Initial commit: cuGenOpt GPU optimization solver

python/cugenopt/include/problems/assignment.cuh

@@ -0,0 +1,114 @@
+/**
+ * assignment.cuh - 指派问题
+ * 
+ * 继承 ProblemBase，使用 ObjDef 目标注册机制
+ */
+
+#pragma once
+#include "types.cuh"
+#include "cuda_utils.cuh"
+#include "operators.cuh"
+
+struct AssignmentProblem : ProblemBase<AssignmentProblem, 1, 16> {
+    const float* d_cost;
+    const float* h_cost;  // host 端成本矩阵（用于 init_relation_matrix）
+    int n;
+    
+    // ---- 目标计算 ----
+    __device__ float calc_total_cost(const Sol& sol) const {
+        float total = 0.0f;
+        const int* assign = sol.data[0];
+        int size = sol.dim2_sizes[0];
+        for (int i = 0; i < size; i++)
+            total += d_cost[i * n + assign[i]];
+        return total;
+    }
+    
+    // ---- 目标定义（OBJ_DEFS 与 compute_obj 必须一一对应）----
+    static constexpr ObjDef OBJ_DEFS[] = {
+        {ObjDir::Minimize, 1.0f, 0.0f},   // case 0: calc_total_cost
+    };
+    __device__ float compute_obj(int idx, const Sol& sol) const {
+        switch (idx) {
+            case 0: return calc_total_cost(sol);   // OBJ_DEFS[0]
+            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;
+    }
+    
+    // ---- shared memory 接口 ----
+    static constexpr size_t SMEM_LIMIT = 48 * 1024;
+    
+    size_t shared_mem_bytes() const {
+        size_t need = (size_t)n * n * sizeof(float);
+        return need <= SMEM_LIMIT ? need : 0;
+    }
+    
+    size_t working_set_bytes() const {
+        return (size_t)n * n * sizeof(float);
+    }
+    
+    __device__ void load_shared(char* smem, int tid, int bsz) {
+        float* sc = reinterpret_cast<float*>(smem);
+        int total = n * n;
+        for (int i = tid; i < total; i += bsz) sc[i] = d_cost[i];
+        d_cost = sc;
+    }
+    
+    // 成本先验：task j 和 task k 如果被相似 agent 偏好，G 值高
+    // O 矩阵：task j 在位置 i 成本低 → O[j][k] 略高（j 倾向排在 k 前面的位置）
+    void init_relation_matrix(float* G, float* O, int N) const {
+        if (!h_cost || N != n) return;
+        // 对每个 task，构建成本向量，task 间余弦相似度 → G
+        // 简化：成本列向量的相关性
+        float max_c = 0.0f;
+        for (int i = 0; i < N * N; i++)
+            if (h_cost[i] > max_c) max_c = h_cost[i];
+        if (max_c <= 0.0f) return;
+        
+        for (int j = 0; j < N; j++)
+            for (int k = 0; k < N; k++) {
+                if (j == k) continue;
+                // G: 两个 task 的成本向量越相似 → 越可能互换
+                float dot = 0.0f, nj = 0.0f, nk = 0.0f;
+                for (int i = 0; i < N; i++) {
+                    float cj = h_cost[i * N + j] / max_c;
+                    float ck = h_cost[i * N + k] / max_c;
+                    dot += cj * ck;
+                    nj += cj * cj;
+                    nk += ck * ck;
+                }
+                float denom = sqrtf(nj) * sqrtf(nk);
+                float sim = (denom > 1e-6f) ? dot / denom : 0.0f;
+                G[j * N + k] = sim * 0.2f;
+                O[j * N + k] = sim * 0.05f;
+            }
+    }
+    
+    static AssignmentProblem create(const float* hc, int n) {
+        AssignmentProblem prob;
+        prob.n = n;
+        prob.h_cost = hc;
+        float* dc;
+        CUDA_CHECK(cudaMalloc(&dc, sizeof(float)*n*n));
+        CUDA_CHECK(cudaMemcpy(dc, hc, sizeof(float)*n*n, cudaMemcpyHostToDevice));
+        prob.d_cost = dc;
+        return prob;
+    }
+    
+    void destroy() {
+        if (d_cost) { cudaFree(const_cast<float*>(d_cost)); d_cost = nullptr; }
+        h_cost = nullptr;
+    }
+};