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Andrei Patrascu
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2020 – today
- 2024
- [i10]Andrei Patrascu, Cristian Rusu, Paul Irofti:
Learning Explicitly Conditioned Sparsifying Transforms. CoRR abs/2403.03168 (2024) - [i9]Paul Irofti, Andrei Iulian Hîji, Andrei Patrascu, Nicolae Cleju:
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection. CoRR abs/2404.04064 (2024) - 2022
- [j11]Andrei Patrascu
, Paul Irofti
:
On finite termination of an inexact Proximal Point algorithm. Appl. Math. Lett. 134: 108348 (2022) - [c9]Paul Irofti
, Cristian Rusu, Andrei Patrascu:
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection. ICASSP 2022: 3378-3382 - [c8]Paul Irofti
, Andrei Patrascu
, Andrei Iulian Hîji
:
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis. COMM 2022: 1-6 - [i8]Paul Irofti, Cristian Rusu, Andrei Patrascu:
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection. CoRR abs/2201.03869 (2022) - [i7]Paul Irofti
, Andrei Patrascu, Andrei Iulian Hîji:
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis. CoRR abs/2205.07109 (2022) - 2021
- [j10]Andrei Patrascu
, Paul Irofti
:
Stochastic proximal splitting algorithm for composite minimization. Optim. Lett. 15(6): 2255-2273 (2021) - [i6]Andrei Patrascu, Paul Irofti:
Computational complexity of Inexact Proximal Point Algorithm for Convex Optimization under Holderian Growth. CoRR abs/2108.04482 (2021) - 2020
- [i5]Andrei Patrascu, Ciprian Paduraru, Paul Irofti:
Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models. CoRR abs/2003.13332 (2020)
2010 – 2019
- 2019
- [j9]Ion Necoara, Andrei Patrascu
, François Glineur
:
Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming. Optim. Methods Softw. 34(2): 305-335 (2019) - [j8]Ion Necoara, Peter Richtárik
, Andrei Patrascu:
Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. SIAM J. Optim. 29(4): 2814-2852 (2019) - [i4]Andrei Patrascu:
On convergence rate of stochastic proximal point algorithm without strong convexity, smoothness or bounded gradients. CoRR abs/1901.08663 (2019) - [i3]Paul Irofti, Andrei Patrascu, Andra Baltoiu:
Fraud Detection in Networks: State-of-the-art. CoRR abs/1910.11299 (2019) - [i2]Andra Baltoiu, Andrei Patrascu, Paul Irofti:
Community-Level Anomaly Detection for Anti-Money Laundering. CoRR abs/1910.11313 (2019) - [i1]Andrei Patrascu, Paul Irofti:
Stochastic proximal splitting algorithm for stochastic composite minimization. CoRR abs/1912.02039 (2019) - 2018
- [j7]Andrei Patrascu
, Ion Necoara
:
On the Convergence of Inexact Projection Primal First-Order Methods for Convex Minimization. IEEE Trans. Autom. Control. 63(10): 3317-3329 (2018) - [c7]Ion Necoara, Andrei Patrascu
:
OR-SAGA: Over-relaxed stochastic average gradient mapping algorithms for finite sum minimization. ECC 2018: 489-494 - 2017
- [j6]Andrei Patrascu, Ion Necoara:
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization. J. Mach. Learn. Res. 18: 198:1-198:42 (2017) - [j5]Andrei Patrascu
, Ion Necoara, Quoc Tran-Dinh:
Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization. Optim. Lett. 11(3): 609-626 (2017) - 2016
- [j4]Ion Necoara, Andrei Patrascu
:
Iteration complexity analysis of dual first-order methods for conic convex programming. Optim. Methods Softw. 31(3): 645-678 (2016) - [c6]Andrei Patrascu
, Ion Necoara:
Complexity certifications of inexact projection primal gradient method for convex problems: Application to embedded MPC. MED 2016: 125-130 - 2015
- [j3]Andrei Patrascu
, Ion Necoara:
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. J. Glob. Optim. 61(1): 19-46 (2015) - [j2]Andrei Patrascu
, Ion Necoara:
Random Coordinate Descent Methods for ℓ0 Regularized Convex Optimization. IEEE Trans. Autom. Control. 60(7): 1811-1824 (2015) - [c5]Ion Necoara, Andrei Patrascu
:
On the behavior of first-order penalty methods for conic constrained convex programming when Lagrange multipliers do not exist. CDC 2015: 1378-1383 - [c4]Andrei Patrascu
, Ion Necoara, Rolf Findeisen
:
Rate of convergence analysis of a dual fast gradient method for general convex optimization. CDC 2015: 3311-3316 - [c3]Andrei Patrascu
, Ion Necoara:
Random Coordinate Descent Methods for Sparse Optimization: Application to Sparse Control. CSCS 2015: 909-914 - 2014
- [j1]Ion Necoara, Andrei Patrascu
:
A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints. Comput. Optim. Appl. 57(2): 307-337 (2014) - [c2]Andrei Patrascu
, Ion Necoara, Panagiotis Patrinos
:
A proximal alternating minimization method for ℓ0-regularized nonlinear optimization problems: application to state estimation. CDC 2014: 4254-4259 - 2013
- [c1]Andrei Patrascu, Ion Necoara:
A random coordinate descent algorithm for large-scale sparse nonconvex optimization. ECC 2013: 2789-2794
Coauthor Index
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last updated on 2024-05-14 21:18 CEST by the dblp team
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