Nonconvex
Nonconvex.jl is an umbrella package over implementations and wrappers of a number of nonconvex constrained optimization algorithms and packages making use of automatic differentiation. Zero, first and second order methods are available. Nonlinear equality and inequality constraints as well as integer constraints are supported. A detailed description of all the algorithms and features available in Nonconvex can be found in the documentation.
The JuliaNonconvex organization
The JuliaNonconvex organization hosts a number of packages which are available for use in Nonconvex.jl. The correct package is loaded using the Nonconvex.@load macro with the algorithm or package name. See the documentation for more details. The following is a summary of all the packages in the JuliaNonconvex organization.
| Package | Description | Tests | Coverage |
|---|---|---|---|
| Nonconvex.jl | Umbrella package for nonconvex optimization | |
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| NonconvexCore.jl | All the interface functions and structs |  |
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| NonconvexMMA.jl | Method of moving asymptotes implementation |  |
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| NonconvexIpopt.jl | Ipopt.jl wrapper |  |
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| NonconvexNLopt.jl | NLopt.jl wrapper |  |
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| NonconvexPercival.jl | Percival.jl wrapper (an augmented Lagrangian algorithm implementation) |  |
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| NonconvexJuniper.jl | Juniper.jl wrapper |  |
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| NonconvexPavito.jl | Pavito.jl wrapper |  |
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| NonconvexSemidefinite.jl | Nonlinear semi-definite programming algorithm |  |
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| NonconvexMultistart.jl | Multi-start optimization algorithms |  |
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| NonconvexBayesian.jl | Constrained Bayesian optimization implementation |  |
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| NonconvexSearch.jl | Multi-trajectory and local search methods |  |
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| NonconvexAugLagLab.jl | Experimental augmented Lagrangian package |  |
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How to contribute?
A beginner? The easiest way to contribute is to read the documentation, test the package and report issues.
An impulsive tester? Improving the test coverage of any package is another great way to contribute to the JuliaNonconvex org. Check the coverage report of any of the packages above by clicking the coverage badge. Find the red lines in the report and figure out tests that would cover these lines of code.
An algorithm head? There are plenty of optimization algorithms that can be implemented and interfaced in Nonconvex.jl. You could be developing the next big nonconvex semidefinite programming algorithm right now! Or the next constraint handling method for evolutionary algorithms!
A hacker? Let's figure out how to wrap some optimization package in Julia in the unique, simple and nimble Nonconvex.jl style.
A software designer? Let's talk about design decisions and how to improve the modularity of the ecosystem.
You can always reach out by opening an issue.