Examples of LUDecompositionQuick

cern.colt.matrix.linalg.LUDecompositionQuick
A low level version of {@link LUDecomposition}, avoiding unnecessary memory allocation and copying. The input to decompose methods is overriden with the result (LU). The input to solve methods is overriden with the result (X). In addition to LUDecomposition, this class also includes a faster variant of the decomposition, specialized for tridiagonal (and hence also diagonal) matrices, as well as a solver tuned for vectors. Its disadvantage is that it is a bit more difficult to use than LUDecomposition. Thus, you may want to disregard this class and come back later, if a need for speed arises.
An instance of this class remembers the result of its last decomposition. Usage pattern is as follows: Create an instance of this class, call a decompose method, then retrieve the decompositions, determinant, and/or solve as many equation problems as needed. Once another matrix needs to be LU-decomposed, you need not create a new instance of this class. Start again by calling a decompose method, then retrieve the decomposition and/or solve your equations, and so on. In case a LU matrix is already available, call method setLU instead of decompose and proceed with solving et al.
If a matrix shall not be overriden, use matrix.copy() and hand the the copy to methods.
For an m x n matrix A with m >= n, the LU decomposition is an m x n unit lower triangular matrix L, an n x n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U; If m < n, then L is m x m and U is m x n.
The LU decomposition with pivoting always exists, even if the matrix is singular, so the decompose methods will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. Attempting to solve such a system will throw an exception if isNonsingular() returns false.

Examples of cern.colt.matrix.linalg.LUDecompositionQuick

    for(int i=0;i<testMatrix.rows();++i)
      Assert.assertEquals(i+1, s.j_x[i],comparisonAccuracy);
    verifyAxb(s);
    
    // Test 2
    LUDecompositionQuick solver = new LUDecompositionQuick();
    solver.decompose(testMatrix);solver.setLU(testMatrix);
    solver.solve(x);
    for(int i=0;i<testMatrix.rows();++i)
      Assert.assertEquals(i+1, x.getQuick(i),comparisonAccuracy);
    verifyAxb(s);


    // Test 3

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Examples of cern.colt.matrix.linalg.LUDecompositionQuick

      }
    }, IllegalArgumentException.class,"singular");    


    checkForCorrectException(new whatToRun() {
      public void run() throws NumberFormatException {
        LUDecompositionQuick coltSolver = new LUDecompositionQuick();
        coltSolver.decompose(matrix);coltSolver.setLU(matrix);
        coltSolver.solve(x);
      }
    }, IllegalArgumentException.class,"singular");    


    checkForCorrectException(new whatToRun() {
      public void run() throws NumberFormatException {

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Examples of cern.colt.matrix.linalg.LUDecompositionQuick

    final DoubleMatrix1D b = DoubleFactory1D.dense.make(size);
    b.assign(randomGenerator);


    final LSolver solver = new LSolver(matrix,b);
    long tmStarted = new Date().getTime();
    LUDecompositionQuick coltSolver = new LUDecompositionQuick();
    coltSolver.decompose(matrix);coltSolver.setLU(matrix);
    
    DoubleMatrix1D vector = b.copy();
    coltSolver.solve(vector);
    long tmFinished = new Date().getTime();
    System.out.println(" time taken: "+((double)tmFinished-tmStarted)/1000);
    verifyAxb(matrix, b, vector);
    
    tmStarted = new Date().getTime();

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Examples of cern.colt.matrix.linalg.LUDecompositionQuick

    }
    
    if (!singular)
      TestSolver.verifyAxb(solver);
    
    LUDecompositionQuick coltSolver = new LUDecompositionQuick();
    coltSolver.decompose(matrix);coltSolver.setLU(matrix);
    DoubleMatrix1D vector = DoubleFactory1D.dense.make(matrix.rows(), 0);
    
    try
    {
      coltSolver.solve(vector);
    }
    catch(IllegalArgumentException ex)
    {
      if (ex.getMessage().contains("singular"))
        Assert.assertTrue(singular);

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Examples of cern.colt.matrix.linalg.LUDecompositionQuick

   * to Colt format, followed by running Colt itself. Hence all this is
   * extremely slow.
   */  
  public void solveUsingColt()
  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }

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