[ANN] NonlinearOptimization TestFunctions.jl: A comprehensive collection of test functions for nonlinear optimization

Package Announcements
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Hello Julia Community,

I am excited to announce my new package, NonlinearOptimizationTestFunctionsInJulia.jl. It is designed to be a comprehensive collection of test functions for benchmarking nonlinear optimization algorithms. Each function comes with an analytical gradient and detailed metadata to ensure efficient and reliable testing.

Key Features:

  • Standardized Test Functions: Includes well-known functions like Rosenbrock, Ackley, and Branin, all with consistent interfaces.
  • Analytical Gradients: Accelerates testing and benchmarking processes.
  • Detailed Metadata: Each function is supplied with information on its starting points, global minima, and properties (e.g., unimodal, multimodal).
  • Compatibility: Seamless integration with popular optimization packages such as Optim.jl, NLopt.jl, and tools like ForwardDiff.jl.

This package is a tool for anyone looking to develop, test, or compare optimization algorithms.

You can find the repository here: GitHub - UweAlex/NonlinearOptimizationTestFunctions.jl: NonlinearOptimizationTestFunctionsInJulia is a Julia package designed for testing and benchmarking nonlinear optimization algorithms. It provides a comprehensive collection of standard test functions, each equipped with analytical gradients, metadata, and validation mechanisms.

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Cool! Looks similar to CUTEst (GitHub - JuliaSmoothOptimizers/CUTEst.jl: Julia's CUTEst Interface; has 286 unconstrained problems) and GitHub - JuliaSmoothOptimizers/OptimizationProblems.jl: Optimization Problems for Julia.

More test functions:

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Can you make sure to support the Optimization.jl form? Then we’ll add it to the SciMLBenchmarks.jl server.

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(post deleted by author)

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Thanks for the great feedback! You’re right that our project shares vibes with CUTEst.jl and OptimizationProblems.jl, but we’re carving out a niche with a pure-Julia, lightweight suite starting with Molga & Smutnicki (2005) functions like Cross-in-Tray. We’re excited to expand with AMPGO and BBOB functions, like Alpine, to test algorithms across dimensions. Unlike CUTEst.jl, we avoid Fortran dependencies for stability.

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There’s no Fortran in OptimizationProblems.jl. Why not contribute new problems there?

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I am new to GitHub, and I am not really experienced with Julia, so I thought it’s better to start something for myself first. Try to create Problems without making problems^^. The plan is to learn enough to be able to support other projects in the future

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