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These problems occur when the functions defining the objective and constraints are the result of costly computer simulations.\n\n## How to Cite\n\nIf you use NOMAD.jl in your work, please cite using the format given in [`CITATION.bib`](https://github.com/bbopt/NOMAD.jl/blob/master/CITATION.bib).\n\n## Installation\n\nNOMAD can be installed and tested through the Julia package manager:\n\n```julia\njulia\u003e ]\npkg\u003e add NOMAD\npkg\u003e test NOMAD\n```\n\n## Quick start\n\nLet's say you want to minimize some objective function :\n\n```julia\nfunction f(x)\n  return x[1]^2 + x[2]^2\nend\n```\n\nwhile keeping some constraint inferior to 0 :\n\n```julia\nfunction c(x)\n  return 1 - x[1]\nend\n```\n\nYou first need to declare a function `eval_fct(x::Vector{Float64})` that returns a *Vector{Float64}* containing the objective function and the constraint evaluated for `x`, along with two booleans.\n\n```julia\nfunction eval_fct(x)\n  bb_outputs = [f(x), c(x)]\n  success = true\n  count_eval = true\n  return (success, count_eval, bb_outputs)\nend\n```\n\n`success` is a boolean set to false if the evaluation should not be taken into account by NOMAD. Here, every evaluation will be considered as a success. `count_eval` is also a boolean, it decides weather the evaluation's counter will be incremented. Here, it is always equal to true so every evaluation will be counted.\n\nThen, create an object of type `NomadProblem` that will contain settings for the optimization.\n\nThe classic constructor takes as arguments the initial point *x0* and the types of the outputs contained in `bb_outputs` (as a `Vector{String}`).\n\n```julia\npb = NomadProblem(2, # number of inputs of the blackbox\n                  2, # number of outputs of the blackbox\n                  [\"OBJ\", \"EB\"], # type of outputs of the blackbox\n                  eval_fct;\n                  lower_bound=[-5.0, -5.0],\n                  upper_bound=[5.0, 5.0])\n```\n\nHere, first element of bb_outputs is the objective function (`f(x)`), second is a constraint treated with the Extreme Barrier method (`c(x)`). In this example, lower and upper bounds have been added but it is not compulsory.\n\nNow call the function `solve(p::NomadProblem, x0::Vector{Float64})` where *x0* is the initial starting point to launch a NOMAD optimization process.\n\n```julia\nresult = solve(pb, [3.0, 3.0])\n```\n\nThe object returned by `solve()` contains information about the run.\n\n## Custom Installation\n\n**Note: NOMAD is already precompiled with Yggdrasil for all platforms.**\n\nTo use your custom NOMAD, set the environmental variables `JULIA_NOMAD_LIBRARY_PATH`\nto point the shared library. Note that NOMAD version 4.3.1 is needed.\n\nFor example:\n```julia\nENV[\"JULIA_NOMAD_LIBRARY_PATH\"] = \"~/Applications/nomad-4.3.1/build/lib\"\nusing NOMAD\n```\n\nAlternatively, you can create an entry in `.julia/config/startup.jl` or set these permanently through your operating system.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbbopt%2Fnomad.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbbopt%2Fnomad.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbbopt%2Fnomad.jl/lists"}