165 citations · 435 across the 12 of their papers we have counts for
12 papers
Two-timescale Extragradient for Finding Local Minimax Points
Jiseok Chae, Kyuwon Kim, Donghwan Kim
Minimax problems are notoriously challenging to optimize. However, we present that the two-timescale extragradient method can be a viable solution. By utilizing dynamical systems t…
HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks
Donghwan Kim, Jaiyoung Park, Jongmin Kim +2
Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offlo…
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems
Sucheol Lee, Donghwan Kim
Modern minimax problems, such as generative adversarial network and adversarial training, are often under a nonconvex-nonconcave setting, and developing an efficient method for suc…
Semi-Anchored Multi-Step Gradient Descent Ascent Method for Structured Nonconvex-Nonconcave Composite Minimax Problems
Sucheol Lee, Donghwan Kim
Minimax problems, such as generative adversarial network, adversarial training, and fair training, are widely solved by a multi-step gradient descent ascent (MGDA) method in practi…
Accelerated Proximal Point Method for Maximally Monotone Operators
Donghwan Kim
This paper proposes an accelerated proximal point method for maximally monotone operators. The proof is computer-assisted via the performance estimation problem approach. The proxi…
Optimizing the Efficiency of First-Order Methods for Decreasing the Gradient of Smooth Convex Functions
Donghwan Kim, Jeffrey A. Fessler
This paper optimizes the step coefficients of first-order methods for smooth convex minimization in terms of the worst-case convergence bound (i.e., efficiency) of the decrease in…
Adaptive Restart of the Optimized Gradient Method for Convex Optimization
Donghwan Kim, Jeffrey A. Fessler
First-order methods with momentum such as Nesterov's fast gradient method are very useful for convex optimization problems, but can exhibit undesirable oscillations yielding slow c…
Fast dual proximal gradient algorithms with rate for convex minimization
Donghwan Kim, Jeffrey A. Fessler
We consider minimizing the composite function that consists of a strongly convex function and a convex function. The fast dual proximal gradient (FDPG) method decreases the dual fu…
Another look at the fast iterative shrinkage/thresholding algorithm (FISTA)
Donghwan Kim, Jeffrey A. Fessler
This paper provides a new way of developing the fast iterative shrinkage/thresholding algorithm (FISTA) that is widely used for minimizing composite convex functions with a nonsmoo…
Generalizing the optimized gradient method for smooth convex minimization
Donghwan Kim, Jeffrey A. Fessler
This paper generalizes the optimized gradient method (OGM) that achieves the optimal worst-case cost function bound of first-order methods for smooth convex minimization. Specifica…
On the convergence analysis of the optimized gradient method
Donghwan Kim, Jeffrey A. Fessler
This paper considers the problem of unconstrained minimization of smooth convex functions having Lipschitz continuous gradients with known Lipschitz constant. We recently proposed…
Optimized first-order methods for smooth convex minimization
Donghwan Kim, Jeffrey A. Fessler
We introduce new optimized first-order methods for smooth unconstrained convex minimization. Drori and Teboulle recently described a numerical method for computing the -iteratio…