Various spell check corrections (#2555)

NEWS.md

@@ -7,15 +7,15 @@ Version 0.21.7 (In development)
 A summary of changes are as follows:
 
 - New features:
-  * Added `primal_feasibility_report`, which can be used to check whether a 
-    primal point statisfies primal feasibility.
-  * Added `coefficient`, which returns the coefficient associtated with a 
+  * Added `primal_feasibility_report`, which can be used to check whether a
+    primal point satisfies primal feasibility.
+  * Added `coefficient`, which returns the coefficient associated with a
     variable in affine and quadratic expressions.
   * Added `copy_conflict`, which returns the IIS of an infeasible model.
   * Added `solution_summary`, which returns (and prints) a struct containing a
     summary of the solution.
   * Allow `AbstractVector` in vector constraints instead of just `Vector`.
-  * Added `latex_formulation(model)` which returns an object representing the 
+  * Added `latex_formulation(model)` which returns an object representing the
     latex formulation of a model. Use `print(latex_formulation(model))` to print
     the formulation as a string.
 - Bug fixes:
@@ -55,7 +55,7 @@ A summary of changes are as follows:
   * `∈` (`\in<tab>`) can now be used in macros instead of `=` or `in`.
   * Use `haskey(model::Model, key::Symbol)` to check if a name `key` is
     registered in a model.
-  * Added `unregister(model::Model, key::Symbol)` to unregister a name `key` 
+  * Added `unregister(model::Model, key::Symbol)` to unregister a name `key`
     from `model`.
   * Added `callback_node_status` for use in callbacks.
   * Added `print_bridge_graph` to visualize the bridging graph generated by
@@ -68,7 +68,7 @@ A summary of changes are as follows:
   * Some internal functions that were previously exported are now no longer
     exported.
   * Fixed a bug when relaxing a fixed binary variable.
-  * Fixed a `StackOverflowError` that occured when `SparseAxisArray`s had a
+  * Fixed a `StackOverflowError` that occurred when `SparseAxisArray`s had a
     large number of elements.
   * Removed an unnecessary type assertion in `list_of_constraint_types`.
   * Fixed a bug when copying models with registered expressions.

docs/make.jl

@@ -81,7 +81,7 @@ makedocs(
     strict = true,
     # ==========================================================================
     # `modules = [JuMP]`, along with `checkdocs = :exports` causes Documenter to
-    # thow an error if exported functions with docstrings are not contained in
+    # throw an error if exported functions with docstrings are not contained in
     # the Documentation. However, problematically, we include some MOI docs,
     # which forces us to include MOI in `modules`, despite the fact that we
     # don't necessarily want to document every MOI method.

docs/src/background/algebraic_modeling_languages.md

@@ -169,7 +169,7 @@ a higher-level modeling interface like JuMP.
 There are three main parts to MathOptInterface:
 
  1. A solver-independent API that abstracts concepts such as adding and deleting
-    variables and constraints, settting and getting parameters, and querying
+    variables and constraints, setting and getting parameters, and querying
     results. For more information on the MathOptInterface API, read the
     [documentation]([https://jump.dev/MathOptInterface.jl/stable/])
 

docs/src/background/should_i_use.md

@@ -89,5 +89,5 @@ Alternatives to consider are:
 
 !!! note
     `Convex.jl` is also built on MathOptInterface, and shares the same set of
-    underlying solvers. However, you input problems differenly, and Convex.jl
+    underlying solvers. However, you input problems differently, and Convex.jl
     checks that the problem is DCP.

docs/src/developers/extensions.md

@@ -54,7 +54,7 @@ model = Model()
 Just as `Bin` and `Int` create binary and integer variables, you can extend
 the [`@variable`](@ref) macro to create new types of variables. Here is an
 explanation by example, where we create a `AddTwice` type, that creates a tuple
-of two JuMP variables instead of a single varaible.
+of two JuMP variables instead of a single variable.
 
 First, create a new struct. This can be anything. Our struct holds a
 [`VariableInfo`](@ref) object that stores bound information, and whether the

docs/src/developers/roadmap.md

@@ -11,22 +11,22 @@ JuMP 1.0 will be ready to release roughly when all of these tasks are completed.
 Some but not all of these tasks are summarized in the
 [JuMP 1.0 milestone](https://github.com/jump-dev/JuMP.jl/milestone/12).
 
-- Create a website for JuMP. (**Done**: [jump.dev](https://jump.dev))
+- Create a website for JuMP (**Done**: [jump.dev](https://jump.dev))
 - Deprecate the JuliaOpt organization and move repositories to the
-  [JuMP-dev](https://github.com/JuMP-dev) organization. (**Done**)
-- Address major regressions from JuMP 0.18.
+  [JuMP-dev](https://github.com/JuMP-dev) organization (**Done**)
+- Address major regressions from JuMP 0.18
   - Performance ([#1403](https://github.com/jump-dev/JuMP.jl/issues/1403),
                  [#1654](https://github.com/jump-dev/JuMP.jl/issues/1654),
                  [#1607](https://github.com/jump-dev/JuMP.jl/issues/1607))
   - Callbacks (**Done**: see `examples/callbacks.jl`)
   - Column generation syntax (**Done**: see `examples/cutting_stock_column_generation.jl`)
   - Support for second-order cones in Gurobi, CPLEX, and Xpress (**Done**)
-- Fix issues that we promised MOI would fix.
+- Fix issues that we promised MOI would fix
   - Checking feasibility of solutions (**Done**: [#2466](https://github.com/jump-dev/JuMP.jl/pull/2466))
   - Accessing IIS (**Done**: see [Conflicts](@ref))
   - Accessing multiple results from solvers (**Done**: [Gurobi#392](https://github.com/jump-dev/Gurobi.jl/pull/392))
   - Dual warm-starts (**Done**: [#2214](https://github.com/jump-dev/JuMP.jl/pull/2214))
-- Address “easy” usability issues
+- Address "easy" usability issues
   - Line numbers in error messages (**Done**: [#2276](https://github.com/jump-dev/JuMP.jl/pull/2276))
   - LP sensitivity summary (**Done**: see [Sensitivity analysis for LP](@ref))
   - Inferred element types for collections in macros (**Done**: [#2070](https://github.com/jump-dev/JuMP.jl/pull/2070))
@@ -35,11 +35,11 @@ Some but not all of these tasks are summarized in the
   - Separate how-to, concept explanation, and technical reference following the
     [Divio recommendations](https://www.divio.com/blog/documentation/) (**Done**)
   - Fully integrate [JuMPTutorials](https://github.com/jump-dev/JuMPTutorials.jl)
-    with JuMP's documentation. (**Done**)
+    with JuMP's documentation (**Done**)
 - Developer experience
-  - Get JuMP’s unit tests running faster. See [#1745](https://github.com/jump-dev/JuMP.jl/pull/1745). (**Done**)
-- All solvers should complete the transition to MOI. (**Done**)
-- Provide packages for installing Bonmin and Couenne.
+  - Get JuMP's unit tests running faster. See [#1745](https://github.com/jump-dev/JuMP.jl/pull/1745). (**Done**)
+- All solvers should complete the transition to MOI (**Done**)
+- Provide packages for installing Bonmin and Couenne
 - [MathOptFormat](https://github.com/odow/MathOptFormat.jl) 1.0 (**Done**)
 
 ## MOI 1.0

docs/src/developers/style.md

@@ -382,7 +382,7 @@ points more specific to Julia and documentation that use [Documenter](https://gi
 - Every exported object needs a docstring
 - All examples in docstrings should be [`jldoctests`](https://juliadocs.github.io/Documenter.jl/stable/man/doctests/)
 - Always use complete English sentences with proper punctuation
-- Do not terminate lists swith punctuation (e.g., as in this doc)
+- Do not terminate lists with punctuation (e.g., as in this doc)
 
 Here is an example:
 ````julia

docs/src/manual/nlp.md

@@ -374,7 +374,7 @@ The above code creates a JuMP model with the objective function
     User-defined functions cannot be re-registered and will not update if you
     modify the underlying Julia function. If you want to change a user-defined
     function between solves, rebuild the model or use a different name. To use
-    a different name programatically, see [Raw expression input](@ref).
+    a different name programmatically, see [Raw expression input](@ref).
 
 ### Register a function and gradient
 
@@ -629,7 +629,7 @@ julia> @NLconstraint(model, Min, x + x^2 <= 1)
 ### More complicated examples
 
 Raw expression input is most useful when the expressions are generated
-programatically, often in conjunction with user-defined functions.
+programmatically, often in conjunction with user-defined functions.
 
 As an example, we construct a model with the nonlinear constraints `f(x) <= 1`,
 where `f(x) = x^2` and `f(x) = sin(x)^2`:

docs/src/manual/variables.md

@@ -680,7 +680,7 @@ x = @variable(model, [1:2, 1:2], set = PSDCone())
 In the section [Variable containers](@ref), we explained how JuMP supports the
 efficient creation of collections of JuMP variables in three types of
 containers. However, users are also free to create collections of JuMP variables
-in their own datastructures. For example, the following code creates a
+in their own data structures. For example, the following code creates a
 dictionary with symmetric matrices as the values:
 ```jldoctest; setup=:(model=Model())
 julia> variables = Dict{Symbol, Array{VariableRef,2}}()

docs/src/tutorials/Conic programs/tips_and_tricks.jl

@@ -36,7 +36,7 @@ Random.seed!(1234) # hide
 nothing            # hide
 
 # !!! tip
-#     A good resource for learning more about functions which can be modelled
+#     A good resource for learning more about functions which can be modeled
 #     using cones is the [MOSEK Modeling Cookbook](https://docs.mosek.com/modeling-cookbook/index.html).
 
 # ## What is a cone?
@@ -221,4 +221,4 @@ objective_value(model)
 # ## Other Cones and Functions
 
 # For other cones supported by JuMP, check out the
-# [MathOptInterface Manual](http://jump.dev/MathOptInterface.jl/stable).
+# [MathOptInterface Manual](https://jump.dev/MathOptInterface.jl/stable).

docs/src/tutorials/Getting started/an_introduction_to_julia.jl

@@ -54,7 +54,7 @@ nothing #hide
 #     typed (e.g., `1 - 2`), as well as the evaluation of the expression (`-1`).
 
 # Did you notice how Julia didn't print `.0` after some of the numbers? Julia is
-# a dynamic language, which means you never have to explictly declare the type
+# a dynamic language, which means you never have to explicitly declare the type
 # of a variable. However, in the background, Julia is giving each variable a
 # type. Check the type of something using the `typeof` function:
 
@@ -91,12 +91,12 @@ nothing #hide
 π
 
 # !!! tip
-#     To make π (and most other greek letters), type `\pi` and then press
+#     To make π (and most other Greek letters), type `\pi` and then press
 #     `[TAB]`.
 
 typeof(π)
 
-# Athough if we do math with irrational numbers, they get converted to
+# However, if we do math with irrational numbers, they get converted to
 # `Float64`:
 
 typeof(2π / 3)
@@ -134,7 +134,7 @@ sin(2π / 3) - √3 / 2
 
 # One way of explaining this difference is to consider how we would write
 # `1 / 3` and `2 / 3` using only four digits after the decimal point. We would
-# write `1 / 3` as `0.3333`, and `2 / 3` as `0.6667`. So, depiste the fact that
+# write `1 / 3` as `0.3333`, and `2 / 3` as `0.6667`. So, despite the fact that
 # `2 * (1 / 3) == 2 / 3`, `2 * 0.3333 == 0.6666 != 0.6667`.
 
 # Let's try that again using ≈ (`\approx + [TAB]`) instead of `==`:
@@ -189,7 +189,7 @@ b = [5, 6]
 A = [1.0 2.0; 3.0 4.0]
 
 # Note how this time the type is `Array{Float64, 2}`; the elements are `Float64`
-# and there are `2` dimenions.
+# and there are `2` dimensions.
 
 # We can do linear algebra:
 
@@ -378,7 +378,7 @@ end
 # ## Comprehensions
 
 # Similar to languages like Haskell and Python, Julia supports the use of simple
-# loops in the construction of arrays and dictionaries, called comprehenions.
+# loops in the construction of arrays and dictionaries, called comprehensions.
 #
 # A list of increasing integers:
 
@@ -487,7 +487,7 @@ catch err; showerror(stdout, err) end  #hide
 # ### Broadcasting
 
 # In the example above, we didn't define what to do if `f` was passed an
-# `Array`. Luckily, Julia provides a convienient syntax for mapping `f`
+# `Array`. Luckily, Julia provides a convenient syntax for mapping `f`
 # element-wise over arrays! Just add a `.` between the name of the function and
 # the opening `(`. This works for _any_ function, including functions with
 # multiple arguments. For example:
@@ -525,7 +525,7 @@ println("mutable_type: $(mutable_type)")
 println("immutable_type: $(immutable_type)")
 
 # Because `Vector{Int}` is a mutable type, modifying the variable inside the
-# function changed the value outside of the function. In constrast, the change
+# function changed the value outside of the function. In contrast, the change
 # to `immutable_type` didn't modify the value outside the function.
 
 # You can check mutability with the `isimmutable` function:
@@ -561,7 +561,7 @@ Random.seed!(33)
 # Pkg.add("JuMP")
 # ```
 
-# For a complete list of registed Julia packages see the package listing at
+# For a complete list of registered Julia packages see the package listing at
 # [JuliaHub](https://juliahub.com).
 
 # From time to you may wish to use a Julia package that is not registered.  In

docs/src/tutorials/Getting started/getting_started_with_JuMP.jl

@@ -108,7 +108,7 @@ using GLPK
 
 model = Model(GLPK.Optimizer)
 
-# Variables are modelled using [`@variable`](@ref):
+# Variables are modeled using [`@variable`](@ref):
 
 @variable(model, x >= 0)
 
@@ -120,7 +120,7 @@ model = Model(GLPK.Optimizer)
 
 @objective(model, Min, 12x + 20y)
 
-# Constraints are modelled using [`@constraint`](@ref). Here `c1` and `c2` are
+# Constraints are modeled using [`@constraint`](@ref). Here `c1` and `c2` are
 # the names of our constraint.
 
 @constraint(model, c1, 6x + 8y >= 100)

docs/src/tutorials/Getting started/variables_constraints_objective.jl

@@ -124,8 +124,8 @@ u = [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]
 
 # ### Variable types
 
-# The last arguement to the `@variable` macro is usually the variable type. Here
-# we'll look at how to specifiy the variable type.
+# The last argument to the `@variable` macro is usually the variable type. Here
+# we'll look at how to specify the variable type.
 
 # #### Integer variables
 
@@ -169,7 +169,7 @@ model = Model()
 # ### Constraint references
 
 # While calling the `@constraint` macro, we can also set up a constraint
-# reference. Such a referrence is useful for obtaining additional information
+# reference. Such a reference is useful for obtaining additional information
 # about the constraint, such as its dual solution.
 
 @constraint(model, con, x <= 4)

docs/src/tutorials/Getting started/working_with_data_files.jl

@@ -75,8 +75,8 @@ import DataFrames
 import XLSX
 
 # To read a Excel file into a DataFrame, we use the following julia code. The
-# first arguement to the `readtable` function is the file to be read and the
-# second arguement is the name of the sheet.
+# first argument to the `readtable` function is the file to be read and the
+# second argument is the name of the sheet.
 
 excel_df = DataFrames.DataFrame(
     XLSX.readtable(joinpath(DATA_DIR, "SalesData.xlsx"), "SalesOrders")...
@@ -111,14 +111,14 @@ csv_df = CSV.read(joinpath(DATA_DIR, "StarWars.csv"), DataFrames.DataFrame)
 
 ss_df = CSV.read(joinpath(DATA_DIR, "Cereal.txt"), DataFrames.DataFrame)
 
-# We can also specify the delimiter by passing the `delim` arguement.
+# We can also specify the delimiter by passing the `delim` argument.
 
 delim_df = CSV.read(
     joinpath(DATA_DIR, "Soccer.txt"), DataFrames.DataFrame, delim = "::"
 )
 
 # Note that by default, are read-only. If we wish to make changes to the data
-# read, we pass the `copycols = true` arguement in the function call.
+# read, we pass the `copycols = true` argument in the function call.
 
 ss_df = CSV.read(
     joinpath(DATA_DIR, "Cereal.txt"), DataFrames.DataFrame, copycols = true
@@ -253,8 +253,8 @@ end
 # * 1 = no visa required for travel
 # * 0 = visa required for travel
 
-# Let us assossciate each passport with a decision variable $pass_{cntr}$ for
-# each country. We want to minize the sum $\sum pass_{cntr}$ over all countries.
+# Let us associate each passport with a decision variable $pass_{cntr}$ for
+# each country. We want to minimize the sum $\sum pass_{cntr}$ over all countries.
 
 # Since we wish to visit all the countries, for every country, we should own at
 # least one passport that lets us travel to that country visa free. For one
@@ -280,7 +280,7 @@ import GLPK
 
 World = names(passport_data)[2:end]
 
-# Then, create the model and intialize the decision variables:
+# Then, create the model and initialize the decision variables:
 model = Model(GLPK.Optimizer)
 @variable(model, pass[cntr in World], Bin)
 

docs/src/tutorials/Mixed-integer linear programs/cannery.jl

@@ -42,7 +42,7 @@ function example_cannery()
     @constraint(
         cannery, demand_con[j = 1:num_markets], sum(ship[:, j]) >= demand[j]
     )
-    ## Minimize transporatation cost
+    ## Minimize transportation cost
     @objective(
         cannery,
         Min,

docs/src/tutorials/Mixed-integer linear programs/cutting_stock_column_generation.jl

@@ -11,10 +11,10 @@
 
 # Intuitively, this problem is about cutting large rolls of paper into smaller
 # pieces. There is an exact demand of pieces to meet, and all rolls have the
-# same size. The goal is to meet the demand while maximising the profits (each
+# same size. The goal is to meet the demand while maximizing the profits (each
 # paper roll has a fixed cost, each sold piece allows earning some money),
 # which is roughly equivalent to using the smallest amount of rolls
-# to cut (or, equivalently, to minimise the amount of paper waste).
+# to cut (or, equivalently, to minimize the amount of paper waste).
 
 # This function takes five parameters:
 

docs/src/tutorials/Mixed-integer linear programs/factory_schedule.jl

@@ -113,7 +113,7 @@ function example_factory_schedule()
       [m in months, f in factories],
       production[m, f] >= d_min_cap[m, f] * status[m, f],
    )
-   ## The production cannot be more tha maximum capacity.
+   ## The production cannot be more that maximum capacity.
    @constraint(
       model,
       [m in months, f in factories],

docs/src/tutorials/Mixed-integer linear programs/finance.jl

@@ -65,7 +65,7 @@ import GLPK
 # 2. upper bounds on $u_{i}$
 # 3. nonnegativity of the decision variables $u_{i}$, $v_{i}$ and $w_{i}$.
 
-# Our objective will be to simply maximimse the company's wealth in June, which
+# Our objective will be to simply maximize the company's wealth in June, which
 # say we represent with the variable $m$.
 
 financing = Model(GLPK.Optimizer)

docs/src/tutorials/Mixed-integer linear programs/network_flows.jl

@@ -176,7 +176,7 @@ max_flow = Model(GLPK.Optimizer)
 @variable(max_flow, f[1:n,1:n] >= 0)
 # Capacity constraints
 @constraint(max_flow, [i = 1:n, j = 1:n], f[i,j] <= G[i,j])
-# Flow conservation contraints
+# Flow conservation constraints
 @constraint(max_flow, [i = 1:n; i != 1 && i != 8], sum(f[i,:]) == sum(f[:,i]))
 @objective(max_flow, Max, sum(f[1, :]))
 

docs/src/tutorials/Mixed-integer linear programs/sudoku.jl

@@ -35,7 +35,7 @@
 # ![Partially solved Sudoku](../../assets/partial_sudoku.svg)
 
 # Solving a Sudoku isn't an optimization problem with an objective; its actually
-# a *feasibility* problem: we wish to find a feasible solution that satsifies
+# a *feasibility* problem: we wish to find a feasible solution that satisfies
 # these rules. You can think of it as an optimization problem with an objective
 # of 0.
 #