WIP

Author: michel2323

examples/scopf.jl

@@ -128,16 +128,14 @@ elseif mode == :twostage
     model, vars, cons, info = scopf_twostage_model(case, contingencies; form = form, backend = backend)
     @info "Model size" n_var = model.meta.nvar n_con = model.meta.ncon schur = info
 
-    # Schur KKT: the per-scenario sparse blocks are factorized by cuDSS on GPU
-    # (batched) / MUMPS on CPU internally; `linear_solver` here is for the
-    # first-stage Schur complement — LAPACK (dense) on CPU, cuDSS (sparse) on GPU.
-    # The GPU Schur complement is assembled as a sparse matrix (only the coupled
-    # base-dispatch design vars fill it) and factorized by cuDSS. The scenario
-    # dimensions and per-variable/per-constraint scenario tags come from the
-    # model's two-stage tags; the tags let the Schur solver partition the
-    # interleaved layout and fold the `nc_design` base-case design constraints
-    # into its first-stage block.
-    lin = backend === nothing ? MadNLP.LapackCPUSolver : MadNLPGPU.CUDSSSolver
+    # Schur KKT: both the per-scenario blocks AND the first-stage Schur complement
+    # are sparse — the complement fills only the coupled base-dispatch design vars.
+    # `linear_solver` here factorizes that sparse complement: MUMPS on CPU, cuDSS on
+    # GPU (both symmetric-indefinite, report inertia). The scenario dimensions and
+    # per-variable/per-constraint scenario tags come from the model's two-stage tags;
+    # the tags let the Schur solver partition the interleaved layout and fold the
+    # `nc_design` base-case design constraints into its first-stage block.
+    lin = backend === nothing ? MadNLP.MumpsSolver : MadNLPGPU.CUDSSSolver
     kkt_opts = schur_kkt_options(info, backend, cudss_ir)
     result = madnlp(model;
         callback = MadNLP.SparseCallback,
@@ -165,9 +163,9 @@ elseif mode == :compare
     pg_single = Array(solution(r1, v1.pg))[:, 1]
 
     m2, v2, _, info = scopf_twostage_model(case, contingencies; form = form, backend = backend)
-    # First-stage Schur complement solver: LAPACK (dense) on CPU, cuDSS (sparse) on
-    # GPU. The per-scenario sparse blocks use cuDSS/MUMPS internally.
-    lin = backend === nothing ? MadNLP.LapackCPUSolver : MadNLPGPU.CUDSSSolver
+    # Sparse first-stage Schur complement solver: MUMPS on CPU, cuDSS on GPU. The
+    # per-scenario sparse blocks use cuDSS/MUMPS internally.
+    lin = backend === nothing ? MadNLP.MumpsSolver : MadNLPGPU.CUDSSSolver
     r2 = madnlp(m2;
         callback = MadNLP.SparseCallback,
         kkt_system = MadNLP.SchurComplementKKTSystem,