Initial commit: cuGenOpt GPU optimization solver

benchmark/experiments/e1_vs_mip/mip.py

@@ -0,0 +1,143 @@
+"""
+E1: GenSolver vs 通用 MIP (SCIP/CBC) — MIP 侧
+
+目的：与 gpu.cu 对比，展示 MIP 在复杂问题上的求解时间和质量
+实例：TSP (N=51,100,150), VRP (A-n32-k5)
+时间预算：1s, 10s, 60s
+
+用法：python mip.py
+"""
+import sys
+import os
+import time
+from ortools.linear_solver import pywraplp
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "common"))
+from instances import load_tsp, load_vrp, euc2d_dist_matrix, TSP_INSTANCES, VRP_INSTANCES
+
+TIME_BUDGETS = [1, 10, 60]
+
+
+def solve_tsp_mtz(dist, n, time_limit_sec, solver_id="SCIP"):
+    """TSP MTZ 公式"""
+    solver = pywraplp.Solver.CreateSolver(solver_id)
+    if not solver:
+        return float("inf"), 0.0, "error"
+
+    x = [[solver.IntVar(0, 1, f"x_{i}_{j}") for j in range(n)] for i in range(n)]
+    u = [solver.IntVar(0, n - 1, f"u_{i}") for i in range(n)]
+
+    for i in range(n):
+        solver.Add(x[i][i] == 0)
+    for i in range(n):
+        solver.Add(sum(x[i][j] for j in range(n)) == 1)
+    for j in range(n):
+        solver.Add(sum(x[i][j] for i in range(n)) == 1)
+    for i in range(1, n):
+        for j in range(1, n):
+            if i != j:
+                solver.Add(u[i] - u[j] + n * x[i][j] <= n - 1)
+
+    solver.Minimize(sum(dist[i][j] * x[i][j] for i in range(n) for j in range(n)))
+    solver.SetTimeLimit(int(time_limit_sec * 1000))
+
+    t0 = time.perf_counter()
+    status = solver.Solve()
+    elapsed_ms = (time.perf_counter() - t0) * 1000.0
+
+    if status in (pywraplp.Solver.OPTIMAL, pywraplp.Solver.FEASIBLE):
+        reason = "optimal" if status == pywraplp.Solver.OPTIMAL else "time"
+        return solver.Objective().Value(), elapsed_ms, reason
+    return float("inf"), elapsed_ms, "infeasible"
+
+
+def solve_vrp_mtz(dist, demands, n_nodes, n_vehicles, capacity, time_limit_sec, solver_id="SCIP"):
+    """VRP MTZ 公式（容量约束 + 子回路消除）"""
+    solver = pywraplp.Solver.CreateSolver(solver_id)
+    if not solver:
+        return float("inf"), 0.0, "error"
+
+    n = n_nodes
+    x = [[[solver.IntVar(0, 1, f"x_{k}_{i}_{j}")
+            for j in range(n)] for i in range(n)] for k in range(n_vehicles)]
+    u = [[solver.IntVar(0, n - 1, f"u_{k}_{i}")
+          for i in range(n)] for k in range(n_vehicles)]
+
+    # each customer visited exactly once
+    for j in range(1, n):
+        solver.Add(sum(x[k][i][j] for k in range(n_vehicles) for i in range(n) if i != j) == 1)
+
+    for k in range(n_vehicles):
+        # flow conservation
+        for j in range(n):
+            solver.Add(sum(x[k][i][j] for i in range(n) if i != j) ==
+                       sum(x[k][j][i] for i in range(n) if i != j))
+        # start/end at depot
+        solver.Add(sum(x[k][0][j] for j in range(1, n)) <= 1)
+        solver.Add(sum(x[k][j][0] for j in range(1, n)) <= 1)
+        # capacity
+        solver.Add(sum(demands[j] * sum(x[k][i][j] for i in range(n) if i != j)
+                       for j in range(1, n)) <= capacity)
+        # no self-loops
+        for i in range(n):
+            solver.Add(x[k][i][i] == 0)
+        # MTZ subtour elimination
+        for i in range(1, n):
+            for j in range(1, n):
+                if i != j:
+                    solver.Add(u[k][i] - u[k][j] + n * x[k][i][j] <= n - 1)
+
+    solver.Minimize(sum(dist[i][j] * x[k][i][j]
+                        for k in range(n_vehicles) for i in range(n) for j in range(n)))
+    solver.SetTimeLimit(int(time_limit_sec * 1000))
+
+    t0 = time.perf_counter()
+    status = solver.Solve()
+    elapsed_ms = (time.perf_counter() - t0) * 1000.0
+
+    if status in (pywraplp.Solver.OPTIMAL, pywraplp.Solver.FEASIBLE):
+        reason = "optimal" if status == pywraplp.Solver.OPTIMAL else "time"
+        return solver.Objective().Value(), elapsed_ms, reason
+    return float("inf"), elapsed_ms, "infeasible"
+
+
+def print_row(instance, config, obj, elapsed_ms, optimal, reason):
+    if obj == float("inf"):
+        print(f"{instance},{config},0,inf,0.00,{elapsed_ms:.1f},inf,0,{reason}")
+    else:
+        gap = (obj - optimal) / optimal * 100.0 if optimal > 0 else 0.0
+        print(f"{instance},{config},0,{obj:.2f},0.00,{elapsed_ms:.1f},{gap:.2f},0,{reason}")
+    sys.stdout.flush()
+
+
+def main():
+    print("instance,config,seed,obj,penalty,time_ms,gap_pct,generations,stop_reason")
+
+    tsp_targets = [e for e in TSP_INSTANCES if e["optimal"] <= 6528]  # eil51, kroA100, ch150
+    for entry in tsp_targets:
+        inst = load_tsp(entry)
+        print(f"  [e1-mip] TSP {inst['name']} (n={inst['n']})", file=sys.stderr)
+        dist = euc2d_dist_matrix(inst["coords"])
+
+        for solver_id in ["SCIP", "CBC"]:
+            for t in TIME_BUDGETS:
+                config = f"mip_{solver_id}_{t}s"
+                obj, ms, reason = solve_tsp_mtz(dist, inst["n"], t, solver_id)
+                print_row(inst["name"], config, obj, ms, inst["optimal"], reason)
+
+    for entry in VRP_INSTANCES:
+        inst = load_vrp(entry)
+        print(f"  [e1-mip] VRP {inst['name']} (n={inst['n']})", file=sys.stderr)
+        dist = euc2d_dist_matrix(inst["coords"])
+
+        for solver_id in ["SCIP"]:
+            for t in TIME_BUDGETS:
+                config = f"mip_{solver_id}_{t}s"
+                obj, ms, reason = solve_vrp_mtz(
+                    dist, inst["demands"], inst["n"],
+                    inst["n_vehicles"], inst["capacity"], t, solver_id)
+                print_row(inst["name"], config, obj, ms, inst["optimal"], reason)
+
+
+if __name__ == "__main__":
+    main()