JuliaNLSolvers/Optim.jl

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1207 stars · 239 forks observed · 2026-10-19

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Optim.jl is a pure-Julia library for univariate and multivariate function optimization, focused on unconstrained local minimization with algorithms like BFGS, L-BFGS, Nelder-Mead, and gradient descent. It also offers some box-constrained and Riemannian support plus global methods such as simulated annealing and particle swarm.

Use cases

• minimize a multivariate objective function in Julia
• fit model parameters by minimizing a loss function
• find local minima of a differentiable function with BFGS or L-BFGS
• optimize a function without gradients using Nelder-Mead
• run global optimization with simulated annealing or particle swarm
• replace C/Fortran optimizers with a pure Julia MIT-licensed solver

When to choose

• you need unconstrained or lightly constrained optimization in Julia with no external binary dependencies
• you want solvers that leverage Julia's multiple dispatch for custom preconditioners and line searches
• you need an MIT-licensed optimizer easily added via Pkg.add

When to avoid

• you need large-scale linear or mixed-integer programming
• you require dedicated global optimization with guarantees (consider BlackBoxOptim)
• you need heavily constrained optimization beyond box constraints

Facets

library · maturity stable

math mathematics machine-learning optimization unconstrained-optimization bfgs gradient-descent local-minimization global-optimization numerical-optimization algorithms julia

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