Merge pull request #791 from SciML/optdocsbump

Update docstrings for Optimization v4

Author: Vaibhavdixit02

Project.toml

@@ -1,7 +1,7 @@
 name = "SciMLBase"
 uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 authors = ["Chris Rackauckas <[email protected]> and contributors"]
-version = "2.53.1"
+version = "2.53.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/problems/optimization_problems.jl

@@ -66,7 +66,7 @@ Any extra keyword arguments are captured to be sent to the optimizers.
 
 * `f`: the function in the problem.
 * `u0`: the initial guess for the optimization variables.
-* `p`: the constant parameters used for defining the problem. Defaults to `NullParameters`.
+* `p`: Either the constant parameters or fixed data (full batch) used in the objective or a [MLUtils](https://github.com/JuliaML/MLUtils.jl) [`DataLoader`](https://juliaml.github.io/MLUtils.jl/stable/api/#MLUtils.DataLoader) for minibatching with stochastic optimization solvers. Defaults to `NullParameters`.
 * `lb`: the lower bounds for the optimization variables `u`.
 * `ub`: the upper bounds for the optimization variables `u`.
 * `int`: integrality indicator for `u`. If `int[i] == true`, then `u[i]` is an integer variable.
@@ -90,6 +90,10 @@ Inequality constraints are then given by making `lcons[i] != ucons[i]`. For exam
 
 Note that these vectors must be sized to match the number of constraints, with one set of conditions for each constraint.
 
+## Data handling
+
+As described above the second argument of the objective definition can take a full batch or a [`DataLoader`](https://juliaml.github.io/MLUtils.jl/stable/api/#MLUtils.DataLoader) object for mini-batching which is useful for stochastic optimization solvers. Thus the data either as an Array or a `DataLoader` object should be passed as the third argument of the `OptimizationProblem` constructor.
+For an example of how to use this data handling, see the `Sophia` example in the [Optimization.jl documentation](https://docs.sciml.ai/Optimization/dev/optimization_packages/optimization) or the [mini-batching tutorial](https://docs.sciml.ai/Optimization/dev/tutorials/minibatch/).
 """
 struct OptimizationProblem{iip, F, uType, P, LB, UB, I, LC, UC, S, K} <:
        AbstractOptimizationProblem{iip}

src/scimlfunctions.jl

@@ -1784,7 +1784,7 @@ A representation of an objective function `f`, defined by:
 ```
 
 and all of its related functions, such as the gradient of `f`, its Hessian,
-and more. For all cases, `u` is the state and `p` are the parameters.
+and more. For all cases, `u` is the state which in this case are the optimization variables and `p` are the fixed parameters or data.
 
 ## Constructor