Examples of Jama.jMatrix

Jama.jMatrix
Jama = Java Matrix class.
The Java Matrix Class provides the fundamental operations of numerical linear algebra. Various constructors create Matrices from two dimensional arrays of double precision floating point numbers. Various "gets" and "sets" provide access to submatrices and matrix elements. Several methods implement basic matrix arithmetic, including matrix addition and multiplication, matrix norms, and element-by-element array operations. Methods for reading and printing matrices are also included. All the operations in this version of the Matrix Class involve real matrices. Complex matrices may be handled in a future version.
Five fundamental matrix decompositions, which consist of pairs or triples of matrices, permutation vectors, and the like, produce results in five decomposition classes. These decompositions are accessed by the Matrix class to compute solutions of simultaneous linear equations, determinants, inverses and other matrix functions. The five decompositions are:
- Cholesky Decomposition of symmetric, positive definite matrices.
- LU Decomposition of rectangular matrices.
- QR Decomposition of rectangular matrices.
- Singular Value Decomposition of rectangular matrices.
- Eigenvalue Decomposition of both symmetric and nonsymmetric square matrices.
Example of use:
Solve a linear system A x = b and compute the residual norm, ||b - A x||.
```
 double[][] vals = {{1.,2.,3},{4.,5.,6.},{7.,8.,10.}}; Matrix A = new Matrix(vals); Matrix b = Matrix.random(3,1); Matrix x = A.solve(b); Matrix r = A.times(x).minus(b); double rnorm = r.normInf(); 
```
@author The MathWorks, Inc. and the National Institute of Standards and Technology. @version 5 August 1998

    checkColumnDimension(v1, v1.length);
    return QR(v1).solve(jMatrix.identity(v1.length, v1.length)).getArray();
  }


  public static double[][] inverse(double[][] v1) {
    return new jMatrix(v1).inverse().getArray();
  }

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  public static double[][] inverse(double[][] v1) {
    return new jMatrix(v1).inverse().getArray();
  }


  public static double[][] solve(double[][] A, double[][] B) {
    return new jMatrix(A).solve(new jMatrix(B)).getArray();
  }

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  public static Jama.JamaLUDecomposition LU(double[][] v) {
    return new Jama.JamaLUDecomposition(jMatrix.constructWithCopy(v));
  }


  public static Jama.JamaCholeskyDecomposition cholesky(double[][] v) {
    return new Jama.JamaCholeskyDecomposition(new jMatrix(v));
  }

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  public static Jama.JamaCholeskyDecomposition cholesky(double[][] v) {
    return new Jama.JamaCholeskyDecomposition(new jMatrix(v));
  }


  public static Jama.JamaSingularValueDecomposition singular(double[][] v) {
    return new Jama.JamaSingularValueDecomposition(new jMatrix(v));
  }

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  public static Jama.JamaSingularValueDecomposition singular(double[][] v) {
    return new Jama.JamaSingularValueDecomposition(new jMatrix(v));
  }


  public static double cond(double[][] v) {
    return new jMatrix(v).cond();
  }

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  public static double cond(double[][] v) {
    return new jMatrix(v).cond();
  }


  public static double det(double[][] v) {
    return new jMatrix(v).det();
  }

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  public static double det(double[][] v) {
    return new jMatrix(v).det();
  }


  public static int rank(double[][] v) {
    return new jMatrix(v).rank();
  }

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  public static int rank(double[][] v) {
    return new jMatrix(v).rank();
  }


  public static double trace(double[][] v) {
    return new jMatrix(v).trace();
  }

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  public static double trace(double[][] v) {
    return new jMatrix(v).trace();
  }


  public static double norm1(double[][] v) {
    return new jMatrix(v).norm1();
  }

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  public static double norm1(double[][] v) {
    return new jMatrix(v).norm1();
  }


  public static double norm2(double[][] v) {
    return new jMatrix(v).norm2();
  }

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