Merge branch 'main' into chore/nvskills-ci-cuopt-server-api-python

Author: ramakrishnap-nv

benchmarks/linear_programming/run_mps_files.sh

@@ -360,7 +360,7 @@ else
         mapfile -t mps_files < <(find "$MPS_DIR" -type f \( -name "*.mps" -o -name "*.MPS" -o -name "*.SIF" \) | sort)
     else
         # Gather .mps/.MPS and .SIF files in the directory
-        mapfile -t mps_files < <(ls "$MPS_DIR"/*.mps "$MPS_DIR"/*.MPS "$MPS_DIR"/*.SIF 2>/dev/null)
+        mapfile -t mps_files < <(ls "$MPS_DIR"/*.mps "$MPS_DIR"/*.MPS "$MPS_DIR"/*.SIF "$MPS_DIR"/*.mps.gz 2>/dev/null)
     fi
 
     echo "Found ${#mps_files[@]} .mps and .SIF files in $MPS_DIR"

cpp/src/barrier/barrier.cu

@@ -1715,6 +1715,26 @@ class iteration_data_t {
     cusparse_view.spmv(alpha, cusparse_u, beta, cusparse_v);
   }
 
+  // v = alpha * A * Dinv * A^T * y + beta * v. Simple interface (plain device vectors,
+  // no pre-built cusparse descriptors) so it can be used as the `a_multiply` callback of
+  // the generic iterative-refinement operator for the ADAT (non-augmented) solve path.
+  void gpu_adat_multiply_simple(f_t alpha,
+                                const rmm::device_uvector<f_t>& y,
+                                f_t beta,
+                                rmm::device_uvector<f_t>& v)
+  {
+    const i_t n = A.n;
+    rmm::device_uvector<f_t> u(n, stream_view_);
+    cusparse_view_.transpose_spmv(1.0, y, 0.0, u);
+    cub::DeviceTransform::Transform(cuda::std::make_tuple(u.data(), d_inv_diag.data()),
+                                    u.data(),
+                                    u.size(),
+                                    cuda::std::multiplies<>{},
+                                    stream_view_.value());
+    RAFT_CHECK_CUDA(stream_view_);
+    cusparse_view_.spmv(alpha, u, beta, v);
+  }
+
   // v = alpha * A * Dinv * A^T * y + beta * v
   void adat_multiply(f_t alpha,
                      const dense_vector_t<i_t, f_t>& y,
@@ -2947,6 +2967,37 @@ i_t barrier_solver_t<i_t, f_t>::gpu_compute_search_direction(iteration_data_t<i_
         settings.log.printf("Linear solve failed\n");
         return -1;
       }
+
+      // Iterative refinement on the ADAT (Schur-complement) system using GMRES.
+      // The direct Cholesky solve can degrade in accuracy on ill-conditioned D near
+      // convergence, as the diagonal D can span many orders of magnitude with small
+      // barrier parameter. In this case, we launch a GMRES-based iterative refinement
+      // loop for added robustness in the Schur-complement (ADAT) approach.
+      // GMRES can handle large, potentially ill-conditioned systems better than simple Richardson
+      // or classical iterative refinement, at the potential cost of higher computational work and
+      // memory. This is only used on the pure Schur-complement (n_dense_columns == 0).
+      if (settings.barrier_iterative_refinement && data.n_dense_columns == 0) {
+        struct adat_op_t {
+          adat_op_t(iteration_data_t<i_t, f_t>& data) : data_(data) {}
+          iteration_data_t<i_t, f_t>& data_;
+          void a_multiply(f_t alpha,
+                          const rmm::device_uvector<f_t>& x,
+                          f_t beta,
+                          rmm::device_uvector<f_t>& y) const
+          {
+            data_.gpu_adat_multiply_simple(alpha, x, beta, y);
+          }
+          void solve(rmm::device_uvector<f_t>& b, rmm::device_uvector<f_t>& x) const
+          {
+            data_.gpu_solve_adat(b, x);
+          }
+        } adat_op(data);
+        const f_t adat_solve_err =
+          iterative_refinement<i_t, f_t, adat_op_t>(adat_op, data.d_h_, data.d_dy_);
+        if (adat_solve_err > 1e-1) {
+          settings.log.printf("||ADAT*dy - h|| %e after IR\n", adat_solve_err);
+        }
+      }
     }  // Close NVTX range
 
     // y_residual <- ADAT*dy - h

cpp/src/branch_and_bound/symmetry.hpp

@@ -319,7 +319,7 @@ class orbital_fixing_t {
       node = node->parent;
     }
 
-    surviving_generators_.resize(max_generators_);
+    surviving_generators_.resize(symmetry->num_generators);
     std::iota(surviving_generators_.begin(), surviving_generators_.end(), 0);
 
     // Seed cumulative fixings from the parent's stored orbital fixings.

skills/cuopt-multi-objective-exploration/BENCHMARK.md

@@ -9,8 +9,8 @@ Recommended for publication based on the completed evaluation evidence in this r
 ## Evaluation Metadata
 
 - Skill: `cuopt-multi-objective-exploration`
-- Evaluation date: 2026-07-30
-- Evaluator version: `0.9.2`
+- Evaluation date: 2026-08-05
+- Evaluator version: `1.0.0`
 - Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
 - Tasks: 8 evaluation tasks (6 positive, 2 negative)
 - Dataset digest: `sha256:45bef5ee60d2a85ef8c4a7175c3d9a15eeac738e7bcf291d44de12ad4a35fff2` (skill-evaluator-dataset-snapshot/1)
@@ -34,12 +34,12 @@ The three-tier evaluation checks whether the skill:
 
 | Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
 |---|---:|---:|
-| Overall | 67% → 95% (+28 points) | 67% → 95% (+28 points) |
+| Overall | 65% → 97% (+33 points) | 67% → 90% (+23 points) |
 | Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
-| Correctness | 92% → 88% (-5 points) | 95% → 98% (+3 points) |
-| Discoverability | 31% → 100% (+69 points) | 44% → 95% (+52 points) |
-| Effectiveness | 79% → 87% (+9 points) | 72% → 82% (+10 points) |
-| Efficiency | 31% → 100% (+69 points) | 25% → 100% (+75 points) |
+| Correctness | 95% → 100% (+5 points) | 95% → 100% (+5 points) |
+| Discoverability | 25% → 100% (+75 points) | 44% → 84% (+40 points) |
+| Effectiveness | 79% → 87% (+8 points) | 73% → 85% (+12 points) |
+| Efficiency | 25% → 100% (+75 points) | 25% → 83% (+58 points) |
 
 **How to read this table:** baseline is the same task attempted without the target skill. Uplift is `skill score - baseline score`, shown in percentage points.
 

skills/cuopt-multi-objective-exploration/SKILL.md

@@ -14,6 +14,7 @@ metadata:
     - workflow
 ---
 
+
 # Multi-Objective Exploration
 
 

skills/cuopt-multi-objective-exploration/skill-card.md

@@ -9,7 +9,7 @@ NVIDIA <br>
 ### License/Terms of Use: <br>
 Apache 2.0 <br>
 ## Use Case: <br>
-Developers and engineers use this skill to explore multi-objective tradeoffs (cost vs. service level, return vs. risk, etc.) by orchestrating repeated cuOpt solves into a Pareto frontier, enabling informed decision-making across competing objectives. <br>
+Developers and engineers who need to explore multi-objective tradeoffs (cost vs. service level, return vs. risk, makespan vs. overtime) by generating and interpreting Pareto frontiers from sequences of cuOpt LP, MILP, or QP solves. <br>
 
 ### Deployment Geography for Use: <br>
 Global <br>
@@ -26,7 +26,7 @@ Mitigation: Review and scan skill before deployment. <br>
 
 ## Reference(s): <br>
 - [cuOpt User Guide](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html) <br>
-- [cuopt-examples (GitHub)](https://github.com/NVIDIA/cuopt-examples) <br>
+- [cuopt-examples](https://github.com/NVIDIA/cuopt-examples) <br>
 
 
 ## Skill Output: <br>
@@ -42,11 +42,11 @@ Mitigation: Review and scan skill before deployment. <br>
 
 
 ## Evaluation Tasks: <br>
-8 evaluation tasks (6 positive, 2 negative) in isolated sandbox pods. <br>
+8 evaluation tasks (6 positive, 2 negative) run in isolated sandbox pods. <br>
 
 ## Evaluation Metrics Used: <br>
 Reported benchmark dimensions: <br>
-- Security: Whether the skill avoids unsafe operations, secret leakage, and unauthorized access. <br>
+- Security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
 - Correctness: Final-answer correctness against the reference answer. <br>
 - Discoverability: Whether the expected skill was found and executed when needed. <br>
 - Effectiveness: Whether the skill helped complete the user's goal and expected workflow. <br>
@@ -65,12 +65,12 @@ Underlying evaluation signals used in this run: <br>
 ## Evaluation Results: <br>
 | Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
 |---|---:|---:|
-| Overall | 67% → 95% (+28 points) | 67% → 95% (+28 points) |
+| Overall | 65% → 97% (+33 points) | 67% → 90% (+23 points) |
 | Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
-| Correctness | 92% → 88% (-5 points) | 95% → 98% (+3 points) |
-| Discoverability | 31% → 100% (+69 points) | 44% → 95% (+52 points) |
-| Effectiveness | 79% → 87% (+9 points) | 72% → 82% (+10 points) |
-| Efficiency | 31% → 100% (+69 points) | 25% → 100% (+75 points) |
+| Correctness | 95% → 100% (+5 points) | 95% → 100% (+5 points) |
+| Discoverability | 25% → 100% (+75 points) | 44% → 84% (+40 points) |
+| Effectiveness | 79% → 87% (+8 points) | 73% → 85% (+12 points) |
+| Efficiency | 25% → 100% (+75 points) | 25% → 83% (+58 points) |
 
 ## Skill Version(s): <br>
 26.10.00 (source: frontmatter) <br>

skills/cuopt-multi-objective-exploration/skill.oms.sig

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