This short note considers an efficient variant of the trust-region algorithm with dynamic accuracy proposed Carter (1993) and Conn, Gould and Toint (2000) as a tool for very high-performance computing, an area where it is critical to allow multi-precision computations for keeping the energy dissipation under control. Numerical experiments are presented indicating that the use of the considered method can bring substantial savings in objective function's and gradient's evaluation "energy costs" by efficiently exploiting multi-precision computations.
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Massachusetts Institute of Technology（麻省理工学院）；NSF AI Institute for Artificial Intelligence and Fundamental Interactions（NSF人工智能与基础相互作用AI研究院）；Harvard University（哈佛大学）
Mixed-precision algorithms for solving the Sylvester matrix equation
Chalmers University of Technology and University of Gothenburg（查尔姆斯理工大学和哥德堡大学）；University of Leeds（利兹大学）；University of Manchester（曼彻斯特大学）；Max Planck Institute for Dynamics of Complex Technical Systems（马普复杂技术系统动力学研究所）
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
College of Computing, Mohammed VI Polytechnic University（穆罕默德六世理工大学计算机学院）；University of Central Florida（中佛罗里达大学）；University of California, Irvine（加州大学欧文分校）
