# 6 Construction and Analysis of RKM for nonlinear DAEs

Now we consider RKM for nonlinear DAEs. We start with a DAE in semi explicit strangeness-free form and give general results on how to write down a general RKM for it and how to analyse the global error. Then, we consider general strangeness-free nonlinear DAEs and show that a certain class of RKM applies well – namely those that can be constructed by collocation with Lagrange polynomials over the Radau, Lobatto, or Gauss quadrature points.

## 6.1 General RKM for Semi-Explicit Strangeness-free DAEs

A semi explicit strangeness-free DAE is of the form \begin{align} \dot x &= f(t, x, y) \tag{6.1} \\ 0 &= g(t, x, y) \tag{6.2} \end{align} with the Jacobian of $$g$$ with respect to $$y$$, i.e. $\partial_y\otimes g(t, x(t), y(t)) =: g_y(t, x(t), y(t)),$ being invertible for all $$t$$ along the solution $$(x,y)$$.

Some observations:

• this system is strangeness-free
• under certain assumptions, any DAE can be brought into this form
• in the linear case $$E\dot z = Az +f$$, with $$z=(x,y)$$, the assumptions basically mean that $E = \begin{bmatrix} I & 0 \\ 0 & 0 \end{bmatrix} \quad\text{and}\quad A = \begin{bmatrix} * & * \\ * & A_{22} \end{bmatrix},$ with $$A_{22}(t)$$ invertible for all $$t$$.
• The condition $$g_y$$ invertible means that, locally, one could consider $\dot x = f(t, x, R(t,x)), \quad\text{with R such that}\quad y=R(t,x).$ However, this is not practical for numerical purposes.

The general strategy to get a suitable formulation of a time discretization of system (6.1)-(6.2) by any RKM is to consider the perturbed version \begin{align*} \dot x = f(t, x, y), \\ \varepsilon \dot y = g(t, x, y), \end{align*} which is an ODE, formulate the RKM, and then let $$\varepsilon \to 0$$. In the Hairer/Wanner Book, this approach is called *$$\varepsilon$$-embedding.

This is, consider

 $$x_{i+1} = x_i + h\sum_{j=1}^s\beta_j \dot X_{ij}$$, $$y_{i+1} = y_i + h\sum_{j=1}^s\beta_j \dot Y_{ij}$$, $$\dot X_{ij} = f(t_i+\gamma_jh, X_{ij}, Y_{ij})$$, $$\varepsilon \dot Y_{ij} = g(t_i+\gamma_j h, X_{ij}, Y_{ij})$$, $$j=1,2,\cdots,s, \quad \quad (*)$$ $$X_{ij} = x_i + h\sum_{\ell=1}^s\alpha_{j\ell}\dot X_{i\ell}$$, $$\phantom{\varepsilon}Y_{ij} = y_i + h\sum_{\ell=1}^s\alpha_{j\ell}\dot Y_{i\ell}$$, $$j=1,2,\cdots,s,$$

i.e., the RKM applied to an ODE in the variables $$(x,y)$$, and replace $$(*)$$ by

$\dot X_{ij} = f(t_i+\gamma_jh, X_{ij}, Y_{ij}),\quad 0 = g(t_i+\gamma_j h, X_{ij}, Y_{ij}), \quad j=1,2,\cdots,s.$

Theorem 6.1 (Kunkel/Mehrmann Thm. 5.16) Consider a semi-explicit, strangeness-free DAE as in (6.1)-(6.2) with a consistent initial value $$(x_0, y_0)$$. The time-discretization by a RKM,

• with $$\mathcal A$$ invertible and $$\rho:=1-\beta^T\mathcal A^{-1}e$$,
• applied as in Table 6.1 with $$\varepsilon=0$$,
• that is convergent of order $$p$$ for ODEs
• and fulfills the Butcher condition $$C(q)$$ with $$q\geq p+1$$

leads to an global error that behaves like $\|\mathfrak X(t_N) - \mathfrak X_N\| = \mathcal O(h^k),$

where

• $$k=p$$, if $$\rho=0$$,
• $$k=\min\{p, q+1\}$$, if $$-1\leq \rho < 1$$
• $$k=\min\{p, q-1\}$$, if $$\rho =1$$.

If $$|\rho|>1$$, then the RKM – applied to (6.1)(6.2) – does not converge.

Some words on the conditions on $$p$$, $$q$$, and $$\rho$$:

• For stiffly accurate methods, $$\beta^T \mathcal A^{-1}e=1$$ and, thus, $$\rho=0$$ $$\rightarrow$$ no order reduction for strangeness free or index-1 systems
• For the implicit midpoint rule also known as the 1-stage Gauss method: $\begin{array}{c|c} \frac 12 & \frac 12 \\ \hline & 1 \end{array}$

• the convergence order for ODEs is $$p=2$$
• but $$1-\beta^T \mathcal A^{-1}e = 1- 1\cdot {\bigl(\frac 12\bigr)}^{-1} 1 = -1$$, so that $$\min\{p-1, q\} = k \leq 1$$, depending on $$q$$.
• in fact $$k=1$$ as $C(q): \quad \sum_{\ell=1}^s\alpha_{j\ell}\gamma_\ell^{\bar k-1}=\frac{1}{\bar k} \gamma_j^{\bar k}, \quad {\bar k}=1,\cdots,q, \quad j=1,\cdots,s$ in the present case of $$s=1$$, $$\alpha_{11}=\gamma_1=\frac 12$$ is fulfilled for $${\bar k}=1: \quad \frac 12 = \frac 12$$
• it is not relevant here, but for $${\bar k}=2:\quad \frac 12 \cdot \frac 12 \neq \frac 12 \cdot \frac 14$$

## 6.2 Collocation RKM for Implicit Strangeness-free DAEs

The general form of a strangeness-free DAE is given as \begin{align} \hat F_1(t,x,\dot x) &= 0 \tag{6.3}\\ \hat F_2(t,x) &= 0 \tag{6.4} \end{align} where the strangeness-free or index-1 assumption is encoded in the existence of implicit functions $$\mathcal L$$, $$\mathcal R$$ such that, with $$x=(x_1,x_2)$$, the implicit DAE (6.3)(6.4) is equivalent to the semi-explicit DAE \begin{align*} \dot x_1 &= \mathcal L(t,x_1,x_2) \\ 0 &= \mathcal R(t,x_1) -x_2 \end{align*}

In what follows we show that a collocation approach coincides with certain RKM discretizations so that the convergence analysis of the RKM can be done via approximation theory.

Regression (Collocation): – If one looks for a function $$x\colon [0,1] \to \mathbb R^{}$$ that fulfills $$F(x(t))=0$$ for all $$t\in[0,1]$$, one may interpolate $$x$$ by, say, a polynomial $$x_p(t) = \sum_{\ell=0}^kx_\ell t^\ell$$ and determine the $$k+1$$ coefficients $$x_\ell$$ via the solution of the system of (nonlinear) equations $$F(x_p(t_\ell))=0$$, $$\ell=0,1,\dotsc,k$$, where the $$t_\ell\in[0,1]$$ are the $$k+1$$ collocation points.

Concretely, we parametrize $$s$$ collocation points via $$$0 = \gamma_0 < \gamma_1 <\gamma_2< \dotsc < \gamma_s=1 \tag{6.5}$$$ and define two sets of Lagrange polynomials $L_\ell(\xi) = \prod_{j=0,j\neq \ell}^s \frac{\xi-\gamma_j}{\gamma_\ell-\gamma_j} \quad\text{and}\quad \tilde L_\ell(\xi) = \prod_{m=1,m\neq \ell}^s \frac{\xi-\gamma_m}{\gamma_\ell-\gamma_m},$ with $$\ell\in\{0,1,\dotsc,s\}$$.

Let $$\mathbb P_k$$ be the space of polynomials of degree $$\leq k-1$$. We define the collocation polynomial $$x_\pi \in \mathbb P_{s+1}$$ via $$$x_\pi (t) = \sum_{\ell=0}^s X_{i\ell}L_\ell\bigl(\frac{t-t_i}{h}\bigr) \tag{6.6}$$$ designed to compute the stage values $$X_{i\ell}$$, where $$X_{i0}=x_i$$ is already given.

The stage derivatives are then defined as $$$\dot X_{ij} = \dot x_\pi(t_i+\gamma_jh) = \frac 1h \sum_{\ell=0}^sX_{i\ell}\dot L_\ell(\gamma_j). \tag{6.7}$$$

To obtain $$x_{i+1}=x_\pi(t_{i+1})=X_{is}$$, we require the polynomial to satisfy the DAE (6.3)(6.4) at the collocation points $$t_{ij}=t_i+\gamma_jh$$, that is

$$$\hat F_1(t_i+\gamma_jh,X_{ij},\dot X_{ij}) = 0, \quad \hat F_2(t_i+\gamma_jh,X_{ij}) = 0, \quad j=1,\dotsc,s. \phantom{F_1} \tag{6.8}$$$

Now we show that this collocation defines a RKM discretization of (6.3)(6.4).

Since $$\tilde L_\ell \in \mathbb P_s$$ for $$\ell=1,\ldots,s$$ it holds that $P_\ell(\sigma):=\int_0^\sigma \tilde L_\ell (\xi)d\xi \in \mathbb P_{s+1}$ that is, by Lagrange interpolation, it can be written as $P_\ell(\sigma) = \sum_{j=0}^s P_\ell(\gamma_j)L_j(\sigma).$

If we differentiate $$P_\ell$$, we get $\dot P_\ell(\sigma) = \sum_{j=0}^s P_\ell(\gamma_j)\dot L_j(\sigma) = \sum_{j=0}^s \int_0^{\gamma_j} \tilde L_\ell (\xi)d\xi \dot L_j(\sigma)=: \sum_{j=0}^s \alpha_{j\ell} \dot L_j(\sigma)$ where define simply define $\alpha_{j\ell} = \int_0^{\gamma_j} \tilde L_\ell (\xi)d\xi.$ Note that $$\alpha_{0\ell}=0$$ such that summation in $$\dot P_\ell(\sigma)$$ starts at $$j=1$$ instead of $$j=0$$. Moreover, by definition of $$P_\ell$$ (and the fundamental theorem of calculus), it holds that $\dot P_\ell(\sigma) = \tilde L_\ell(\sigma),$ which gives that $$\dot P_\ell(\gamma_m) = \delta_{\ell m}$$ that is $\dot P_\ell(\gamma_m) = \sum_{j=1}^s\alpha_{j\ell}\dot L_j(\gamma_m) = \begin{cases} 1, &\quad \text{if }\ell =m \\ 0, &\quad \text{otherwise} \end{cases}$ for $$\ell, m=1,\dotsc,s$$.

Accordingly, if we define $$\mathcal A := \bigl[\alpha_{j\ell}\bigr]_{j,\ell=1,\dotsc,s} \in \mathbb R^{s,s}$$ and $V:=\bigl[v_{mj}\bigr]_{m,j=1,\dotsc,s} = \bigl[ \dot L_j(\gamma_m) \bigr]_{m,j=1,\dotsc,s} \in \mathbb R^{s,s} ,$ it follows that $$V=\mathcal A^{-1}$$.

Moreover, since, $\sum_{j=0}^s L_j(\sigma) \equiv 1, \quad\text{so that }\quad\sum_{j=0}^s \dot L_j(\sigma) \equiv 0,$ we have that $\sum_{j=0}^s \dot L_j(\gamma_m) =0= \sum_{j=0}^s v_{mj}$ and, thus, $v_{m0} = -\sum_{j=1}^s \dot L_j(\gamma_m) = -e_m^TVe.$ With these relations we rewrite (6.7) as $h\dot X_{im} = \sum_{\ell=0}^sX_{i\ell}\dot L_\ell(\gamma_m) = v_{m0}x_i + \sum_{\ell=1}^sv_{m\ell}X_{i\ell}.$ and $$h\sum_{m=1}^s\alpha_{\ell m} \dot X_{im}$$ as \begin{align} h\sum_{m=1}^s \alpha_{\ell m}\dot X_{im} &= \sum_{m=1}^s \alpha_{\ell m}v_{m0}x_i + \sum_{j,m=1}^s \alpha_{\ell m}v_{mj}X_{ij} \notag \\ &= -e_\ell^T \mathcal AV e x_i + \sum_{j=1}^se_\ell^T \mathcal AVe_jX_{ij} \tag{6.9}\\ &= -x_i + X_{i\ell}, \notag \end{align} which, together with (6.8), indeed defines a RKM.

Some remarks:

• the preceding derivation shows that the collocation (6.6) and (6.8) is equivalent to the RKM scheme (6.9) and (6.8)
• convergence of these schemes applied to (6.3)(6.4) is proven in Kunkel/Mehrmann Theorem 5.17
• with fixing $$\gamma_s=1$$, the obtained RKM is stiffly accurate
• the remaining $$s-1$$ $$\gamma$$s can be chosen to get optimal convergence rates $$\rightarrow$$ RadauIIa schemes
• if also $$\gamma_s$$ is chosen optimal in terms of convergence, the Gauss schemes are obtained