WRAPD

Author:

Brown George E.1,Narain Rahul2

Affiliation:

1. University of Minnesota

2. Indian Institute of Technology Delhi

Abstract

Local-global solvers such as ADMM for elastic simulation and geometry optimization struggle to resolve large rotations such as bending and twisting modes, and large distortions in the presence of barrier energies. We propose two improvements to address these challenges. First, we introduce a novel local-global splitting based on the polar decomposition that separates the geometric nonlinearity of rotations from the material nonlinearity of the deformation energy. The resulting ADMM-based algorithm is a combination of an L-BFGS solve in the global step and proximal updates of element stretches in the local step. We also introduce a novel method for dynamic reweighting that is used to adjust element weights at runtime for improved convergence. With both improved rotation handling and element weighting, our algorithm is considerably faster than state-of-the-art approaches for quasi-static simulations. It is also much faster at making early progress in parameterization problems, making it valuable as an initializer to jump-start second-order algorithms.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Reference33 articles.

1. A Recursive Algebraic Coloring Technique for Hardware-efficient Symmetric Sparse Matrix-vector Multiplication

2. Preconditioned ADMM with Nonlinear Operator Constraint

3. Large-scale Sparse Inverse Covariance Matrix Estimation

4. Matthias Bollhöfer Olaf Schenk Radim Janalik Steve Hamm and Kiran Gullapalli. 2020. State-of-the-Art Sparse Direct Solvers. (2020) 3--33. 10.1007/978-3-030-43736-7__1 Matthias Bollhöfer Olaf Schenk Radim Janalik Steve Hamm and Kiran Gullapalli. 2020. State-of-the-Art Sparse Direct Solvers. (2020) 3--33. 10.1007/978-3-030-43736-7__1

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