Shortened message printed for IndeterminantLinearSystemException.

gtsam/linear/linearExceptions.h

@@ -108,71 +108,8 @@ namespace gtsam {
 Thrown when a linear system is ill-posed.  The most common cause for this\n\
 error is having underconstrained variables.  Mathematically, the system is\n\
 either underdetermined, or its quadratic error function is concave in some\n\
-directions.\n\
-\n\
-Examples of situations causing this error are:\n\
-- A landmark observed by two cameras with a very small baseline will have\n\
-  high uncertainty in its distance from the cameras but low uncertainty\n\
-  in its bearing, creating a poorly-conditioned system.\n\
-- A landmark observed by only a single ProjectionFactor, RangeFactor, or\n\
-  BearingFactor (the landmark is not completely constrained).\n\
-- An overall scale or rigid transformation ambiguity, for example missing\n\
-  a prior or hard constraint on the first pose, or missing a scale\n\
-  constraint between the first two cameras (in structure-from-motion).\n\
-\n\
-Mathematically, the following conditions cause this problem:\n\
-- Underdetermined system:  This occurs when the variables are not\n\
-  completely constrained by factors.  Even if all variables are involved\n\
-  in factors, the variables can still be numerically underconstrained,\n\
-  (for example, if a landmark is observed by only one ProjectionFactor).\n\
-  Mathematically in this case, the rank of the linear Jacobian or Hessian\n\
-  is less than the number of scalars in the system.\n\
-- Indefinite system:  This condition occurs when the system Hessian is\n\
-  indefinite, i.e. non-positive-semidefinite.  Note that this condition\n\
-  can also indicate an underdetermined system, but when solving with\n\
-  Cholesky, accumulation of numerical errors can cause the system to\n\
-  become indefinite (have some negative eigenvalues) even if it is in\n\
-  reality positive semi-definite (has some near-zero positive\n\
-  eigenvalues).  Alternatively, if the system contains some\n\
-  HessianFactor's with negative eigenvalues, these can create a truly\n\
-  indefinite system, whose quadratic error function has negative\n\
-  curvature in some directions.\n\
-- Poorly-conditioned system:  A system that is positive-definite (has a\n\
-  unique solution) but is numerically ill-conditioned can accumulate\n\
-  numerical errors during solving that cause the system to become\n\
-  indefinite later during the elimination process.  Note that poorly-\n\
-  conditioned systems will usually have inaccurate solutions, even if\n\
-  they do not always trigger this error.  Systems with almost-\n\
-  unconstrained variables or vastly different measurement uncertainties\n\
-  or variable units can be poorly-conditioned.\n\
-\n\
-Resolving this problem:\n\
-- This exception contains the variable at which the problem was\n\
-  discovered (IndeterminantLinearSystemException::nearbyVariable()).\n\
-  Note, however, that this is not necessarily the variable where the\n\
-  problem originates.  For example, in the case that a prior on the\n\
-  first camera was forgotten, it may only be another camera or landmark\n\
-  where the problem can be detected.  Where the problem is detected\n\
-  depends on the graph structure and variable elimination ordering.\n\
-- MATLAB (or similar software) is a useful tool to diagnose this problem.\n\
-  Use GaussianFactorGraph::sparseJacobian_(),\n\
-  GaussianFactorGraph::sparseJacobian()\n\
-  GaussianFactorGraph::denseJacobian(), and\n\
-  GaussianFactorGraph::denseHessian() to output the linear graph in\n\
-  matrix format.  If using the MATLAB wrapper, the returned matrices can\n\
-  be immediately inspected and analyzed using standard methods.  If not\n\
-  using the MATLAB wrapper, the output of these functions may be written\n\
-  to text files and then loaded into MATLAB.\n\
-- When outputting linear graphs as Jacobian matrices, rows are ordered in\n\
-  the same order as factors in the graph, which each factor occupying the\n\
-  number of rows indicated by its JacobianFactor::rows() function.  Each\n\
-  column appears in elimination ordering, as in the Ordering class.  Each\n\
-  variable occupies the number of columns indicated by the\n\
-  JacobianFactor::getDim() function.\n\
-- When outputting linear graphs as Hessian matrices, rows and columns are\n\
-  ordered in elimination order and occupy scalars in the same way as\n\
-  described for Jacobian columns in the previous bullet.\
-";
+directions.  See the GTSAM Doxygen documentation at http://borg.cc.gatech.edu/ \n\
+on gtsam::IndeterminantLinearSystemException for more information.\n";
       return what_.c_str();
     }
   };