pmax_grad_y

numeric vector or array, value of <code>x</code>.

numeric vector or array, value of <code>y</code>.

numeric vector or array, value of <code>pmax(x, y)</code>.

numeric vector or array, gradient of <code>y</code>.

grad

Calculate the gradient of <code>pmax(x, y)</code> with respect to <code>y</code>.

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.

pmax_grad_y: Parallel Maxima Gradient

Description

Arguments

Value