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Remove usage of boldsymbol in formulas 

Only on a few places the `\boldsymbol` is used in formulas, this has been removed to make it consistent with other packages.
This commit is contained in:
albert-github 2023-05-18 12:46:58 +02:00
parent c36d6df775
commit 13ea7e90ee
2 changed files with 6 additions and 6 deletions
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2
Barycentric_coordinates_2/doc/Barycentric_coordinates_2/Barycentric_coordinates_2.txt
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@ -584,7 +584,7 @@ one should be cautious when using the unnormalized mean value weights. In that c
The harmonic coordinates are computed by solving the Laplace equation
<center>
\f$\Delta \boldsymbol{b} = \boldsymbol{0}\f$
\f$\Delta b = 0\f$
</center>
subject to suitable Dirichlet boundary conditions. Harmonic coordinates are the only coordinates

10
Shape_regularization/doc/Shape_regularization/Shape_regularization.txt
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@ -456,12 +456,12 @@ This framework follows Section 3 from \cgalCite{cgal:bl-kippi-18}, however the a
from that paper was extended and generalized. The idea behind the main algorithm is
to minimize the energy
<center>\f$U(\boldsymbol{x}) = (1 - \lambda) D(\boldsymbol{x}) + \lambda V(\boldsymbol{x})\f$,</center>
<center>\f$U(x) = (1 - \lambda) D(x) + \lambda V(x)\f$,</center>
where \f$\boldsymbol{x} = (x_1, \dots, x_n)\f$ is a configuration of perturbations operated
on \f$n\f$ input items, \f$D(\boldsymbol{x})\f$ and \f$V(\boldsymbol{x})\f$ represent a data
where \f$x = (x_1, \dots, x_n)\f$ is a configuration of perturbations operated
on \f$n\f$ input items, \f$D(x)\f$ and \f$V(x)\f$ represent a data
term and pairwise potential respectively, and \f$\lambda \in [0, 1]\f$ is a parameter weighting
these two terms. By setting up the correct types of \f$D(\boldsymbol{x})\f$ and \f$V(\boldsymbol{x})\f$,
these two terms. By setting up the correct types of \f$D(x)\f$ and \f$V(x)\f$,
the problem can be reformulated into a quadratic optimization problem with \f$(n + m)\f$ variables
and \f$2(n + m)\f$ linear constraints, where \f$m\f$ is the number of unique pairs formed by connecting
an item to one of its closest neighbors. Let us explain how it all works when the input items
@ -475,7 +475,7 @@ segment and \f$j\f$ is the index of the jth segment is inserted in the graph whe
This way each pair is inserted only once. The neighbors are found via the \ref QP_Regularization_Segments_Delaunay
"Delaunay Neighbor Query".
When we have the graph, we fill in the terms \f$D(\boldsymbol{x})\f$ and \f$V(\boldsymbol{x})\f$
When we have the graph, we fill in the terms \f$D(x)\f$ and \f$V(x)\f$
via the concept `RegularizationType`. First, we obtain a maximum perturbation bound for each segment
via the method `RegularizationType::bound()`. Since we want to rotate segments, we return here
the maximum allowed angle deviation for each segment with respect to its original orientation, lets