no code implementations • 29 Nov 2023 • Seonho Park, Pascal Van Hentenryck
This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds.
no code implementations • 21 Jan 2023 • Seonho Park, Wenbo Chen, Terrence W. K. Mak, Pascal Van Hentenryck
This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA).
no code implementations • 28 Nov 2022 • Seonho Park, Wenbo Chen, Dahye Han, Mathieu Tanneau, Pascal Van Hentenryck
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors.
no code implementations • 18 Aug 2022 • Seonho Park, Pascal Van Hentenryck
This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference.
no code implementations • 2 Apr 2022 • Neil Barry, Minas Chatzos, Wenbo Chen, Dahye Han, Chaofan Huang, Roshan Joseph, Michael Klamkin, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck, Shangkun Wang, Hanyu Zhang, Haoruo Zhao
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification Vocal Bursts Intensity Prediction
no code implementations • 27 Dec 2021 • Wenbo Chen, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck
Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i. e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions.
no code implementations • 21 Sep 2021 • Seonho Park, Maciej Rysz, Kathleen M. Dipple, Panos M. Pardalos
Deep learning-based image retrieval has been emphasized in computer vision.
no code implementations • 14 Apr 2021 • Seonho Park, Panos M. Pardalos
Estimating the data density is one of the challenging problems in deep learning.
1 code implementation • 5 May 2020 • Seonho Park, George Adosoglou, Panos M. Pardalos
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable.
1 code implementation • 27 Jun 2019 • Seonho Park, Seung Hyun Jung, Panos M. Pardalos
In this paper, we suggest an algorithm combining negative curvature with the adaptive cubic regularized Newton method to update even at unsuccessful iterations.