update

2025.10.11_time_test_of_openblas_eigen_numpy/a.cpp

@@ -0,0 +1,35 @@
+#define EIGEN_USE_BLAS  // 注释或取消注释来测试
+#include <iostream>
+#include <chrono>
+#include <vector>
+#include <iomanip> 
+#include <Eigen/Dense>
+
+int main() {
+    std::vector<int> sizes = {100, 200, 300, 500, 1000, 2000, 3000, 5000};  // 要测试的不同矩阵大小 
+    const int trials = 3;  // 每个尺寸的测试次数
+
+    for (int size : sizes) {
+        std::cout << "Testing size: " << size << "x" << size << std::endl;
+        
+        Eigen::MatrixXd A = Eigen::MatrixXd::Random(size, size);
+        A = A.transpose() * A + Eigen::MatrixXd::Identity(size, size);  // 确保矩阵可逆
+
+        auto start = std::chrono::high_resolution_clock::now();
+        
+        for (int i = 0; i < trials; ++i) {
+            Eigen::MatrixXd A_inv = A.inverse();
+        }
+        
+        auto end = std::chrono::high_resolution_clock::now();
+        auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
+        
+        std::cout << "Average time per inversion: " 
+                  << std::fixed << std::setprecision(3) 
+                  << (static_cast<double>(duration.count()) / 1000 / trials) 
+                  << " s" << std::endl;
+        std::cout << "----------------------------------" << std::endl;
+    }
+
+    return 0;
+}

2025.10.11_time_test_of_openblas_eigen_numpy/a.py

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+import numpy as np
+import time
+
+sizes = [100, 200, 300, 500, 1000, 2000, 3000, 5000]
+trials = 3
+
+for size in sizes:
+    print(f"Testing size: {size}x{size}")
+    
+    A = np.random.rand(size, size)
+    A = A.T @ A + np.eye(size)
+    
+    start = time.time()
+    
+    for _ in range(trials):
+        A_inv = np.linalg.inv(A)
+    
+    end = time.time()
+    duration = end - start
+    
+    print(f"Average time per inversion: {duration/trials:.3f} s")
+    print("----------------------------------")