<--- Back to Details
First PageDocument Content
Mathematical analysis / Numerical analysis / Mathematics / Numerical linear algebra / Root-finding algorithms / Iterative refinement / Residual / Newton's method / Pi / Approximations of / Gradient descent / Mathematical optimization
Date: 2010-07-21 10:31:16
Mathematical analysis
Numerical analysis
Mathematics
Numerical linear algebra
Root-finding algorithms
Iterative refinement
Residual
Newton's method
Pi
Approximations of
Gradient descent
Mathematical optimization

What is iterative refinement How to use iterative refinement to verify a computed result? Influence of the computing precision Conclusion and future work Iterative refinement:

Add to Reading List

Source URL: www.lix.polytechnique.fr

Download Document from Source Website

File Size: 545,47 KB

Share Document on Facebook

Similar Documents

Hybrid Approximate Gradient and Stochastic Descent for Falsification of Nonlinear Systems Shakiba Yaghoubi and Georgios Fainekos Abstract— Studying transient properties of nonlinear systems is an important problem for

Hybrid Approximate Gradient and Stochastic Descent for Falsification of Nonlinear Systems Shakiba Yaghoubi and Georgios Fainekos Abstract— Studying transient properties of nonlinear systems is an important problem for

DocID: 1xUAR - View Document

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent Christopher De Sa Matthew Feldman Christopher Ré Kunle Olukotun Departments of Electrical Engineering and Computer Science Stanford Uni

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent Christopher De Sa Matthew Feldman Christopher Ré Kunle Olukotun Departments of Electrical Engineering and Computer Science Stanford Uni

DocID: 1vj6v - View Document

Distributed Gradient Descent in Bacterial Food Search Shashank Singh∗1 , Sabrina Rashid∗2 , Saket Navlakha3 , Ziv Bar-Joseph4† 1 Machine Learning Department and Department of Statistics, Carnegie Mellon University,

Distributed Gradient Descent in Bacterial Food Search Shashank Singh∗1 , Sabrina Rashid∗2 , Saket Navlakha3 , Ziv Bar-Joseph4† 1 Machine Learning Department and Department of Statistics, Carnegie Mellon University,

DocID: 1vhcx - View Document

Stat 928: Statistical Learning Theory  Lecture: 22 Exponentiated Gradient Descent Instructor: Sham Kakade

Stat 928: Statistical Learning Theory Lecture: 22 Exponentiated Gradient Descent Instructor: Sham Kakade

DocID: 1vbLp - View Document

Pattern Recognition And Machine Learning - EPFL - Fall 2015 Emtiyaz Khan, Timur Bagautdinov, Carlos Becker, Ilija Bogunovic & Ksenia Konyushkova 2. Linear Regression and Gradient Descent 2.1

Pattern Recognition And Machine Learning - EPFL - Fall 2015 Emtiyaz Khan, Timur Bagautdinov, Carlos Becker, Ilija Bogunovic & Ksenia Konyushkova 2. Linear Regression and Gradient Descent 2.1

DocID: 1v534 - View Document