Question About Numerical Derivatives/Gradients: Why has no one yet implemented a gradient function in Julia that is similar to the gradient function in MATLAB and NumPy?

This page summarizes the projects mentioned and recommended in the original post on /r/Julia
Julia jacobians hessians calculus Derivatives

InfluxDB
Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
Our great sponsors

  • FiniteDiff.jl

    Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

    • There is a finite difference gradient in https://github.com/JuliaDiff/FiniteDiff.jl

  • ForwardDiff.jl

    Forward Mode Automatic Differentiation for Julia

    • In these discussions, which are the only ones I could find that are the most pertinent and similar to what I'm talking about, https://github.com/JuliaDiff/ForwardDiff.jl/issues/390 and https://discourse.julialang.org/t/differentiation-without-explicit-function-np-gradient/57784 , nobody suggested or answered FiniteDiff.jl's finite differencing gradient for getting the numerical derivatives/gradients of an array of values. The answer is either the diff() function or Interpolations.jl, which I already explained in the post why I would want an alternative to those two options to exist, without having to call NumPy's gradient function.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts