Fix typos detected by spell-checking GitHub Action

libprima · Oct 23, 2023 · 9760ada · 9760ada
1 parent 10d3a56
commit 9760ada
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Showing 3 changed files with 22 additions and 21 deletions.
diff --git a/.github/actions/spelling/allow.txt b/.github/actions/spelling/allow.txt
@@ -1103,6 +1103,7 @@ isrvub
 isscalar
 isspace
 isstruct
+issuccess
 issymmetric
 istp
 istr

diff --git a/src/PRIMA.jl b/src/PRIMA.jl
@@ -28,7 +28,7 @@ An object, say `info`, of this type has the following properties:
     info.nl_ineq  # non-linear inequality constraints, empty vector if none
 
 Call `issuccess(info)` or `issuccess(info.status)` to check whether algorithm
-was sucessful. Call `PRIMA.reason(info)` or `PRIMA.reason(info.status)` to
+was successful. Call `PRIMA.reason(info)` or `PRIMA.reason(info.status)` to
 retrieve a textual description of the algorithm termination.
 
 """
@@ -62,7 +62,7 @@ LinearAlgebra.issuccess(status::Status) =
     PRIMA.reason(info::PRIMA.Info) -> str
     PRIMA.reason(status::PRIMA.Status) -> str
 
-yield a textual message explaining `info.statu` or `status`, the status code
+yield a textual message explaining `info.status` or `status`, the status code
 returned by one of the PRIMA optimizers.
 
 """
@@ -521,7 +521,7 @@ function cobyla!(f, x::DenseVector{Cdouble};
     A_eq, b_eq = _get_linear_constraints(linear_eq, n, scl)
     A_ineq, b_ineq = _get_linear_constraints(linear_ineq, n, scl)
 
-    # Alocate vector to store all non-linear constraints (the non-linear
+    # Allocate vector to store all non-linear constraints (the non-linear
     # equalities being implemented as 2 inequalities each).
     nl_all = Vector{Cdouble}(undef, 2*length(nl_eq) + length(nl_ineq))
 
@@ -655,7 +655,7 @@ struct ObjFun{F,E,I}
     c_ineq::I   # callable implementing non-linear inequalities as `c_ineq(x) ≤ 0`
     n_ineq::Int # number of non-linear inequalities
     scl::Vector{Cdouble} # scaling factors or empty
-    wrk::Vector{Cdouble} # workspace for variables
+    x::Vector{Cdouble}   # workspace for variables
 end
 
 unconstrained(x::AbstractVector{T}) where {T} = NullVector{T}()
@@ -781,7 +781,7 @@ function unsafe_call(f::ObjFun, x_ptr::Ptr{Cdouble}, c_ptr::Ptr{Cdouble})
 end
 
 function get_variables(f::ObjFun, x_ptr::Ptr{Cdouble})
-    n, x, scl = f.n, f.wrk, f.scl
+    n, x, scl = f.n, f.x, f.scl
     length(x) == n || corrupted_structure("invalid number of variables")
     if isempty(scl)
         # No scaling of variables.

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -18,20 +18,20 @@ optimizer(algo::Symbol) =
     algo === :prima  ? PRIMA.prima  :
     error("unknown optimizer `:$algo`")
 
-prt_1(x::AbstractVector, info::PRIMA.Info) = prt_1(stdout, x, info)
-function prt_1(io::IO, x::AbstractVector, info::PRIMA.Info)
+print_1(x::AbstractVector, info::PRIMA.Info) = print_1(stdout, x, info)
+function print_1(io::IO, x::AbstractVector, info::PRIMA.Info)
     msg = PRIMA.reason(info)
     println(io, "x = $x, f(x) = $(info.fx), status = $(info.status), msg = '$msg', evals = $(info.nf)")
 end
 
-prt_2(x::AbstractVector, info::PRIMA.Info) = prt_2(stdout, x, info)
-function prt_2(io::IO, x::AbstractVector, info::PRIMA.Info)
+print_2(x::AbstractVector, info::PRIMA.Info) = print_2(stdout, x, info)
+function print_2(io::IO, x::AbstractVector, info::PRIMA.Info)
     msg = PRIMA.reason(info)
     println(io, "x = $x, f(x) = $(info.fx), cstrv = $(info.cstrv), status = $(info.status), msg = '$msg', evals = $(info.nf)")
 end
 
-prt_3(x::AbstractVector, info::PRIMA.Info) = prt_3(stdout, x, info)
-function prt_3(io::IO, x::AbstractVector, info::PRIMA.Info)
+print_3(x::AbstractVector, info::PRIMA.Info) = print_3(stdout, x, info)
+function print_3(io::IO, x::AbstractVector, info::PRIMA.Info)
     msg = PRIMA.reason(info)
     println("x = $x, f(x) = $(info.fx), cstrv = $(info.cstrv), c(x) = $(info.nl_ineq), status = $(info.status), msg = '$msg', evals = $(info.nf)")
 end
@@ -97,7 +97,7 @@ end
             kwds = (rhobeg = 1.0, rhoend = 1e-3, ftarget = -Inf,
                     maxfun = 200n, npt = 2n + 1, iprint = PRIMA.MSG_EXIT)
             x, info = @inferred PRIMA.newuoa(f, x0; kwds...)
-            prt_1(x, info)
+            print_1(x, info)
             @test x ≈ [3,2] atol=2e-2 rtol=0
             @test f(x) ≈ info.fx
             @test x0 == x0_sav
@@ -117,7 +117,7 @@ end
             kwds = (rhobeg = 1.0, rhoend = 1e-3, ftarget = -Inf,
                     maxfun = 200n, iprint = PRIMA.MSG_EXIT)
             x, info = @inferred PRIMA.uobyqa(f, x0; kwds...)
-            prt_1(x, info)
+            print_1(x, info)
             @test x ≈ [3,2] atol=2e-2 rtol=0
             @test f(x) ≈ info.fx
             @test x0 == x0_sav
@@ -134,7 +134,7 @@ end
                     rhobeg = 1.0, rhoend = 1e-3, ftarget = -Inf,
                     maxfun = 200n, npt = 2n + 1, iprint = PRIMA.MSG_EXIT)
             x, info = @inferred PRIMA.bobyqa(f, x0; kwds...)
-            prt_1(x, info)
+            print_1(x, info)
             @test x ≈ [3,2] atol=2e-2 rtol=0
             @test f(x) ≈ info.fx
             @test x0 == x0_sav
@@ -158,7 +158,7 @@ end
             # First call with just the number of non-linear inequality constraints.
             x, info = @inferred PRIMA.cobyla(f, x0; kwds...,
                                              nonlinear_ineq = c_ineq)
-            prt_3(x, info)
+            print_3(x, info)
             @test x ≈ [3,2] atol=2e-2 rtol=0
             @test f(x) ≈ info.fx
             @test x0 == x0_sav
@@ -188,7 +188,7 @@ end
                     rhobeg = 1.0, rhoend = 1e-3, ftarget = -Inf,
                     maxfun = 200*n, npt = 2n + 1, iprint = PRIMA.MSG_EXIT)
             x, info = @inferred PRIMA.lincoa(f, x0; kwds...)
-            prt_2(x, info)
+            print_2(x, info)
             @test x ≈ [3,2] atol=2e-2 rtol=0
             @test f(x) ≈ info.fx
             @test x0 == x0_sav
@@ -279,7 +279,7 @@ end
             else
                 continue
             end
-            prt_1(x, info)
+            print_1(x, info)
             @test x ≈ [1,1] rtol=0 atol=(optim == :cobyla ? 3e-2 : 2e-2)
             @test f(x) ≈ info.fx
 
@@ -301,7 +301,7 @@ end
                 else
                     continue
                 end
-                prt_1(x, info)
+                print_1(x, info)
                 @test x ≈ [1.095247,1.2] rtol=0 atol=2e-2
                 @test f(x) ≈ info.fx
             end
@@ -320,7 +320,7 @@ end
                 else
                     continue
                 end
-                prt_1(x, info)
+                print_1(x, info)
                 @test x ≈ [1.0,1.0] rtol=0 atol=(optim == :cobyla ? 3e-2 : 2e-2)
                 @test f(x) ≈ info.fx
 
@@ -337,7 +337,7 @@ end
                 else
                     continue
                 end
-                prt_1(x, info)
+                print_1(x, info)
                 @test x ≈ [1.0,1.0] rtol=0 atol=(optim == :cobyla ? 3e-2 : 2e-2)
                 @test f(x) ≈ info.fx
 
@@ -355,7 +355,7 @@ end
                 else
                     continue
                 end
-                prt_1(x, info)
+                print_1(x, info)
                 @test x ≈ [1.441832,2.077557] rtol=0 atol=(optim == :cobyla ? 3e-2 : 2e-2)
                 @test f2(x) ≈ info.fx
             end