From 00c3b35d8f0d894e0ff70768b58ee200631cc6a2 Mon Sep 17 00:00:00 2001 From: Oscar Dowson Date: Tue, 10 Dec 2024 13:29:27 +1300 Subject: [PATCH] [docs] clarify black-box opt. in "Should you use JuMP" (#3901) --- docs/src/should_i_use.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/src/should_i_use.md b/docs/src/should_i_use.md index 28ef5e58217..b8180049b7d 100644 --- a/docs/src/should_i_use.md +++ b/docs/src/should_i_use.md @@ -74,18 +74,18 @@ consider using other packages such as: ### Black-box, derivative free, or unconstrained optimization -JuMP does support nonlinear programs with constraints and objectives containing +JuMP supports nonlinear programs with constraints and objectives containing user-defined operators. However, the functions must be automatically -differentiable, or need to provide explicit derivatives. (See +differentiable, or you need to provide explicit derivatives. (See [User-defined operators](@ref jump_user_defined_operators) for more information.) -If your function is a black-box that is non-differentiable (for example, it is -the output of a simulation written in C++), JuMP is not the right tool for the +If your function is a black-box that is non-differentiable (for example, the +function calls a simulation written in C++), JuMP is not the right tool for the job. This also applies if you want to use a derivative free method. Even if your problem is differentiable, if it is unconstrained there is limited benefit (and downsides in the form of more overhead) to using JuMP over tools -which are only concerned with function minimization. +which are concerned only with function minimization. Alternatives to consider are: