18 citations · 149 across the 24 of their papers we have counts for
6 papers · 1 filter
Additive Schwarz Methods for Convex Optimization with Backtracking
Jongho Park
This paper presents a novel backtracking strategy for additive Schwarz methods for general convex optimization problems as an acceleration scheme. The proposed backtracking strateg…
Two-level Group Convolution
Youngkyu Lee, Jongho Park, Chang-Ock Lee
Group convolution has been widely used in order to reduce the computation time of convolution, which takes most of the training time of convolutional neural networks. However, it i…
Preconditioning for finite element methods with strain smoothing
Chaemin Lee, Jongho Park
Strain smoothing methods such as the smoothed finite element methods (S-FEMs) and the strain-smoothed element~(SSE) method have successfully improved the performance of finite elem…
An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise
Minwoo Lee, Jongho Park
Dynamic mode decomposition (DMD) is an efficient tool for decomposing spatio-temporal data into a set of low-dimensional modes, yielding the oscillation frequencies and the growth…
Parareal Neural Networks Emulating a Parallel-in-time Algorithm
Chang-Ock Lee, Youngkyu Lee, Jongho Park
As deep neural networks (DNNs) become deeper, the training time increases. In this perspective, multi-GPU parallel computing has become a key tool in accelerating the training of D…
Fast Gradient Methods for Uniformly Convex and Weakly Smooth Problems
Jongho Park
In this paper, acceleration of gradient methods for convex optimization problems with weak levels of convexity and smoothness is considered. Starting from the universal fast gradie…