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The design of JuMP and MathProgBase (Miles Lubin, MIT)
What is Automatic Differentiation
Jarrett Revels: Forward-Mode Automatic Differentiation in Julia
Automatic Differentiation
Automatic Differentiation for Solid Mechanics in Julia | Andrea Vigliotti | JuliaCon 2022
Automatic Differentiation for Quantum Electron... | M Towara, N Schmitz, G Kemlin | JuliaCon 2022
Automatic differentiation and machine learning
The State of JuMP | Miles Lubin | JuliaCon 2023
Barak A. Pearlmutter – Automatic Differentiation: History and Headroom
JuMP-dev 2018 | MathOptInterface and JuMP 0.19 | Miles Lubin
Leveraging Sparsity to Accelerate Automatic Differentiation | Hill | Paris 2025
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Last Updated: October 27, 2026
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Summary
Visit julialang.org/ to download Julia. Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the JuliaOpt site at juliaopt.org and the meetup schedule at juliaopt.org/developersmeetup. This short tutorial covers the basics of ... approximation a very bad approximation the solution is to DFTK.jl is a framework for the quantum-chemical simulation of materials using Density Functional Theory. Many relevant physical ... Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. For more info on the Julia Programming Language, us on Twitter: twitter.com/JuliaLanguage and consider ... "Leveraging Sparsity to Accelerate
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