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Miscellaneous fixes.

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Change-Id: I521e11f2d20bf24960bbc6b5dab4ec8bb1503d23
This commit is contained in:
Sameer Agarwal
2012-08-30 10:26:44 -07:00
parent 1e3cbd9a44
commit 98bf14d2b9
3 changed files with 21 additions and 16 deletions
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docs/changes.tex
+3 -1
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@@ -12,6 +12,8 @@
TolerantLossFunction} and \texttt{ComposedLossFunction}. (James Roseborough).
\item New \texttt{DENSE\_NORMAL\_CHOLESKY} linear solver, which uses Eigen's
LDLT factorization on the normal equations.
\item Cached symbolic factorization when using \texttt{CXSparse}.
(Petter Strandark)
\item The traditional Dogleg solver now uses an elliptical trust
region (Markus Moll)
\item Support for returning initial and final gradients \& Jacobians.
@@ -38,7 +40,7 @@
\begin{itemize}
\item Do not link to \texttt{libgomp} when building on
windows. (Petter Strandmark)
\item Include \texttt{gflags.h} in \texttt{test_utils.cc}. (Petter
\item Include \texttt{gflags.h} in \texttt{test\_utils.cc}. (Petter
Strandmark)
\item Use standard random number generation routines. (Petter Strandmark)
\item \texttt{TrustRegionMinimizer} does not implicitly negate the
internal/ceres/cxsparse.cc
+12 -9
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@@ -48,7 +48,9 @@ CXSparse::~CXSparse() {
}
}
bool CXSparse::SolveCholesky(cs_di* A, cs_dis* factor, double* b) {
bool CXSparse::SolveCholesky(cs_di* A,
cs_dis* symbolic_factorization,
double* b) {
// Make sure we have enough scratch space available.
if (scratch_size_ < A->n) {
if (scratch_size_ > 0) {
@@ -58,28 +60,29 @@ bool CXSparse::SolveCholesky(cs_di* A, cs_dis* factor, double* b) {
}
// Solve using Cholesky factorization
csn* N = cs_chol(A, factor);
if (N == NULL) {
csn* numeric_factorization = cs_chol(A, symbolic_factorization);
if (numeric_factorization == NULL) {
LOG(WARNING) << "Cholesky factorization failed.";
return false;
}
// When the Cholesky factorization succeeded, these methods are guaranteed to
// succeed as well. In the comments below, "x" refers to the scratch space.
// succeeded as well. In the comments below, "x" refers to the scratch space.
//
// Set x = P * b.
cs_ipvec(factor->pinv, b, scratch_, A->n);
cs_ipvec(symbolic_factorization->pinv, b, scratch_, A->n);
// Set x = L \ x.
cs_lsolve(N->L, scratch_);
cs_lsolve(numeric_factorization->L, scratch_);
// Set x = L' \ x.
cs_ltsolve(N->L, scratch_);
cs_ltsolve(numeric_factorization->L, scratch_);
// Set b = P' * x.
cs_pvec(factor->pinv, scratch_, b, A->n);
cs_pvec(symbolic_factorization->pinv, scratch_, b, A->n);
// Free Cholesky factorization.
cs_nfree(N);
cs_nfree(numeric_factorization);
return true;
}
internal/ceres/cxsparse.h
+6 -6
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@@ -51,13 +51,13 @@ class CXSparse {
~CXSparse();
// Solves a symmetric linear system A * x = b using Cholesky factorization.
// A - The system matrix.
// factor - The symbolic factorization of A. This is obtained from
// AnalyzeCholesky.
// b - The right hand size of the linear equation. This array will also
// recieve the solution.
// A - The system matrix.
// symbolic_factorization - The symbolic factorization of A. This is obtained
// from AnalyzeCholesky.
// b - The right hand size of the linear equation. This
// array will also recieve the solution.
// Returns false if Cholesky factorization of A fails.
bool SolveCholesky(cs_di* A, cs_dis* factor, double* b);
bool SolveCholesky(cs_di* A, cs_dis* symbolic_factorization, double* b);
// Creates a sparse matrix from a compressed-column form. No memory is
// allocated or copied; the structure A is filled out with info from the