Merge pull request #442 from metab0t/la_functions

Use dot and tr from LinearAlgebra instead of JuMP.

Author: chriscoey

src/Cones/Cones.jl

@@ -46,7 +46,7 @@ function dot_expr(
     vars::AbstractVecOrMat{<:Union{VR,AE}},
     opt::Optimizer,
 )
-    return JuMP.@expression(opt.oa_model, JuMP.dot(z, vars))
+    return JuMP.@expression(opt.oa_model, LinearAlgebra.dot(z, vars))
 end
 
 function clean_array!(z::AbstractArray)

src/Cones/secondordercone.jl

@@ -88,7 +88,7 @@ function _get_cuts(
     p = LinearAlgebra.norm(r)
     u = cache.oa_s[1]
     @views w = cache.oa_s[2:end]
-    cut = JuMP.@expression(opt.oa_model, p * u + JuMP.dot(r, w))
+    cut = JuMP.@expression(opt.oa_model, p * u + LinearAlgebra.dot(r, w))
     return [cut]
 end
 

src/models.jl

@@ -12,15 +12,15 @@ function setup_models(opt::Optimizer)
     # mixed-integer OA model, add discrete constraints later
     oa_model = opt.oa_model = JuMP.Model(() -> opt.oa_opt)
     oa_x = opt.oa_x = JuMP.@variable(oa_model, [1:length(opt.c)])
-    JuMP.@objective(oa_model, Min, JuMP.dot(opt.c, oa_x))
+    JuMP.@objective(oa_model, Min, LinearAlgebra.dot(opt.c, oa_x))
     JuMP.@constraint(oa_model, opt.A * oa_x .== opt.b)
     oa_aff = JuMP.@expression(oa_model, opt.h - opt.G * oa_x)
 
     if opt.solve_relaxation
         # continuous relaxation model
         relax_model = opt.relax_model = JuMP.Model(() -> opt.conic_opt)
         relax_x = opt.relax_x = JuMP.@variable(relax_model, [1:length(opt.c)])
-        JuMP.@objective(relax_model, Min, JuMP.dot(opt.c, relax_x))
+        JuMP.@objective(relax_model, Min, LinearAlgebra.dot(opt.c, relax_x))
         JuMP.@constraint(
             relax_model,
             opt.b - opt.A * relax_x in MOI.Zeros(length(opt.b))
@@ -48,7 +48,7 @@ function setup_models(opt::Optimizer)
         # continuous subproblem model
         subp_model = opt.subp_model = JuMP.Model(() -> opt.conic_opt)
         subp_x = opt.subp_x = JuMP.@variable(subp_model, [1:num_cont_vars])
-        JuMP.@objective(subp_model, Min, JuMP.dot(c_cont, subp_x))
+        JuMP.@objective(subp_model, Min, LinearAlgebra.dot(c_cont, subp_x))
         K0 = MOI.Zeros(length(opt.b_cont))
         opt.subp_eq = JuMP.@constraint(subp_model, -A_cont * subp_x in K0)
         subp_aff = JuMP.@expression(subp_model, -G_cont * subp_x)

test/JuMP_tests.jl

@@ -343,8 +343,8 @@ function _psd2(opt)
     m = JuMP.Model(opt)
 
     X = JuMP.@variable(m, [1:d, 1:d], PSD)
-    JuMP.@objective(m, Max, JuMP.dot(mat, X))
-    JuMP.@constraint(m, JuMP.tr(X) == 1)
+    JuMP.@objective(m, Max, LinearAlgebra.dot(mat, X))
+    JuMP.@constraint(m, LinearAlgebra.tr(X) == 1)
     JuMP.optimize!(m)
     @test JuMP.termination_status(m) == MOI.OPTIMAL
     @test JuMP.primal_status(m) == MOI.FEASIBLE_POINT