.png){kind=icon}
返回
A separable augmented Lagrangian algorithm for optimal structural design
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-019-02363-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose an iterative separable augmented Lagrangian algorithm (SALA) for optimal structural design, with SALA being a subset of the alternating directions of multiplier method (ADMM)-type algorithms. Our algorithm solves a sequence of separable quadratic-like programs, able to capture reciprocal- and exponential-like behavior, which is desirable in structural optimization. A salient feature of the algorithm is that the primal and dual variable updates are all updated using closed-form expressions. Since algorithms in the ADMM class are known to be very sensitive to scaling, we propose a scaling method inspired by the well-known ALGENCAN algorithm. Comparative results for SALA, ALGENCAN, and the Galahad LSQP solver are presented for selected test problems. Finally, although we do not exploit this feature herein, the primal and dual updates are both embarrassingly parallel, which makes the algorithm suitable for implementation on massively parallel computational devices, including general purpose graphical processor units (GPGPUs).
Keyword:
Structural optimization
Separable augmented Lagrangian algorithm (SALA)
Alternating directions of multiplier method (ADMM)
Separable quadratic program (QP)
Scaling
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:
4
论文数:
4.9K
被引数:
1.7W
机构
引用论文
Comparison of drinking water mutagenicity with leaching of polycyclic aromatic hydrocarbons from water distribution pipes.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏
分享收藏
查看更多内容