songsenand
/
cgal
mirror of https://github.com/CGAL/cgal
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Barycentric_coordinates_2/doc/Barycentric_coordinates_2/Barycentric_coordinates_2.txt
View File

@ -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 The harmonic coordinates are computed by solving the Laplace equation
<center> <center>
\f$\Delta \boldsymbol{b} = \boldsymbol{0}\f$ \f$\Delta b = 0\f$
</center> </center>
subject to suitable Dirichlet boundary conditions. Harmonic coordinates are the only coordinates subject to suitable Dirichlet boundary conditions. Harmonic coordinates are the only coordinates

10
Shape_regularization/doc/Shape_regularization/Shape_regularization.txt
View File

@ -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 from that paper was extended and generalized. The idea behind the main algorithm is
to minimize the energy 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 where \f$x = (x_1, \dots, x_n)\f$ 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 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 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 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 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 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 This way each pair is inserted only once. The neighbors are found via the \ref QP_Regularization_Segments_Delaunay
"Delaunay Neighbor Query". "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 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 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 the maximum allowed angle deviation for each segment with respect to its original orientation, lets