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Index refactoring: StorageIndex must be used for storage only (and locally when it make sense). In all other cases use the global Index type.

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This commit is contained in:
Gael Guennebaud
2015-02-13 18:57:41 +01:00
parent fe51319980
commit fc202bab39
78 changed files with 514 additions and 564 deletions
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8
Eigen/src/SparseCholesky/SimplicialCholesky.h
View File

@@ -92,8 +92,8 @@ class SimplicialCholeskyBase : public SparseSolverBase<Derived>
Derived& derived() { return *static_cast<Derived*>(this); }
const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline StorageIndex cols() const { return m_matrix.cols(); }
inline StorageIndex rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index rows() const { return m_matrix.rows(); }
/** \brief Reports whether previous computation was successful.
*
@@ -108,12 +108,12 @@ class SimplicialCholeskyBase : public SparseSolverBase<Derived>
/** \returns the permutation P
* \sa permutationPinv() */
const PermutationMatrix<Dynamic,Dynamic,Index>& permutationP() const
const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationP() const
{ return m_P; }
/** \returns the inverse P^-1 of the permutation P
* \sa permutationP() */
const PermutationMatrix<Dynamic,Dynamic,Index>& permutationPinv() const
const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationPinv() const
{ return m_Pinv; }
/** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.

20
Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
View File

@@ -57,7 +57,7 @@ void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrix
ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0);
for(StorageIndex k = 0; k < size; ++k)
for(Index k = 0; k < size; ++k)
{
/* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
m_parent[k] = -1; /* parent of k is not yet known */
@@ -104,7 +104,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
eigen_assert(ap.rows()==ap.cols());
const StorageIndex size = ap.rows();
const Index size = ap.rows();
eigen_assert(m_parent.size()==size);
eigen_assert(m_nonZerosPerCol.size()==size);
@@ -119,20 +119,20 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
bool ok = true;
m_diag.resize(DoLDLT ? size : 0);
for(StorageIndex k = 0; k < size; ++k)
for(Index k = 0; k < size; ++k)
{
// compute nonzero pattern of kth row of L, in topological order
y[k] = 0.0; // Y(0:k) is now all zero
StorageIndex top = size; // stack for pattern is empty
Index top = size; // stack for pattern is empty
tags[k] = k; // mark node k as visited
m_nonZerosPerCol[k] = 0; // count of nonzeros in column k of L
for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
{
StorageIndex i = it.index();
Index i = it.index();
if(i <= k)
{
y[i] += numext::conj(it.value()); /* scatter A(i,k) into Y (sum duplicates) */
StorageIndex len;
Index len;
for(len = 0; tags[i] != k; i = m_parent[i])
{
pattern[len++] = i; /* L(k,i) is nonzero */
@@ -149,7 +149,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
y[k] = 0.0;
for(; top < size; ++top)
{
StorageIndex i = pattern[top]; /* pattern[top:n-1] is pattern of L(:,k) */
Index i = pattern[top]; /* pattern[top:n-1] is pattern of L(:,k) */
Scalar yi = y[i]; /* get and clear Y(i) */
y[i] = 0.0;
@@ -160,8 +160,8 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
else
yi = l_ki = yi / Lx[Lp[i]];
StorageIndex p2 = Lp[i] + m_nonZerosPerCol[i];
StorageIndex p;
Index p2 = Lp[i] + m_nonZerosPerCol[i];
Index p;
for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
y[Li[p]] -= numext::conj(Lx[p]) * yi;
d -= numext::real(l_ki * numext::conj(yi));
@@ -180,7 +180,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
}
else
{
StorageIndex p = Lp[k] + m_nonZerosPerCol[k]++;
Index p = Lp[k] + m_nonZerosPerCol[k]++;
Li[p] = k ; /* store L(k,k) = sqrt (d) in column k */
if(d <= RealScalar(0)) {
ok = false; /* failure, matrix is not positive definite */