inexact projection primal gradient method for convex problems: Application to embedded MPC.

Andrei Patrascu, Ion Necoara

Proceedings of the 24th Mediterranean Conference on Control and Automation, 2016

2015

Random Coordinate Descent Methods for ℓ₀ Regularized Convex Optimization.

Andrei Patrascu, Ion Necoara

IEEE Trans. Autom. Control., 2015

Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization.

Andrei Patrascu, Ion Necoara

J. Glob. Optim., 2015

Random Coordinate Descent Methods for Sparse Optimization: Application to Sparse Control.

Andrei Patrascu, Ion Necoara

Proceedings of the 20th International Conference on Control Systems and Computer Science, 2015

Rate of convergence analysis of a dual fast gradient method for general convex optimization.

Andrei Patrascu, Ion Necoara, Rolf Findeisen

Proceedings of the 54th IEEE Conference on Decision and Control, 2015

On the behavior of first-order penalty methods for conic constrained convex programming when Lagrange multipliers do not exist.

Ion Necoara, Andrei Patrascu

Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2014

A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints.

Ion Necoara, Andrei Patrascu

Comput. Optim. Appl., 2014

A proximal alternating minimization method for ℓ0-regularized nonlinear optimization problems: application to state estimation.

Andrei Patrascu, Ion Necoara, Panagiotis Patrinos

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013

A random coordinate descent algorithm for large-scale sparse nonconvex optimization.

Andrei Patrascu, Ion Necoara

Proceedings of the 12th European Control Conference, 2013