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

http://www.cnblogs.com/allanyz/archive/2009/05/04/1449081.html。

下面我要讲的例子是matlab自带的例子，位置在MATLABroot\R2008a\toolbox\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", then

* 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();

}

}

}