Pseudo-linear Convergence of an Additive Schwarz Method for Dual Total Variation Minimization. - Jongho Park 002 (CoRR, 2019)

Domain Decomposition Methods Using Dual Conversion for the Total Variation Minimization with $$L^1$$ L 1 Fidelity Term. - Chang-Ock Lee; Changmin Nam; Jongho Park 002 (J. Sci. Comput., 2019)

A Finite Element Nonoverlapping Domain Decomposition Method with Lagrange Multipliers for the Dual Total Variation Minimizations. - Chang-Ock Lee; Jongho Park 002 (J. Sci. Comput., 2019)

Fast Nonoverlapping Block Jacobi Method for the Dual Rudin-Osher-Fatemi Model. - Chang-Ock Lee; Jongho Park 002 (SIAM J. Imaging Sci., 2019)

A Finite Element Approach for the Dual Rudin-Osher-Fatemi Model and Its Nonoverlapping Domain Decomposition Methods. - Chang-Ock Lee; Eun-Hee Park 003; Jongho Park 002 (SIAM J. Sci. Comput., 2019)

An Overlapping Domain Decomposition Framework without Dual Formulation for Variational Imaging Problems. - Jongho Park 002 (CoRR, 2020)

Correction to: A dual iterative substructuring method with a small penalty parameter. - Chang-Ock Lee; Eun-Hee Park 003; Jongho Park 002 (CoRR, 2020)

Additive Schwarz Methods for Convex Optimization as Gradient Methods. - Jongho Park 002 (SIAM J. Numer. Anal., 2020)

A variational framework for the strain-smoothed element method. - Chaemin Lee; Jongho Park 002 (Comput. Math. Appl., 2021)

Accelerated Additive Schwarz Methods for Convex Optimization with Adaptive Restart. - Jongho Park 002 (J. Sci. Comput., 2021)

Fast gradient methods for uniformly convex and weakly smooth problems. - Jongho Park 002 (Adv. Comput. Math., 2022)

Additive Schwarz methods for convex optimization with backtracking. - Jongho Park 002 (Comput. Math. Appl., 2022)

A neuron-wise subspace correction method for the finite neuron method. - Jongho Park 002; Jinchao Xu; Xiaofeng Xu (CoRR, 2022)

Two-level group convolution. - Youngkyu Lee; Jongho Park 002; Chang-Ock Lee (Neural Networks, 2022)

Preconditioning for finite element methods with strain smoothing. - Chaemin Lee; Jongho Park 002 (Comput. Math. Appl., 2023)

A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators. - Minwoo Lee; Kyu-Tae Kim; Jongho Park 002 (CoRR, 2023)

DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime. - Jongho Park 002; Jinchao Xu (CoRR, 2023)

Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimates. - Youngkyu Lee; Jongho Park 002; Chang-Ock Lee (CoRR, 2023)

An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise. - Minwoo Lee; Jongho Park 002 (SIAM J. Appl. Dyn. Syst., 2023)

Two-level overlapping Schwarz preconditioners with universal coarse spaces for 2mth-order elliptic problems. - Jongho Park 002 (CoRR, 2024)

Randomized greedy algorithms for neural network optimization. - Jongho Park 002; Jinchao Xu; Xiaofeng Xu (CoRR, 2024)

Additive Schwarz Methods for Semilinear Elliptic Problems with Convex Energy Functionals: Convergence Rate Independent of Nonlinearity. - Jongho Park 002 (SIAM J. Sci. Comput., 2024)

Two-Level Overlapping Schwarz Preconditioners with Universal Coarse Spaces for ( boldsymbol{2m} )th-Order Elliptic Problems. - Jongho Park 002 (SIAM J. Sci. Comput., 2024)

Parareal Neural Networks Emulating a Parallel-in-Time Algorithm. - Youngkyu Lee; Jongho Park 002; Chang-Ock Lee (IEEE Trans. Neural Networks Learn. Syst., 2024)

Additive Schwarz Methods for Fourth-Order Variational Inequalities. - Jongho Park 002 (J. Sci. Comput., 2024)