Jongho Park (박종호, 朴鐘浩)

Research Assistant Professor

Natural Science Research Institute

KAIST, Korea

E-mail: jongho.park (at) kaist.ac.kr

Research Interests

Computational mathematics, Numerical analysis, Domain decomposition methods, Convex optimization, Mathematical imaging, Finite element methods

Publications

Submitted papers

  1. Jongho Park. Fast gradient methods for uniformly convex and weakly smooth problems (2021) arXiv

  2. Chang-Ock Lee, Youngkyu Lee, and Jongho Park. A parareal architecture for very deep neural network (2021)

  3. Jongho Park. Additive Schwarz methods for convex optimization---convergence theory and acceleration (2021)

  4. Chang-Ock Lee, Youngkyu Lee, and Jongho Park. Parareal neural networks emulating a parallel-in-time algorithm (2021) arXiv

  5. Jongho Park. Accelerated additive Schwarz methods for convex optimization with adaptive restart (2020) arXiv

  6. Chang-Ock Lee and Jongho Park. A FETI-DP method for nonlinear variational inequalities utilizing linear local problems (2020)

  7. Chaemin Lee and Jongho Park. A variational framework for the strain-smoothed element method (2020) arXiv

  8. Hohyun Jung, Jongho Park, and Frederick Kin Hing Phoa. Measuring inter- and intra-individual variabilities in the state-space model (2020)

Published papers

  1. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. Corrigendum to "A dual iterative substructuring method with a small penalty parameter". To appear in Journal of the Korean Mathematical Society. link arXiv

  2. Jongho Park. Pseudo-linear convergence of an additive Schwarz method for dual total variation minimization. Electronic Transactions on Numerical Analysis, 54, 176--197 (2021) link arXiv

  3. Chang-Ock Lee and Jongho Park. Recent advances in domain decomposition methods for total variation minimization. Journal of the Korean Society for Industrial and Applied Mathematics, 24(2), 161--197 (2020) link

  4. Jongho Park. An overlapping domain decomposition framework without dual formulation for variational imaging problems. Advances in Computational Mathematics, 46(4), 57 (2020) link arXiv

  5. Jongho Park. Additive Schwarz methods for convex optimization as gradient methods. SIAM Journal on Numerical Analysis, 58(3), 1495--1530 (2020) link arXiv

  6. Chang-Ock Lee and Jongho Park. Fast nonoverlapping block Jacobi method for the dual Rudin--Osher--Fatemi model. SIAM Journal on Imaging Sciences, 12(4), 2009--2034 (2019) link arXiv

  7. Chang-Ock Lee and Jongho Park. A finite element nonoverlapping domain decomposition method with Lagrange multipliers for the dual total variation minimizations. Journal of Scientific Computing, 81(3), 2331--2355 (2019) link arXiv

  8. Chang-Ock Lee, Eun-Hee Park, and Jongho Park. A finite element approach for the dual Rudin--Osher--Fatemi model and its nonoverlapping domain decomposition methods. SIAM Journal on Scientific Computing, 41(2), B205--B228 (2019) link arXiv

  9. Chang-Ock Lee, Changmin Nam, and Jongho Park. Domain decomposition methods using dual conversion for the total variation minimization with $L^1$ fidelity term. Journal of Scientific Computing, 78(2), 951--970 (2019) link

Slides

  1. Additive Schwarz methods for convex optimization. Seminar in Department of Applied Mathematics, Kyung Hee University, November 26, 2020. slide

  2. Domain decomposition methods for convex optimization in image processing: focusing on total variation minimization. Ph.D. Defense, Department of Mathematical Sciences, KAIST, May 31, 2019. slide