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Anderson acceleration for geometry optimization and physics simulation

Peng, Yue, Deng, Bailin, Zhang, Juyong, Geng, Fanyu, Qin, Wenjie and Liu, Ligang 2018. Anderson acceleration for geometry optimization and physics simulation. ACM Transactions on Graphics 37 (4) , 42. 10.1145/3197517.3201290

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Abstract

Many computer graphics problems require computing geometric shapes subject to certain constraints. This often results in non-linear and non-convex optimization problems with globally coupled variables, which pose great challenge for interactive applications. Local-global solvers developed in recent years can quickly compute an approximate solution to such problems, making them an attractive choice for applications that prioritize efficiency over accuracy. However, these solvers suffer from lower convergence rate, and may take a long time to compute an accurate result. In this paper, we propose a simple and effective technique to accelerate the convergence of such solvers. By treating each local-global step as a fixed-point iteration, we apply Anderson acceleration, a well-established technique for fixed-point solvers, to speed up the convergence of a local-global solver. To address the stability issue of classical Anderson acceleration, we propose a simple strategy to guarantee the decrease of target energy and ensure its global convergence. In addition, we analyze the connection between Anderson acceleration and quasi-Newton methods, and show that the canonical choice of its mixing parameter is suitable for accelerating local-global solvers. Moreover, our technique is effective beyond classical local-global solvers, and can be applied to iterative methods with a common structure. We evaluate the performance of our technique on a variety of geometry optimization and physics simulation problems. Our approach significantly reduces the number of iterations required to compute an accurate result, with only a slight increase of computational cost per iteration. Its simplicity and effectiveness makes it a promising tool for accelerating existing algorithms as well as designing efficient new algorithms.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computing Machinery (ACM)
ISSN: 0730-0301
Date of First Compliant Deposit: 11 May 2018
Date of Acceptance: 15 May 2018
Last Modified: 01 Jul 2019 03:26
URI: http://orca.cf.ac.uk/id/eprint/111400

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