Merge pull request #611 from vaerksted/master

fix typos

docs/src/optimization_packages/metaheuristics.md

@@ -38,7 +38,7 @@ Each optimizer sets default settings based on the optimization problem, but spec
 
 Additionally, `Metaheuristics` common settings which would be defined by [`Metaheuristics.Options`](https://jmejia8.github.io/Metaheuristics.jl/stable/api/#Metaheuristics.Options) can be simply passed as special keyword arguments to `solve` without the need to use the `Metaheuristics.Options` struct.
 
-Lastly, information about the optimization problem such as the true optimum is set via [`Metaheuristics.Information`](https://jmejia8.github.io/Metaheuristics.jl/stable/api/#Metaheuristics.Information) and passed as part of the optimizer struct to `solve` e.g., `solve(prob, ECA(information=Metaheuristics.Inoformation(f_optimum = 0.0)))`
+Lastly, information about the optimization problem such as the true optimum is set via [`Metaheuristics.Information`](https://jmejia8.github.io/Metaheuristics.jl/stable/api/#Metaheuristics.Information) and passed as part of the optimizer struct to `solve` e.g., `solve(prob, ECA(information=Metaheuristics.Information(f_optimum = 0.0)))`
 
 The currently available algorithms and their parameters are listed [here](https://jmejia8.github.io/Metaheuristics.jl/stable/algorithms/).
 

docs/src/optimization_packages/optim.md

@@ -196,7 +196,7 @@ Gradient-based optimizers are optimizers which utilize the gradient information
           * `precondprep = (P, x) -> nothing`
   - [`Optim.BFGS()`](https://julianlsolvers.github.io/Optim.jl/stable/#algo/lbfgs/): **Broyden-Fletcher-Goldfarb-Shanno algorithm**
 
-      + `solve(problem, BFGS(alpaguess, linesearch, initial_invH, initial_stepnorm, manifold))`
+      + `solve(problem, BFGS(alphaguess, linesearch, initial_invH, initial_stepnorm, manifold))`
 
       + `alphaguess` computes the initial step length (for more information, consult [this source](https://github.com/JuliaNLSolvers/LineSearches.jl) and [this example](https://julianlsolvers.github.io/LineSearches.jl/latest/examples/generated/optim_initialstep.html))
 

lib/OptimizationMOI/src/nlp.jl

@@ -189,7 +189,7 @@ function MOI.eval_objective_gradient(evaluator::MOIOptimizationNLPEvaluator, G,
     if evaluator.f.grad === nothing
         error("Use OptimizationFunction to pass the objective gradient or " *
               "automatically generate it with one of the autodiff backends." *
-              "If you are using the ModelingToolkit sybolic interface, pass the `grad` kwarg set to `true` in `OptimizationProblem`.")
+              "If you are using the ModelingToolkit symbolic interface, pass the `grad` kwarg set to `true` in `OptimizationProblem`.")
     end
     evaluator.f.grad(G, x)
     return
@@ -213,7 +213,7 @@ function MOI.eval_constraint_jacobian(evaluator::MOIOptimizationNLPEvaluator, j,
     elseif evaluator.f.cons_j === nothing
         error("Use OptimizationFunction to pass the constraints' jacobian or " *
               "automatically generate i with one of the autodiff backends." *
-              "If you are using the ModelingToolkit sybolic interface, pass the `cons_j` kwarg set to `true` in `OptimizationProblem`.")
+              "If you are using the ModelingToolkit symbolic interface, pass the `cons_j` kwarg set to `true` in `OptimizationProblem`.")
     end
     evaluator.f.cons_j(evaluator.J, x)
     if evaluator.J isa SparseMatrixCSC
@@ -276,7 +276,7 @@ function MOI.eval_hessian_lagrangian(evaluator::MOIOptimizationNLPEvaluator{T},
     if evaluator.f.hess === nothing
         error("Use OptimizationFunction to pass the objective hessian or " *
               "automatically generate it with one of the autodiff backends." *
-              "If you are using the ModelingToolkit sybolic interface, pass the `hess` kwarg set to `true` in `OptimizationProblem`.")
+              "If you are using the ModelingToolkit symbolic interface, pass the `hess` kwarg set to `true` in `OptimizationProblem`.")
     end
     fill!(h, zero(T))
     k = 0
@@ -303,7 +303,7 @@ function MOI.eval_hessian_lagrangian(evaluator::MOIOptimizationNLPEvaluator{T},
         if evaluator.f.cons_h === nothing
             error("Use OptimizationFunction to pass the constraints' hessian or " *
                   "automatically generate it with one of the autodiff backends." *
-                  "If you are using the ModelingToolkit sybolic interface, pass the `cons_h` kwarg set to `true` in `OptimizationProblem`.")
+                  "If you are using the ModelingToolkit symbolic interface, pass the `cons_h` kwarg set to `true` in `OptimizationProblem`.")
         end
         evaluator.f.cons_h(evaluator.cons_H, x)
         for (μi, Hi) in zip(μ, evaluator.cons_H)

src/adtypes.jl

@@ -101,7 +101,7 @@ OptimizationFunction(f, AutoModelingToolkit(); kwargs...)
 This uses the [ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl)
 package's `modelingtookitize` functionality to generate the derivatives and other fields of an `OptimizationFunction`.
 This backend creates the symbolic expressions for the objective and its derivatives as well as
-the constraints and their derivatives. Through `structural_simplify`, it enforces symplifications
+the constraints and their derivatives. Through `structural_simplify`, it enforces simplifications
 that can reduce the number of operations needed to compute the derivatives of the constraints. This automatically
 generates the expression graphs that some solver interfaces through OptimizationMOI like
 [AmplNLWriter.jl](https://github.com/jump-dev/AmplNLWriter.jl) require.