# JuliaNLSolvers/Optim.jl

Optimization functions for Julia

Repository: https://github.com/JuliaNLSolvers/Optim.jl
Canonical: https://ross.abutalabs.com/products/optimjl
Homepage: https://julianlsolvers.github.io/Optim.jl/stable/
Language: Julia
License: NOASSERTION
License Family: other
Topics: optim, julia, optimization, unconstrained-optimization, unconstrained-optimisation, optimisation
Last push: 2026-09-02T21:49:30+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 88, longevity 100
- inputs: {"age_days": 5264, "days_push": 0, "days_rel": 0, "gap_med": 43, "n_releases_24m": 14}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1204, forks 239 (observed 2026-09-03T02:15:06.323762+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: math
- domain: mathematics, machine-learning
- platform: -
- tags: optimization, unconstrained-optimization, bfgs, gradient-descent, local-minimization, global-optimization, numerical-optimization, algorithms, julia

## Member repositories
- JuliaNLSolvers/Optim.jl (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:06.323762+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:19:41.827997+00:00, confidence not recorded.
  - readme: https://github.com/JuliaNLSolvers/Optim.jl (fetched 2026-09-03T02:15:06.323762+00:00, sha 4df32975eef5)
  - homepage: https://julianlsolvers.github.io/Optim.jl/stable/ (fetched 2026-08-29T12:27:20.697514+00:00, sha 94025c89e38f)
- Data as of 2026-08-30T08:39:29.467469+00:00.