User callback and sc_opf renaming

.github/workflows/ci.yml

@@ -17,7 +17,7 @@ jobs:
       labels: cuda
     strategy:
       matrix:
-        julia-version: ['1']
+        julia-version: ['1.11']
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4

.github/workflows/docs.yml

@@ -13,7 +13,7 @@ jobs:
       labels: cuda
     strategy:
       matrix:
-        julia-version: ['1']
+        julia-version: ['1.11']
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/install-juliaup@v2

README.md

@@ -30,21 +30,21 @@ result = madnlp(model; tol=1e-6)
 #the other input data to generate a structure of named tuples which can then interface with 
 #ExaModels to generate the full model. We do not make any relaxations or decompositions for this problem
 
-model, sc_data, vars, lengths = scopf_model(
+model, sc_data, vars, lengths = goc3_model(
     "data/C3E4N00073D1_scenario_303.json", "data/C3E4N00073D1_scenario_303_solution.json"; 
     backend = CUDABackend()
 )
 result = madnlp(model; tol=1e-4)
 
 #Solution from GPU can be used to warm start a CPU solution or vice versa
-model_cpu, sc_data, vars, lengths = scopf_model(
+model_cpu, sc_data, vars, lengths = goc3_model(
     "data/C3E4N00073D1_scenario_303.json", "data/C3E4N00073D1_scenario_303_solution.json"; 
     result_set = [result, vars]
 )
 result_cpu = ipopt(model_cpu; tol=1e-8)
 
 #Additionally, the SC problem can be evaluated without contingencies
-model, sc_data, vars, lengths = scopf_model(
+model, sc_data, vars, lengths = goc3_model(
     "data/C3E4N00073D1_scenario_303.json", "data/C3E4N00073D1_scenario_303_solution.json"; 
     backend = CUDABackend(), include_ctg = false
 )