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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

Mathematical programming, 2014-04, Vol.144 (1-2), p.1-38

2014

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Parallel coordinate descent methods for big data optimization

Mathematical programming, 2016-03, Vol.156 (1-2), p.433-484

2016

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Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

SIAM journal on matrix analysis and applications, 2020-01, Vol.41 (2), p.487-524

2020

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Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

IEEE journal of selected topics in signal processing, 2016-03, Vol.10 (2), p.242-255

2016

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Distributed optimization with arbitrary local solvers

Optimization methods & software, 2017-07, Vol.32 (4), p.813-848

2017

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Applying deep learning to the newsvendor problem

IIE transactions, 2020-04, Vol.52 (4), p.444-463

2020

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A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization

Manufacturing & service operations management, 2022-01, Vol.24 (1), p.285-304

2022

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Random-reshuffled SARAH does not need full gradient computations

Optimization letters, 2024-04, Vol.18 (3), p.727-749

2024

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Randomized sketch descent methods for non-separable linearly constrained optimization

IMA journal of numerical analysis, 2021-04, Vol.41 (2), p.1056-1092

2021

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On optimal probabilities in stochastic coordinate descent methods

Optimization letters, 2016-08, Vol.10 (6), p.1233-1243

2016

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A robust multi-batch L-BFGS method for machine learning

Optimization methods & software, 2020-01, Vol.35 (1), p.191-219

2020

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DynNet: Physics-based neural architecture design for nonlinear structural response modeling and prediction

Engineering structures, 2021-02, Vol.229, p.111582, Article 111582

2021

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Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment

Computer-aided civil and infrastructure engineering, 2020-12, Vol.35 (12), p.1349-1364

2020

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Inexact SARAH algorithm for stochastic optimization

Optimization methods & software, 2021-01, Vol.36 (1), p.237-258

2021

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A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow

Optimization methods & software, 2017-07, Vol.32 (4), p.849-871

2017

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Matrix completion under interval uncertainty

European journal of operational research, 2017-01, Vol.256 (1), p.35-43

2017

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Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands

IEEE access, 2023, Vol.11, p.87958-87969

2023

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Stochastic Gradient Methods with Preconditioned Updates

Journal of optimization theory and applications, 2024-05, Vol.201 (2), p.471-489

2024

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Inexact tensor methods and their application to stochastic convex optimization

Optimization methods & software, 2024-01, Vol.39 (1), p.42-83

2024

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Uncertainty quantification in digital image correlation for experimental evaluation of deep learning based damage diagnostic

Structure and infrastructure engineering, 2021-10, Vol.17 (11), p.1459-1473

2021

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Preconditioning meets biased compression for efficient distributed optimization

Computational management science, 2024-06, Vol.21 (1), p.14, Article 14

2024

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On the complexity of parallel coordinate descent

Optimization methods & software, 2018-03, Vol.33 (2), p.372-395

2018

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Hybrid Methods in Solving Alternating-Current Optimal Power Flows

IEEE transactions on smart grid, 2017-11, Vol.8 (6), p.2988-2998

2017

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A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

JPhys Energy, 2023-01, Vol.5 (1), p.14009

2023

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Cooperation Via Intimidation: An Emergent System of Mutual Threats can Maintain Social Order

Journal of artificial societies and social simulation, 2017-10, Vol.20 (4)

2017

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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

Parallel coordinate descent methods for big data optimization

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

Distributed optimization with arbitrary local solvers

Applying deep learning to the newsvendor problem

A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization

Random-reshuffled SARAH does not need full gradient computations

Randomized sketch descent methods for non-separable linearly constrained optimization

On optimal probabilities in stochastic coordinate descent methods

A robust multi-batch L-BFGS method for machine learning

DynNet: Physics-based neural architecture design for nonlinear structural response modeling and prediction

Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment

Inexact SARAH algorithm for stochastic optimization

A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow

Matrix completion under interval uncertainty

Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands

Stochastic Gradient Methods with Preconditioned Updates

Inexact tensor methods and their application to stochastic convex optimization

Uncertainty quantification in digital image correlation for experimental evaluation of deep learning based damage diagnostic

Preconditioning meets biased compression for efficient distributed optimization

On the complexity of parallel coordinate descent

Hybrid Methods in Solving Alternating-Current Optimal Power Flows

A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

Cooperation Via Intimidation: An Emergent System of Mutual Threats can Maintain Social Order

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