Update docs/src/inverse_problems/optimization_ode_param_fitting.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/inverse_problems/optimization_ode_param_fitting.md

@@ -120,7 +120,7 @@ optprob = OptimizationProblem(loss_function, [1.0, 1.0, 1.0]; lb = [0.1, 0.1, 0.
 nothing # hide
 ```
 
-In addition to boundaries, Optimization.jl also supports setting [linear and non-linear constraints](https://docs.sciml.ai/Optimization/stable/tutorials/constraints/#constraints) on its output solution.
+In addition to boundaries, Optimization.jl also supports setting [linear and non-linear constraints](https://docs.sciml.ai/Optimization/stable/tutorials/constraints/#constraints) on its output solution for some optimizers.
 
 ## Parameter fitting with known parameters
 If we from previous knowledge know that *kD = 0.1*, and only would like to fit the values of *kD* and *kP*, this can be achieved through `build_loss_objective`'s `prob_generator` argument. First, we create a function (`fixed_p_prob_generator`) that modifies our `ODEProblem` to incorporate this knowledge: