143 citations · 429 across the 41 of their papers we have counts for
42 papers
Revisiting Simultaneous Methods for Dynamic Optimization in the GPU Era
Joseph W. Choi, Sungho Shin
We revisit the classical topic in dynamic optimization: sequential vs simultaneous methods for solving DAE-constrained optimization problems, with a particular focus on how graphic…
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
Sungho Shin, François Pacaud, Mihai Anitescu
This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkabl…
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement
Seunghyeok Back, Sangbeom Lee, Kangmin Kim +4
Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims t…
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
Sungho Shin, Yeonguk Yu, Kyoobin Lee
In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR…
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
Alexander Engelmann, Sungho Shin, François Pacaud +1
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of i…
Block Selection Method for Using Feature Norm in Out-of-distribution Detection
Yeonguk Yu, Sungho Shin, Seongju Lee +2
Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of…
Near-Optimal Performance of Stochastic Model Predictive Control
Sungho Shin, Sen Na, Mihai Anitescu
This article presents a dynamic regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative un…
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Sungho Shin, Joosoon Lee, Junseok Lee +2
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention si…
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
David Cole, Sungho Shin, François Pacaud +2
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method red…
A Julia Framework for Graph-Structured Nonlinear Optimization
David L Cole, Sungho Shin, Victor Zavala
Graph theory provides a convenient framework for modeling and solving structured optimization problems. Under this framework, the modeler can arrange/assemble the components of an…
Condensed interior-point methods: porting reduced-space approaches on GPU hardware
François Pacaud, Sungho Shin, Michel Schanen +2
The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the…
A Feasible Reduced Space Method for Real-Time Optimal Power Flow
François Pacaud, Daniel Adrian Maldonado, Sungho Shin +2
We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with sec…
Controllability and Observability Imply Exponential Decay of Sensitivity in Dynamic Optimization
Sungho Shin, Victor M. Zavala
We study a property of dynamic optimization (DO) problems (as those encountered in model predictive control and moving horizon estimation) that is known as exponential decay of sen…
Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
Sungho Shin, Mihai Anitescu, Victor M. Zavala
We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs. These NLPs arise in many applications such as dynamic optimization, stochastic o…
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Paul F. Lang, Sungho Shin, Victor M. Zavala
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia pr…
Graph-Based Modeling and Decomposition of Energy Infrastructures
Sungho Shin, Carleton Coffrin, Kaarthik Sundar +1
Nonlinear optimization problems are found at the heart of real-time operations of critical infrastructures. These problems are computationally challenging because they embed comple…
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
Yoonho Boo, Sungho Shin, Jungwook Choi +1
The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve t…
Multiple Classification with Split Learning
Jongwon Kim, Sungho Shin, Yeonguk Yu +2
Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep le…
S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima
Wonyong Sung, Iksoo Choi, Jinhwan Park +2
The stochastic gradient descent (SGD) method is most widely used for deep neural network (DNN) training. However, the method does not always converge to a flat minimum of the loss…
A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl
Jordan Jalving, Sungho Shin, Victor M. Zavala
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical hype…
Quantized Neural Networks: Characterization and Holistic Optimization
Yoonho Boo, Sungho Shin, Wonyong Sung
Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications. Previous studies mostly focused on developing optimization methods f…
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
Sen Na, Sungho Shin, Mihai Anitescu +1
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domai…
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Qiugang Lu, Sungho Shin, Victor M. Zavala
Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates…
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
Sungho Shin, Qiugang Lu, Victor M. Zavala
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems. We observe that SID seeks to solve an…