An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact
A new method called MAS-PNCG has been developed to improve the efficiency of Incremental Potential Contact simulations. This method addresses the high computational costs associated with traditional approaches by utilizing a Sparse-Input Woodbury update algorithm. Experimental results show that MAS-PNCG significantly outperforms existing solvers in terms of speed and efficiency.
- ▪Incremental Potential Contact simulations are essential for ensuring intersection-free results but are computationally expensive.
- ▪The MAS-PNCG method reduces maintenance costs and improves convergence in contact-rich scenarios.
- ▪Experiments indicate that MAS-PNCG outperforms state-of-the-art solvers by significant margins.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2604.19892 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
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Computer Science > Graphics arXiv:2604.19892 (cs) [Submitted on 21 Apr 2026] Title:An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact Authors:Yu Zhang, Xing Shen, Kemeng Huang, Wei Chen, Yin Yang, Taku Komura, Tiantian Liu, Xingang Pan View a PDF of the paper titled An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact, by Yu Zhang and 7 other authors View PDF HTML (experimental) Abstract:Incremental Potential Contact (IPC) guarantees intersection-free simulation but suffers from high computational costs due to the expensive Hessian assembly and linear solves required by Newton's method.
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