Automatic Differentiation: Theory and Practice

07/13/2022
by   Mario Lezcano Casado, et al.
0

We present the classical coordinate-free formalism for forward and backward mode ad in the real and complex setting. We show how to formally derive the forward and backward formulae for a number of matrix functions starting from basic principles.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/22/2022

You Only Linearize Once: Tangents Transpose to Gradients

Automatic differentiation (AD) is conventionally understood as a family ...
research
11/09/2020

Derivatives of partial eigendecomposition of a real symmetric matrix for degenerate cases

This paper presents the forward and backward derivatives of partial eige...
research
07/26/2016

Forward-Mode Automatic Differentiation in Julia

We present ForwardDiff, a Julia package for forward-mode automatic diffe...
research
12/06/2018

Joint Target Detection, Tracking and Classification with Forward-Backward PHD Smoothing

Forward-backward Probability Hypothesis Density (PHD) smoothing is an ef...
research
10/07/2022

A δf PIC method with auxiliary Forward-Backward Lagrangian reconstructions

In this note we describe a δ f particle method where the bulk density is...
research
06/01/2022

Nonsmooth automatic differentiation: a cheap gradient principle and other complexity results

We provide a simple model to estimate the computational costs of the bac...
research
03/08/2022

Introduction to Automatic Backward Filtering Forward Guiding

In this document I aim to give an informal treatment of automatic Backwa...

Please sign up or login with your details

Forgot password? Click here to reset