/// <summary>
/// Solves the system of equations analytically.
/// </summary>
/// <param name="b">The b.</param>
/// <param name="IsASymmetric">if set to <c>true</c> [a is symmetric].</param>
/// <param name="potentialDiagonals">The potential diagonals.</param>
/// <returns>System.Double[].</returns>
public IList <double> SolveAnalytically(IList <double> b, bool IsASymmetric = false)
{
if (IsASymmetric)
{
if (ValuesChanged || TopologyChanged)
{
MatrixInCCS = convertToCCS(this);
}
if (TopologyChanged)
{
symbolicFactorizationMat = CSparse.SymbolicAnalysisLDL(MatrixInCCS);
}
if (ValuesChanged || TopologyChanged)
{
CSparse.FactorizeLDL(MatrixInCCS, symbolicFactorizationMat, out D, out FactorizationMatrix);
}
TopologyChanged = ValuesChanged = false;
return(CSparse.SolveLDL(b, FactorizationMatrix, D, symbolicFactorizationMat.InversePermute));
}
else
{
var ccs = convertToCCS(this);
var columnPermutation = ApproximateMinimumDegree.Generate(
new SymbolicColumnStorage(ccs), NumCols);
CompressedColumnStorage L, U;
int[] pinv;
// Numeric LU factorization
CSparse.FactorizeLU(ccs, columnPermutation, out L, out U, out pinv);
var x = CSparse.ApplyInverse(pinv, b, NumCols); // x = b(p)
CSparse.SolveLower(L, x); // x = L\x.
CSparse.SolveUpper(U, x); // x = U\x.
return(CSparse.ApplyInverse(columnPermutation, x, NumCols)); // b(q) = x
}
}
/// <summary>
/// Compute the numeric LDL' factorization of PAP'.
/// </summary>
/// <param name="A">The matrix, A.</param>
/// <param name="S">The symobolic factorization.</param>
/// <param name="D">The diagonals of LDL.</param>
/// <param name="L">The lower triangular matrix.</param>
/// <exception cref="System.Exception">Diagonal element is zero.</exception>
/// <exception cref="Exception">Diagonal element is zero.</exception>
internal static void FactorizeLDL(CompressedColumnStorage A, SymbolicFactorization S, out double[] D,
out CompressedColumnStorage L)
{
var n = A.ncols;
var aColPointers = A.ColumnPointers;
var aRowIndices = A.RowIndices;
var aValues = A.Values;
var parent = S.ParentIndices;
var P = S.PermutationVector;
var Pinv = S.InversePermute;
D = new double[n];
L = new CompressedColumnStorage(n, n, S.ColumnPointers[n]);
Array.Copy(S.ColumnPointers, L.ColumnPointers, n + 1);
var lColPointers = L.ColumnPointers;
var lRowIndices = L.RowIndices;
var lValues = L.Values;
// Workspace
var y = new double[n];
var pattern = new int[n];
var flag = new int[n];
var lnz = new int[n];
for (var k = 0; k < n; k++)
{
// compute nonzero Pattern of kth row of L, in topological order
y[k] = 0.0; // Y(0:k) is now all zero
var top = n;
flag[k] = k; // mark node k as visited
lnz[k] = 0; // count of nonzeros in column k of L
var kk = P != null ? P[k] : k;
var p2 = aColPointers[kk + 1];
for (var p = aColPointers[kk]; p < p2; p++)
{
var i = Pinv != null ? Pinv[aRowIndices[p]] : aRowIndices[p]; // get A(i,k)
if (i <= k)
{
y[i] += aValues[p]; // scatter A(i,k) into Y (sum duplicates)
int len;
for (len = 0; flag[i] != k; i = parent[i])
{
pattern[len++] = i; // L(k,i) is nonzero
flag[i] = k; // mark i as visited
}
while (len > 0)
{
pattern[--top] = pattern[--len];
}
}
}
// compute numerical values kth row of L (a sparse triangular solve)
D[k] = y[k]; // get D(k,k) and clear Y(k)
y[k] = 0.0;
for (; top < n; top++)
{
var i = pattern[top]; // Pattern [top:n-1] is pattern of L(:,k)
var yi = y[i];
y[i] = 0.0;
p2 = lColPointers[i] + lnz[i];
int p;
for (p = lColPointers[i]; p < p2; p++)
{
y[lRowIndices[p]] -= lValues[p] * yi;
}
var l_ki = yi / D[i];
D[k] -= l_ki * yi;
lRowIndices[p] = k; // store L(k,i) in column form of L
lValues[p] = l_ki;
lnz[i]++; // increment count of nonzeros in col i
}
if (D[k] == 0.0) // failure, D(k,k) is zero
{
throw new Exception("Diagonal element is zero.");
}
}
}
