.png){kind=icon}
返回
Efficient Quadratic Penalization Through the Partial Minimization Technique
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2017.2754474.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Common computational problems, such as parameter estimation in dynamic models and partial differential equation (PDE)-constrained optimization, require data fitting over a set of auxiliary parameters subject to physical constraints over an underlying state. Naive quadratically penalized formulations, commonly used in practice, suffer from inherent ill-conditioning. We show that surprisingly the partial minimization technique regularizes the problem, making it well-conditioned. This viewpoint sheds newlight on variable projection techniques, as well as the penalty method for PDE-constrained optimization, and motivates robust extensions. In addition, we outline an inexact analysis, showing that the partial minimization subproblem can be solved very loosely in each iteration. We illustrate the theory and algorithms on boundary control, optimal transport, and parameter estimation for robust dynamic inference.
Keyword:
Kalman smoothing
Nonconvex optimization
PDE constrained optimization
variable projection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
Postnatal colonization with human "infant-type" Bifidobacterium species alters behavior of adult gnotobiotic mice.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Immunocytochemical evidence for the gabaergic innervation of the stretch receptor neurons in crayfishNeuroscience
NeuroscienceIF0
PREAI
分享收藏分享收藏
查看更多内容