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-rw-r--r--Eigen/src/IterativeLinearSolvers/ConjugateGradient.h26
1 files changed, 20 insertions, 6 deletions
diff --git a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
index f64f2534d..a74a8155e 100644
--- a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -41,15 +41,29 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
int n = mat.cols();
VectorType residual = rhs - mat * x; //initial residual
- VectorType p(n);
+ RealScalar rhsNorm2 = rhs.squaredNorm();
+ if(rhsNorm2 == 0)
+ {
+ x.setZero();
+ iters = 0;
+ tol_error = 0;
+ return;
+ }
+ RealScalar threshold = tol*tol*rhsNorm2;
+ RealScalar residualNorm2 = residual.squaredNorm();
+ if (residualNorm2 < threshold)
+ {
+ iters = 0;
+ tol_error = sqrt(residualNorm2 / rhsNorm2);
+ return;
+ }
+
+ VectorType p(n);
p = precond.solve(residual); //initial search direction
VectorType z(n), tmp(n);
- RealScalar absNew = internal::real(residual.dot(p)); // the square of the absolute value of r scaled by invM
- RealScalar rhsNorm2 = rhs.squaredNorm();
- RealScalar residualNorm2 = 0;
- RealScalar threshold = tol*tol*rhsNorm2;
+ RealScalar absNew = numext::real(residual.dot(p)); // the square of the absolute value of r scaled by invM
int i = 0;
while(i < maxIters)
{
@@ -66,7 +80,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
z = precond.solve(residual); // approximately solve for "A z = residual"
RealScalar absOld = absNew;
- absNew = internal::real(residual.dot(z)); // update the absolute value of r
+ absNew = numext::real(residual.dot(z)); // update the absolute value of r
RealScalar beta = absNew / absOld; // calculate the Gram-Schmidt value used to create the new search direction
p = z + beta * p; // update search direction
i++;