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Panos Parpas
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2020 – today
- 2025
- [j35]Panos Parpas, Corey Muir:
Predict globally, correct locally: Parallel-in-time optimization of neural networks. Autom. 171: 111976 (2025) - 2024
- [j34]Anastasia Borovykh, Nikolas Kantas, Panos Parpas
, Grigorios A. Pavliotis
Stochastic Mirror Descent for Convex Optimization with Consensus Constraints. SIAM J. Appl. Dyn. Syst. 23(3): 2208-2241 (2024) - [j33]Tony Lelièvre, Panos Parpas
Using Witten Laplacians to Locate Index-1 Saddle Points. SIAM J. Sci. Comput. 46(2): 770- (2024) - [c6]Aadhithya Ashok Naarayan
Stable Multilevel Deep Neural Networks for Option Pricing and xVAs Using Forward-Backward Stochastic Differential Equations. ICAIF 2024: 336-343 - [i9]Daniel Lengyel, Panos Parpas, Boumediene Hamzi, Houman Owhadi:
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach. CoRR abs/2408.06465 (2024) - 2023
- [i8]Anastasia Borovykh, Nikolas Kantas, Panos Parpas, Greg Pavliotis:
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds. CoRR abs/2302.00766 (2023) - [i7]Daniel Lengyel, Panos Parpas, Nikolas Kantas, Nicholas R. Jennings:
Curvature Aligned Simplex Gradient: Principled Sample Set Construction For Numerical Differentiation. CoRR abs/2310.12712 (2023) - 2022
- [j32]Chin Pang Ho
Newton-type multilevel optimization method. Optim. Methods Softw. 37(1): 45-78 (2022) - [j31]Berç Rustem, Panos Parpas:
In Memoriam: Nicos Christofides (1942-2019). Oper. Res. Forum 3(1) (2022) - [i6]Tony Lelièvre
Using Witten Laplacians to locate index-1 saddle points. CoRR abs/2212.10135 (2022)
2010 – 2019
- 2019
- [j30]Chin Pang Ho
Empirical risk minimization: probabilistic complexity and stepsize strategy. Comput. Optim. Appl. 73(2): 387-410 (2019) - [j29]Juan S. Campos, Ruth Misener
A multilevel analysis of the Lasserre hierarchy. Eur. J. Oper. Res. 277(1): 32-41 (2019) - [j28]Vahan Hovhannisyan, Yannis Panagakis
Fast Multilevel Algorithms for Compressive Principal Component Pursuit. SIAM J. Imaging Sci. 12(1): 624-649 (2019) - [i5]Panos Parpas, Corey Muir:
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks. CoRR abs/1902.02542 (2019) - [i4]Nikolas Kantas, Panos Parpas, Grigorios A. Pavliotis:
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima? CoRR abs/1905.04121 (2019) - [i3]Conor McMeel, Panos Parpas:
Majorisation-minimisation algorithms for minimising the difference between lattice submodular functions. CoRR abs/1905.13492 (2019) - [i2]Batuhan Güler, Alexis Laignelet, Panos Parpas:
Towards Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations. CoRR abs/1910.11623 (2019) - 2018
- [j27]Panos Parpas
Special issue: Optimization models and algorithms for data science. Math. Program. 167(1): 1-3 (2018) - [j26]Juan S. Campos, Panos Parpas
A Multigrid Approach to SDP Relaxations of Sparse Polynomial Optimization Problems. SIAM J. Optim. 28(1): 1-29 (2018) - 2017
- [j25]Panos Parpas
Guest Editorial. Optim. Methods Softw. 32(4): 669 (2017) - [j24]Panos Parpas
A Multilevel Proximal Gradient Algorithm for a Class of Composite Optimization Problems. SIAM J. Sci. Comput. 39(5) (2017) - [c5]Vahan Hovhannisyan, Yannis Panagakis
Multilevel Approximate Robust Principal Component Analysis. ICCV Workshops 2017: 536-544 - 2016
- [j23]Duy V. N. Luong
A Weighted Mirror Descent Algorithm for Nonsmooth Convex Optimization Problem. J. Optim. Theory Appl. 170(3): 900-915 (2016) - [j22]Radu Baltean-Lugojan, Panos Parpas
Robust Numerical Calibration for Implied Volatility Expansion Models. SIAM J. Financial Math. 7(1): 917-946 (2016) - [j21]Vahan Hovhannisyan, Panos Parpas
MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization. SIAM J. Imaging Sci. 9(4): 1829-1857 (2016) - 2015
- [j20]Panos Parpas
Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach. INFORMS J. Comput. 27(2): 358-377 (2015) - [c4]Chin Pang Ho
On Using Spectral Graph Theory to Infer the Structure of Multiscale Markov Processes. SIAM Conf. on Control and its Applications 2015: 228-235 - [i1]Vahan Hovhannisyan, Panos Parpas, Stefanos Zafeiriou:
MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization. CoRR abs/1509.05715 (2015) - 2014
- [j19]Panos Parpas
Editorial to computational techniques in management science. Comput. Manag. Sci. 11(1-2): 3-4 (2014) - [j18]Panos Parpas
A stochastic multiscale model for electricity generation capacity expansion. Eur. J. Oper. Res. 232(2): 359-374 (2014) - [j17]Chin Pang Ho
Singularly Perturbed Markov Decision Processes: A Multiresolution Algorithm. SIAM J. Control. Optim. 52(6): 3854-3886 (2014) - 2013
- [j16]Panos Parpas
A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control. Autom. 49(6): 1663-1671 (2013) - [j15]Gabriela Tavares, Panos Parpas
On the information-based complexity of stochastic programming. Oper. Res. Lett. 41(6): 622-626 (2013) - [j14]Fook Wai Kong, Panos Parpas
Sum of Non-Concave Utilities Maximization for MIMO Interference Systems. IEEE Trans. Wirel. Commun. 12(4): 1744-1751 (2013) - [c3]Giuliano Casale
A Feasibility Study of Host-Level Contention Detection by Guest Virtual Machines. CloudCom (2) 2013: 152-157 - 2012
- [j13]Panos Parpas
Editorial. Comput. Manag. Sci. 9(3): 301-302 (2012) - [j12]Mort Webster, Nidhi Santen, Panos Parpas
An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty. Comput. Manag. Sci. 9(3): 339-362 (2012) - [j11]Kai Ye, Panos Parpas
Robust portfolio optimization: a conic programming approach. Comput. Optim. Appl. 52(2): 463-481 (2012) - [c2]Duy V. N. Luong, Panos Parpas
Solving MRF Minimization by Mirror Descent. ISVC (1) 2012: 587-598 - 2010
- [j10]Polyxeni-Margarita Kleniati, Panos Parpas
Partitioning procedure for polynomial optimization. J. Glob. Optim. 48(4): 549-567 (2010)
2000 – 2009
- 2009
- [j9]Panos Parpas
Mean and variance optimization of non-linear systems and worst-case analysis. Comput. Optim. Appl. 43(2): 235-259 (2009) - [j8]Dietmar Maringer
Global optimization of higher order moments in portfolio selection. J. Glob. Optim. 43(2-3): 219-230 (2009) - [j7]Panos Parpas
Global optimization of robust chance constrained problems. J. Glob. Optim. 43(2-3): 231-247 (2009) - [j6]Panos Parpas
Convergence analysis of a global optimization algorithm using stochastic differential equations. J. Glob. Optim. 45(1): 95-110 (2009) - [j5]Angelos Tsoukalas
A smoothing algorithm for finite min-max-min problems. Optim. Lett. 3(1): 49-62 (2009) - [r5]Panos Parpas
Decomposition Algorithms for the Solution of Multistage Mean-Variance Optimization Problems. Encyclopedia of Optimization 2009: 619-627 - [r4]Panos Parpas
Duality Gaps in Nonconvex Optimization. Encyclopedia of Optimization 2009: 802-805 - [r3]Panos Parpas
Global Optimization Algorithms for Financial Planning Problems. Encyclopedia of Optimization 2009: 1277-1282 - [r2]Panos Parpas
Laplace Method and Applications to Optimization Problems. Encyclopedia of Optimization 2009: 1818-1822 - [r1]Panos Parpas
Maximum Entropy and Game Theory. Encyclopedia of Optimization 2009: 1999-2004 - 2008
- [j4]Daniel Kuhn, Panos Parpas, Berç Rustem:
Bound-based decision rules in multistage stochastic programming. Kybernetika 44(2): 134-150 (2008) - [j3]Berç Rustem, Stanislav Zakovic, Panos Parpas
An interior point algorithm for continuous minimax: implementation and computation. Optim. Methods Softw. 23(6): 911-928 (2008) - 2007
- [j2]Panos Parpas
Computational Assessment of Nested Benders and Augmented Lagrangian Decomposition for Mean-Variance Multistage Stochastic Problems. INFORMS J. Comput. 19(2): 239-247 (2007) - 2006
- [j1]Panos Parpas
Linearly Constrained Global Optimization and Stochastic Differential Equations. J. Glob. Optim. 36(2): 191-217 (2006) - [c1]Panos Parpas
Global Optimization of the Scenario Generation and Portfolio Selection Problems. ICCSA (3) 2006: 908-917
Coauthor Index
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