public static void main(string[] args)
{
// create a random matrix that can be solved
int N = 5;
IMersenneTwister rand = new MersenneTwisterFast(234);
DMatrixSparseCSC A = RandomMatrices_DSCC.rectangle(N, N, N * N / 4, rand);
RandomMatrices_DSCC.ensureNotSingular(A, rand);
// Create the LU decomposition
LUSparseDecomposition_F64 <DMatrixSparseCSC> decompose =
DecompositionFactory_DSCC.lu(FillReducing.NONE);
// Decompose the matrix.
// If you care about the A matrix being modified call decompose.inputModified()
if (!decompose.decompose(A))
{
throw new InvalidOperationException("The matrix is singular");
}
// Extract new copies of the L and U matrices
DMatrixSparseCSC L = decompose.getLower(null);
DMatrixSparseCSC U = decompose.getUpper(null);
DMatrixSparseCSC P = decompose.getRowPivot(null);
// Storage for an intermediate step
DMatrixSparseCSC tmp = (DMatrixSparseCSC)A.createLike();
// Storage for the inverse matrix
DMatrixSparseCSC Ainv = (DMatrixSparseCSC)A.createLike();
// Solve for the inverse: P*I = L*U*inv(A)
TriangularSolver_DSCC.solve(L, true, P, tmp, null, null, null);
TriangularSolver_DSCC.solve(U, false, tmp, Ainv, null, null, null);
// Make sure the inverse has been found. A*inv(A) = identity should be an identity matrix
DMatrixSparseCSC found = (DMatrixSparseCSC)A.createLike();
CommonOps_DSCC.mult(A, Ainv, found);
found.print();
}
public static void main(String[] args)
{
IMersenneTwister rand = new MersenneTwisterFast(234);
// easy to work with sparse format, but hard to do computations with
DMatrixSparseTriplet work = new DMatrixSparseTriplet(5, 4, 5);
work.addItem(0, 1, 1.2);
work.addItem(3, 0, 3);
work.addItem(1, 1, 22.21234);
work.addItem(2, 3, 6);
// convert into a format that's easier to perform math with
DMatrixSparseCSC Z = ConvertDMatrixStruct.convert(work, (DMatrixSparseCSC)null);
// print the matrix to standard out in two different formats
Z.print();
Console.WriteLine();
Z.printNonZero();
Console.WriteLine();
// Create a large matrix that is 5% filled
DMatrixSparseCSC A = RandomMatrices_DSCC.rectangle(ROWS, COLS, (int)(ROWS * COLS * 0.05), rand);
// large vector that is 70% filled
DMatrixSparseCSC x = RandomMatrices_DSCC.rectangle(COLS, XCOLS, (int)(XCOLS * COLS * 0.7), rand);
Console.WriteLine("Done generating random matrices");
// storage for the initial solution
DMatrixSparseCSC y = new DMatrixSparseCSC(ROWS, XCOLS, 0);
DMatrixSparseCSC z = new DMatrixSparseCSC(ROWS, XCOLS, 0);
// To demonstration how to perform sparse math let's multiply:
// y=A*x
// Optional storage is set to null so that it will declare it internally
long before = DateTimeHelper.CurrentTimeMilliseconds;
IGrowArray workA = new IGrowArray(A.numRows);
DGrowArray workB = new DGrowArray(A.numRows);
for (int i = 0; i < 100; i++)
{
CommonOps_DSCC.mult(A, x, y, workA, workB);
CommonOps_DSCC.add(1.5, y, 0.75, y, z, workA, workB);
}
long after = DateTimeHelper.CurrentTimeMilliseconds;
Console.WriteLine("norm = " + NormOps_DSCC.fastNormF(y) + " sparse time = " + (after - before) + " ms");
DMatrixRMaj Ad = ConvertDMatrixStruct.convert(A, (DMatrixRMaj)null);
DMatrixRMaj xd = ConvertDMatrixStruct.convert(x, (DMatrixRMaj)null);
DMatrixRMaj yd = new DMatrixRMaj(y.numRows, y.numCols);
DMatrixRMaj zd = new DMatrixRMaj(y.numRows, y.numCols);
before = DateTimeHelper.CurrentTimeMilliseconds;
for (int i = 0; i < 100; i++)
{
CommonOps_DDRM.mult(Ad, xd, yd);
CommonOps_DDRM.add(1.5, yd, 0.75, yd, zd);
}
after = DateTimeHelper.CurrentTimeMilliseconds;
Console.WriteLine("norm = " + NormOps_DDRM.fastNormF(yd) + " dense time = " + (after - before) + " ms");
}
