Inexact descent methods for elastic parameter optimization

Guowei Yan, Wei Li, Ruigang Yang, Huamin Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Elastic parameter optimization has revealed its importance in 3D modeling, virtual reality, and additive manufacturing in recent years. Unfortunately, it is known to be computationally expensive, especially if there are many parameters and data samples. To address this challenge, we propose to introduce the inexactness into descent methods, by iteratively solving a forward simulation step and a parameter update step in an inexact manner. The development of such inexact descent methods is centered at two questions: 1) how accurate/inaccurate can the two steps be; and 2) what is the optimal way to implement an inexact descent method. The answers to these questions are in our convergence analysis, which proves the existence of relative error thresholds for the two inexact steps to ensure the convergence. This means we can simply solve each step by a fixed number of iterations, if the iterative solver is at least linearly convergent. While the use of the inexact idea speeds up many descent methods, we specifically favor a GPU-based one powered by state-of-the-art simulation techniques. Based on this method, we study a variety of implementation issues, including backtracking line search, initialization, regularization, and multiple data samples. We demonstrate the use of our inexact method in elasticity measurement and design applications. Our experiment shows the method is fast, reliable, memory-efficient, GPUfriendly, flexible with different elastic models, scalable to a large parameter space, and parallelizable for multiple data samples.

Original languageEnglish
Article number253
JournalACM Transactions on Graphics
Volume37
Issue number6
DOIs
StatePublished - Nov 2018

Bibliographical note

Funding Information:
This work was funded by NSF grant CHS-1524992 and NSFC grant 61332017. The authors would also like to thank Adobe and NVIDIA for additional equipment and funding supports.

Publisher Copyright:
© 2018 Copyright held by the owner/author(s).

Keywords

  • Constrained optimization
  • Heterogeneity
  • Inexact method
  • Nonlinear elasticity
  • Quasistatic simulation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Fingerprint

Dive into the research topics of 'Inexact descent methods for elastic parameter optimization'. Together they form a unique fingerprint.

Cite this