* \end{pmatrix}
* \f]
*
- * with determinant of the Jacobian equal to $J= \beta \hat \x^{2\beta-1}$.
+ * with determinant of the Jacobian equal to $J= \beta \hat x^{2\beta-1}$.
* Such transformation maps the reference square $[0,1]\times[0,1]$ to the
* reference simplex, by collapsing the left side of the square and squeezing
* quadrature points towards the origin, and then shearing the resulting
/**
* Access the real parts of solutions for a solved eigenvector problem,
- * pair index solutions, $\text{index}\,\in\,0\hdots
+ * pair index solutions, $\text{index}\,\in\,0\dots
* \text{n\_converged}-1$.
*/
void
/**
* Access the real and imaginary parts of solutions for a solved
- * eigenvector problem, pair index solutions, $\text{index}\,\in\,0\hdots
+ * eigenvector problem, pair index solutions, $\text{index}\,\in\,0\dots
* \text{n\_converged}-1$.
*/
void
* \f[
* G(z_i) \dealcoloneq M z_i − h_n A^I_{i,i} f_I (t^I_{n,i}, z_i) − a_i = 0
* \f]
- * must be solved for each stage $z_i , i = 1, \ldot, s$, where
+ * must be solved for each stage $z_i , i = 1, \ldots, s$, where
* we have the data
* \f[
* a_i \dealcoloneq
* \end{cases}
* \f]
*
- * where $y,\dot y$ are vectors in $\R^n$, $t$ is often the time (but can
- * also be a parametric quantity), and
- * $F:\R\times\R^n\times\R^n\rightarrow\R^n$. Such problem is solved
- * using Newton iteration augmented with a line search global
- * strategy. The integration method used in IDA is the variable-order,
+ * where $y,\dot y$ are vectors in $\mathbb{R}^n$, $t$ is often the time (but
+ * can also be a parametric quantity), and
+ * $F:\mathbb{R}\times\mathbb{R}^n\times \mathbb{R}^n\rightarrow\mathbb{R}^n$.
+ * Such problem is solved using Newton iteration augmented with a line search
+ * global strategy. The integration method used in IDA is the variable-order,
* variable-coefficient BDF (Backward Differentiation Formula), in
* fixed-leading-coefficient. The method order ranges from 1 to 5, with
* the BDF of order $q$ given by the multistep formula