approx_grad

character scalar, name of the node.

numeric scalar, index of the target node that is differentiated. Defaults to the first element.

index

numeric scalar, step size. Defaults to 1e-4.

Differentiate node <code>x</code> with respect to node <code>y</code> by numerical differentiation.

internal

Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The 'cgraph' package supports various operations including basic arithmetic, trigonometry operations, and linear algebra operations. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning.

approx_grad: Approximate Gradients

Description

Usage

Arguments

Value