DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박진규 | - |
dc.contributor.author | Jung, Haewon | - |
dc.contributor.author | 정해원 | - |
dc.date.accessioned | 2024-07-25T19:30:13Z | - |
dc.date.available | 2024-07-25T19:30:13Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1044786&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320386 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2023.2,[iv, 33 p. :] | - |
dc.description.abstract | Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP. Even though ADMM shows promising results in solving various types of QP, its convergence speed is known to be highly dependent on the step-size parameter $\rho$. Due to the absence of a general rule for setting $\rho$, it is often tuned manually or heuristically. In this paper, we propose CA-ADMM (Context-aware Adaptive ADMM)) which learns to adaptively adjust \rho to accelerate ADMM. CA-ADMM extracts the spatio-temporal context, which captures the dependency of the primal and dual variables of QP and their temporal evolution during the ADMM iterations. CA-ADMM chooses $\rho$ based on the extracted context. Through extensive numerical experiments, we validated that CA-ADMM effectively generalizes to unseen QP problems with different sizes and classes (i.e., having different QP parameter structures). Furthermore, we verified that CA-ADMM could dynamically adjust $\rho$ considering the stage of the optimization process to accelerate the convergence speed further. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 2차 계획법▼a그래프 뉴럴 네트워크▼a강화학습 | - |
dc.subject | Quadratic programming▼aGraph nueral network▼aReinforcement learning | - |
dc.title | Learning context-aware adaptive solvers to accelerate convex quadratic programming | - |
dc.title.alternative | 2차 계획법 가속화를 위한 컨텍스트 인지 적응 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Park, Jinkyoo | - |
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