Initial commit: cuGenOpt GPU optimization solver

python/cugenopt/include/problems/tsp_xlarge.cuh

@@ -0,0 +1,99 @@
+/**
+ * tsp_xlarge.cuh - 超大规模 TSP 问题定义 (最多 512 城市)
+ * 
+ * 继承 ProblemBase，逻辑与 tsp_large.cuh 一致，D2=512
+ * 注意：距离矩阵 512×512×4B = 1MB，远超 48KB shared memory
+ *       因此 shared_mem_bytes() 返回 0，距离矩阵留在 global memory
+ */
+
+#pragma once
+#include "types.cuh"
+#include "cuda_utils.cuh"
+#include "operators.cuh"
+
+struct TSPXLargeProblem : ProblemBase<TSPXLargeProblem, 1, 512> {
+    const float* d_dist;
+    const float* h_dist;  // host 端距离矩阵（用于 init_relation_matrix）
+    int n;
+    
+    __device__ float calc_total_distance(const Sol& sol) const {
+        float total = 0.0f;
+        const int* route = sol.data[0];
+        int size = sol.dim2_sizes[0];
+        for (int i = 0; i < size; i++)
+            total += d_dist[route[i] * n + route[(i + 1) % size]];
+        return total;
+    }
+    
+    static constexpr ObjDef OBJ_DEFS[] = {
+        {ObjDir::Minimize, 1.0f, 0.0f},
+    };
+    __device__ float compute_obj(int idx, const Sol& sol) const {
+        switch (idx) {
+            case 0: return calc_total_distance(sol);
+            default: return 0.0f;
+        }
+    }
+    
+    __device__ float compute_penalty(const Sol& sol) const { return 0.0f; }
+    
+    ProblemConfig config() const {
+        ProblemConfig cfg;
+        cfg.encoding = EncodingType::Permutation;
+        cfg.dim1 = 1;  cfg.dim2_default = n;
+        fill_obj_config(cfg);
+        return cfg;
+    }
+    
+    // 距离矩阵太大，不放 shared memory
+    size_t shared_mem_bytes() const { return 0; }
+    __device__ void load_shared(char*, int, int) {}
+    
+    size_t working_set_bytes() const {
+        return (size_t)n * n * sizeof(float);
+    }
+    
+    // 用距离矩阵初始化 G/O 先验：距离近 → 分数高
+    void init_relation_matrix(float* G, float* O, int N) const {
+        if (!h_dist || N != n) return;
+        // 找最大距离用于归一化
+        float max_d = 0.0f;
+        for (int i = 0; i < N; i++)
+            for (int j = 0; j < N; j++)
+                if (h_dist[i * N + j] > max_d) max_d = h_dist[i * N + j];
+        if (max_d <= 0.0f) return;
+        
+        for (int i = 0; i < N; i++) {
+            for (int j = 0; j < N; j++) {
+                if (i == j) continue;
+                // 距离近 → G 高（分组倾向强）
+                float proximity = 1.0f - h_dist[i * N + j] / max_d;
+                G[i * N + j] = proximity * 0.3f;  // 初始信号不要太强，留空间给 EMA
+                // 距离近 → O 也给一点信号（对称的，不偏向任何方向）
+                O[i * N + j] = proximity * 0.1f;
+            }
+        }
+    }
+    
+    int heuristic_matrices(HeuristicMatrix* out, int max_count) const {
+        if (max_count < 1 || !h_dist) return 0;
+        out[0] = {h_dist, n};
+        return 1;
+    }
+    
+    static TSPXLargeProblem create(const float* h_dist_ptr, int n) {
+        TSPXLargeProblem prob;
+        prob.n = n;
+        prob.h_dist = h_dist_ptr;  // 保留 host 指针
+        float* dd;
+        CUDA_CHECK(cudaMalloc(&dd, sizeof(float) * n * n));
+        CUDA_CHECK(cudaMemcpy(dd, h_dist_ptr, sizeof(float) * n * n, cudaMemcpyHostToDevice));
+        prob.d_dist = dd;
+        return prob;
+    }
+    
+    void destroy() {
+        if (d_dist) { cudaFree(const_cast<float*>(d_dist)); d_dist = nullptr; }
+        h_dist = nullptr;
+    }
+};