Optimal experimental designs are probability measures with finite support enjoying an optimality property for the computation of least squares estimators. We present an algorithm for computing optimal designs on finite sets based on the long-time asymptotics of the gradient flow of the log-determinant of the so called information matrix. We prove the convergence of the proposed algorithm, and provide a sharp estimate on the rate its convergence. Numerical experiments are performed on few test cases using the new matlab package OptimalDesignComputation.
A Range-Deflated Stochastic Lanczos Quadrature Method for Large-Scale Log-Determinant Estimation
The University of Dodoma（多多马大学）；African Institute for Mathematical Sciences, Research and Innovation Centre（非洲数学科学研究所研究与创新中心）；Western Norway University of Applied Sciences（挪威西部应用科学大学）；University of Cape Town（开普敦大学）
Novel technique based on Léja Points Approximation for Log-determinant Estimation of Large matrices
The University of Dodoma（多多马大学）；African Institute for Mathematical Sciences, Research and Innovation Centre (AIMSRIC)（非洲数学科学研究所研究与创新中心）；Western Norway University of Applied Sciences（挪威西部应用科学大学）；University of Cape Town（开普敦大学）
Determinant Estimation under Memory Constraints and Neural Scaling Laws
Department of Statistics, University of California, Berkeley CA, USA（加州大学伯克利分校统计学系）；International Computer Science Institute, Berkeley CA, USA（国际计算机科学研究所）；Electronic Engineering, University of Melbourne, Australia（墨尔本大学电子工程系）；School of Mathematics（数学学院）；Statistics, University of Melbourne, Australia（统计学，墨尔本大学）；Physics, University of Queensland, Australia（物理，昆士兰大学）；Lawrence Berkeley National Laboratory, Berkeley CA, USA（劳伦斯伯克利国家实验室）
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）
