* Polynomials for a variant of Hermite polynomials with better condition
* number in the interpolation than the basis from HermiteInterpolation.
*
- * In analogy to the actual Hermite polynomials this basis evaluates the
+ * In analogy to the proper Hermite polynomials, this basis evaluates the
* first polynomial $p_0$ to 1 at $x=0$ and has both a zero value and zero
* derivative at $x=1$. Likewise, the last polynomial $p_n$ evaluates to 1
- * at $x=1$ but has zero value and zero derivative at $x=0$. The second
+ * at $x=1$ with a zero value and zero derivative at $x=0$. The second
* polynomial $p_1$ and the second to last polynomial $p_{n-1}$ represent
- * the derivative degree of freedom at $x=0$ and $x=1$, respectively. As
- * such, they are zero at both the end points $x=0, x=1$ and have zero
+ * the derivative degree of freedom at $x=0$ and $x=1$, respectively.
+ * They are zero at both the end points $x=0, x=1$ and have zero
* derivative at the opposite end, $p_1'(1)=0$ and $p_{n-1}'(0)=0$. As
* opposed to the original Hermite polynomials, $p_0$ does not have zero
* derivative at $x=0$. The additional degree of freedom is used to make
* respectively. Furthermore, the extension of these polynomials to higher
* degrees $n>3$ is constructed by adding additional nodes inside the unit
* interval, again ensuring better conditioning. The nodes are computed as
- * the roots of the Jacobi polynomials for $\alpha=\beta=2$ which are
+ * the roots of the Jacobi polynomials for $\alpha=\beta=2$, which are
* orthogonal against the generating function $x^2(1-x)^2$ with the Hermite
* property. Then, these polynomials are constructed in the usual way as
* Lagrange polynomials with double roots at $x=0$ and $x=1$. For example at
* through the factor $(x-0.5)$. In summary, this basis is dominated by
* nodal contributions, but it is not a nodal one because the second and
* second to last polynomials that are non-nodal, and due to the presence of
- * double nodes in $x=0$ and $x=1$.
+ * double nodes in $x=0$ and $x=1$. The weights of the basis functions are
+ * set such that the sum of all polynomials with unit weight represents the
+ * constant function 1, similarly to Lagrange polynomials.
*
* The basis only contains Hermite information at <code>degree>=3</code>,
* but it is also implemented for degrees between 0 and two. For the linear
* case, the usual hat functions are implemented, whereas the polynomials
- * for <code>degree=2</code> are $p_0(x)=(1-x)^2$, $p_1(x)=4x(x-1)$, and
+ * for <code>degree=2</code> are $p_0(x)=(1-x)^2$, $p_1(x)=2x(x-1)$, and
* $p_2(x)=x^2$, in accordance with the construction principle for degree 3
* that allows a non-zero of $p_0$ and $p_2$.
*
*
* This polynomial inherits the advantageous property of Hermite polynomials
* where only two functions have value and/or derivative nonzero on a face
+ * advantageous for discontinuous Galerkin methods
* but gives better condition numbers of interpolation, which improves the
* performance of some iterative schemes like conjugate gradients with
- * point-Jacobi.
+ * point-Jacobi. This polynomial is used in FE_DGQHermite.
*
* @author Martin Kronbichler
* @date 2018