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jump-dev · Aug 16, 2023 · 817845f · 817845f
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diff --git a/Project.toml b/Project.toml
@@ -22,7 +22,7 @@ JuMPDimensionalDataExt = "DimensionalData"
 [compat]
 DimensionalData = "0.24"
 MacroTools = "0.5"
-MathOptInterface = "1.18"
+MathOptInterface = "1.19"
 MutableArithmetics = "1.1"
 OrderedCollections = "1"
 SnoopPrecompile = "1"

diff --git a/docs/src/manual/nonlinear.md b/docs/src/manual/nonlinear.md
@@ -252,7 +252,7 @@ model = Model();
 ```
 
 You may assume the Hessian matrix `H` is initialized with zeros, and because `H`
-is symmetric, you need only to fill in the non-zero of the lower-triangular
+is symmetric, you need only to fill in the non-zero lower-triangular
 terms. The matrix type passed in as `H` depends on the automatic differentiation
 system, so make sure the first argument to the Hessian function supports an
 `AbstractMatrix` (it may be something other than `Matrix{Float64}`). Moreover,
@@ -294,7 +294,7 @@ differentiation of user-defined functions.
  computed derivatives are not subject to approximation error.
 
 JuMP uses [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) to
-perform automatic differentiation; see the ForwardDiff.jl
+perform automatic differentiation of user-defined functions; see the ForwardDiff.jl
 [documentation](https://www.juliadiff.org/ForwardDiff.jl/v0.10.2/user/limitations.html)
 for a description of how to write a function suitable for automatic
 differentiation.