🤖 Format .jl files (#119)

Co-authored-by: geoffroyleconte <[email protected]>

Author: github-actions[bot]

src/presolve/postsolve_utils.jl

@@ -67,8 +67,13 @@ function restore_s!(
   restore_x!(kept_cols, s_u_in, s_u, nvar)
 end
 
-function add_Hx!(z::Vector{T}, hcols::Vector{Col{T}}, kept_cols::Vector{Bool}, x::Vector{T}) where {T}
-  for j in 1:length(hcols)
+function add_Hx!(
+  z::Vector{T},
+  hcols::Vector{Col{T}},
+  kept_cols::Vector{Bool},
+  x::Vector{T},
+) where {T}
+  for j = 1:length(hcols)
     hcolj = hcols[j]
     Hxj = zero(T)
     for (i, hij) in zip(hcolj.nzind, hcolj.nzval)
@@ -78,4 +83,4 @@ function add_Hx!(z::Vector{T}, hcols::Vector{Col{T}}, kept_cols::Vector{Bool}, x
     z[j] += Hxj
   end
   return z
-end
+end

src/presolve/presolve.jl

@@ -414,10 +414,7 @@ postsolve!(
   sol_in::QMSolution{S},
 ) where {T, S} = postsolve!(psqm.psd, sol, sol_in)
 
-function postsolve(
-  psd::PresolvedData{T, S},
-  sol_in::QMSolution{S},
-) where {T, S}
+function postsolve(psd::PresolvedData{T, S}, sol_in::QMSolution{S}) where {T, S}
   x = fill!(S(undef, psd.nvar), zero(T))
   y = fill!(S(undef, psd.ncon), zero(T))
   s_l = fill!(S(undef, psd.nvar), zero(T))

src/presolve/primal_constraints.jl

@@ -81,21 +81,21 @@ function postsolve!(
   add_Hx!(z, psd.hcols, kept_cols, x) # z = c + Hx
   for l = 1:n
     kept_cols[l] || continue
-    for (k, akl) in zip(acols[l].nzind, acols[l].nzval) 
+    for (k, akl) in zip(acols[l].nzind, acols[l].nzval)
       (psd.kept_rows[i] && k != i) || continue
       z[l] -= akl * y[k]
     end
   end
 
   if forced_lcon
-    yi = T(-Inf) 
+    yi = T(-Inf)
     for (l, ail) in zip(arowi.nzind, arowi.nzval)
       (kept_cols[l]) || continue
       trial = z[l] / ail
       (trial > yi) && (yi = trial)
     end
   else
-    yi = T(Inf) 
+    yi = T(Inf)
     for (l, ail) in zip(arowi.nzind, arowi.nzval)
       (kept_cols[l]) || continue
       trial = z[l] / ail

test/test_presolve.jl

@@ -166,25 +166,29 @@ end
 end
 
 @testset "presolve solves problem" begin
-  Q = [6. 2. 1.
-       2. 5. 2.
-       1. 2. 4.]
-  c = [-8.; -3; -3]
-  A = [1. 0. 1.
-       0. 2. 1.]
-  b = [0.; 3]
-  l = [0.;0;0]
+  Q = [
+    6.0 2.0 1.0
+    2.0 5.0 2.0
+    1.0 2.0 4.0
+  ]
+  c = [-8.0; -3; -3]
+  A = [
+    1.0 0.0 1.0
+    0.0 2.0 1.0
+  ]
+  b = [0.0; 3]
+  l = [0.0; 0; 0]
   u = [Inf; Inf; Inf]
   qp = QuadraticModel(
     c,
     SparseMatrixCOO(tril(Q)),
-    A=SparseMatrixCOO(A),
-    lcon=b,
-    ucon=b,
-    lvar=l,
-    uvar=u,
-    c0=0.,
-    name="QM"
+    A = SparseMatrixCOO(A),
+    lcon = b,
+    ucon = b,
+    lvar = l,
+    uvar = u,
+    c0 = 0.0,
+    name = "QM",
   )
   statsps = presolve(qp)
   psqp = statsps.solver_specific[:presolvedQM]