初探java和matlab混合编程

最近要实现⼀个算法，需要⽤到矩阵运算，matlab的强项就是进⾏矩阵运算，所以要⾼效进⾏矩阵运算，就要在java中调⽤matlab。关于环境变量等的相关配置，请⼤家参考此⽂。

下⾯我要讲的例⼦是matlab⾃带的例⼦，位置在MATLABroot\\R2008a\oolbox\\javabuilder\\Examples。⼤家在matlab联机帮助⽂件中输⼊matlab build for java就能搜出此例⼦

个⼈认为这个例⼦⽐较精髓，通过这个例⼦可以完整的弄明⽩java是如何调⽤matlab进⾏矩阵运算的此例的java⽂件如下

代码

/* getfactor.java

* This file is used as an example for the MATLAB * Builder JA product. *

* Copyright 2001-2006 The MathWorks, Inc. */

/* Necessary package imports */

import com.mathworks.toolbox.javabuilder.*;import factormatrix.*;

/*

* getfactor class computes cholesky, LU, and QR * factorizations of a finite difference matrix * of order N. The value of N is passed on the * command line. If a second command line arg * is passed with the value of \"sparse\ * a sparse matrix is used. */

class getfactor{

public static void main(String[] args) {

MWNumericArray a = null; /* Stores matrix to factor */ Object[] result = null; /* Stores the result */

factor theFactor = null; /* Stores factor class instance */ try {

/* If no input, exit */ if (args.length == 0) {

System.out.println(\"Error: must input a positive integer\"); return; }

/* Convert input value */

int n = Integer.valueOf(args[0]).intValue();

if (n <= 0) {

System.out.println(\"Error: must input a positive integer\"); return; }

/*

* Allocate matrix. If second input is \"sparse\" * allocate a sparse array */

int[] dims = {n, n};

if (args.length > 1 && args[1].equals(\"sparse\"))

a = MWNumericArray.newSparse(dims[0], dims[1],n+2*(n-1), MWClassID.DOUBLE, MWComplexity.REAL); else

a = MWNumericArray.newInstance(dims,MWClassID.DOUBLE, MWComplexity.REAL); /* Set matrix values */ int[] index = {1, 1};

for (index[0] = 1; index[0] <= dims[0]; index[0]++) {

for (index[1] = 1; index[1] <= dims[1]; index[1]++) {

if (index[1] == index[0]) a.set(index, 2.0);

else if (index[1] == index[0]+1 || index[1] == index[0]-1) a.set(index, -1.0); } }

/* Create new factor object */ theFactor = new factor();

/* Print original matrix */

System.out.println(\"Original matrix:\"); System.out.println(a);

/* Compute cholesky factorization and print results. */ result = theFactor.cholesky(1, a);

System.out.println(\"Cholesky factorization:\"); System.out.println(result[0]); MWArray.disposeArray(result);

/* Compute LU factorization and print results. */ result = theFactor.ludecomp(2, a); System.out.println(\"LU factorization:\"); System.out.println(\"L matrix:\"); System.out.println(result[0]); System.out.println(\"U matrix:\"); System.out.println(result[1]); MWArray.disposeArray(result);

/* Compute QR factorization and print results. */ result = theFactor.qrdecomp(2, a);

System.out.println(\"QR factorization:\"); System.out.println(\"Q matrix:\"); System.out.println(result[0]); System.out.println(\"R matrix:\"); System.out.println(result[1]); }

catch (Exception e) {

System.out.println(\"Exception: \" + e.toString()); }

finally {

/* Free native resources */ MWArray.disposeArray(a); MWArray.disposeArray(result); if (theFactor != null) theFactor.dispose(); } }}