Commons math performance optimization technique: improve the calculation speed of mathematics tools in the Java class library

Commons math performance optimization technique: improve the calculation speed of mathematics tools in the Java class library Abstract: The mathematical tools in the Java class library are essential for many science, engineering and data analysis.Improving the calculation speed of these tools is essential for optimization performance.This article will introduce some commonly used performance optimization techniques to help you accelerate the process of using the Apache Commons Math library for mathematics calculation. 1. Use native data types: When you need to process large data sets, use the native data types as much as possible, such as int, double, etc.The processing speed of the native data type is much faster than the object type, which can significantly improve the operating efficiency.For example, when the circular iteration is calculated, the native integer variable is used instead of the Integer object. // Use native integer variables int sum = 0; for (int i = 0; i < array.length; i++) { sum += array[i]; } // Avoid using object types Integer sum = 0; for (Integer number : array) { sum += number; } 2. Use efficient data structure: According to specific requirements, choosing a suitable data structure can reduce the computing time complexity.For example, if you need to insert and delete frequently, using LinkedList instead of ArrayList will be more efficient. // Use linkedList for frequent insertion and delete operations List<Integer> list = new LinkedList<>(); list.add(2); // O(1) list.add(5); // O(1) list.remove(0); // O(n) for ArrayList, but O(1) for LinkedList 3. Use parallel computing: For some complicated computing tasks, the processing process can be accelerated with multi -threaded or parallel computing.Using Java's parallel streams or ThreadPoolexecutor can easily implement parallel calculations. // Use parallel flow operation to perform parallel calculation (find average) List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); double average = numbers.parallelStream().mapToInt(Integer::intValue).average().getAsDouble(); // Use the thread pool for parallel calculation ExecutorService executorService = Executors.newFixedThreadPool(4); List<Future<Double>> results = new ArrayList<>(); for (Integer number : numbers) { Callable<Double> task = () -> Math.sqrt(number); Future<Double> result = executorService.submit(task); results.add(result); } executorService.shutdown(); // Processing parallel calculation results double sum = 0; for (Future<Double> result : results) { sum += result.get(); } double average = sum / results.size(); 4. Data type conversion optimization: In the data type conversion, try to avoid using inefficient conversion methods.For example, when the double is converted to int, the math.round () method is used instead of directly compulsory type conversion. double num = 5.8; // Convert to int using math.round () to int int roundedNum = (int) Math.round(num); // Avoid direct compulsory type conversion int roundedNum = (int) num; 5. Cycle optimization: When performing cyclic calculations, try to move the excess calculation in the cycle as much as possible to the outside of the cycle.This optimization method can reduce the number of cycles and repeated calculations and improve efficiency. // The error cycle optimization method double sum = 0; for (int i = 0; i < array.length; i++) { for (int j = 0; j < 10; j++) { SUM += Array [i] * j; // Surgery calculation } } // The correct cycle optimization method double sum = 0; for (int i = 0; i < array.length; i++) { int value = Array [i]; // Single calculation outside the cycle for (int j = 0; j < 10; j++) { sum += value * j; } } Conclusion: Through the above performance optimization skills, you can effectively improve the speed of using the Apache Commons Math library for mathematical calculation.Selecting appropriate data types, data structures and computing strategies, and the optimization of cycle and data type conversion can have a significant impact on performance.Although the focus of this article is the Commons Math library, these techniques are also applicable to the performance optimization of other Java mathematical libraries. Note: The above code is only an example. In actual applications, please make appropriate modification and optimization according to the specific situation.