Randomized Diagonal Estimation in Julia
Offered By: The Julia Programming Language via YouTube
Course Description
Overview
Explore a comprehensive toolkit for implicit diagonal estimation in this 11-minute JuliaCon 2024 talk by Niclas Popp. Dive into RandomizedDiagonalEstimation.jl, which offers established algorithms for approximating the diagonal of large matrices using only matrix-vector products. Learn about applications in network science, material science, and machine learning. Discover the Girard-Hutchinson estimator and Diag++ method, as well as novel extensions for estimating the diagonal of matrix functions like exponentials and inverses. Understand how the package integrates with the Julia ecosystem and allows for easy addition of future algorithms. Gain insights into this powerful tool for tackling problems in various scientific fields through efficient diagonal estimation techniques.
Syllabus
Randomized Diagonal Estimation in Julia | Popp | JuliaCon 2024
Taught by
The Julia Programming Language
Related Courses
Julia Scientific ProgrammingUniversity of Cape Town via Coursera Julia for Beginners in Data Science
Coursera Project Network via Coursera Linear Regression and Multiple Linear Regression in Julia
Coursera Project Network via Coursera Decision Tree and Random Forest Classification using Julia
Coursera Project Network via Coursera Logistic Regression for Classification using Julia
Coursera Project Network via Coursera