# Inf-sup lower bound

Lower bound for coercivity constant

We require a lower bound $\alpha_{\mathrm{LB}}(\mu)$ for $\alpha(\mu) = \alpha_c(\mu),\ \forall \mu \in \mathcal{D}^\mu$

Two strategies are available:

• « Min $\Theta$ » approach if $a$ is parametrically coercive (i.e. the coercivity constant depends solely on $\mu$)

• and more generally the Successive Constraint Method (SCM) which can also be applied in case of « Inf-Sup » stable problems (Stokes, Helmholtz,…​)

## 1. « Min $\Theta$ » Approach

### 1.1. Lower bound for $\alpha(\mu)$

Lemma

For a parametrically coercive bilinear form

• $\Theta^q(\mu) > 0,\ \forall \mu \in \mathcal{D}^\mu$ and

• $a^q(v,v) \geq 0,\ \forall v \in X,\ 1 \leq q \leq Q$

We have

$\Theta^{\mathrm{min},\bar{\mu}}_a(\mu) = \alpha(\bar{\mu})\min_{q=1...Q}\frac{\Theta_a^q(\mu)}{\Theta_a^q(\bar{\mu})} \leq \alpha(\mu)$

where $\bar{\mu} \in \mathcal{D}^\mu$ which was used to define the $X$-inner product and induced norm

Recall that

\begin{align} (u,v)_X &= a(u,v;\bar{\mu}), \quad \forall u,v \in X\\ \|v\|_X &= \sqrt{(u,v)_X}, \quad \forall v \in X \end{align}

### 1.2. Upper bound for $\gamma(\mu)$

Similarly we develop an upper bound $\gamma_{\mathrm{UB}}(\mu)$ for $\gamma(\mu)$. We define

$\infty > \Theta^{\mathrm{max},\bar{\mu}}_a(\mu) = \gamma(\bar{\mu}) \max_{q=1...Q}\frac{\Theta_q^q(\mu)}{\Theta_a^q(\bar{\mu})}\geq \gamma(\mu)$

Remark : $\gamma_{\mathrm{UB}}(\mu)$ is actually not required in practice but relevant in the theory.

### 1.3. Summary

If $a$ is parametrically coercive we then choose

• the coercivity constant lower bound to be $\alpha_{\mathrm{LB}}(\mu)\equiv \Theta^{\mathrm{min},\bar{\mu}}_a(\mu)$

• and the continuity constant upper bound to be ($a$ symmetric) $\gamma_{\mathrm{UB}}(\mu)\equiv \Theta^{\mathrm{max},\bar{\mu}}_a(\mu)$

Remark on cost :

• Online cost to evaluate $\alpha_{\mathrm{LB}}(\mu)$ : $O(Q_a)$

• Choice of inner product important $(u,v)_X = a(u,v;\bar{\mu})$ (see multiple inner products approach)

• Extends to non-symmetric problems by considering the symmetric part $a_s(u,v;\mu) = \frac{1}{2}\left( a(u,v;\mu)+a(v,u;\mu) \right)$

## 2. Successive constraint method (SCM)

### 2.1. Stability estimates

We wish to compute $\alpha_{\mathrm{LB}}: \mathcal{D} \rightarrow \mathbb{R}$ such that

$0 < \alpha_{\mathrm{LB}}(\mu) \leq \alpha^{\mathcal{N}}(\mu), \quad \mu \in \mathcal{D}$

and it computation is rapid $O(1)$ where $\alpha^{\mathcal{N}}(\mu)= \displaystyle\inf_{w \in X^{\mathcal{N}}} \frac{a(w,w;\mu)}{\|w\|_X^2}$

Computation of $\alpha^{\mathcal{N}}(\mu)$ : $\alpha^{\mathcal{N}}(\mu)$ is the minimum eigenvalue of the following generalized eigenvalue problem

$a(w,v;\mu) = \lambda(\mu)\ m(w,v;\mu), \quad (A w = \lambda B w)$

where $m(\cdot,\cdot)$ is the bi-linear form associated with $\|\cdot\|_X$ and $B$ is the associated matrix.

### 2.2. Successive Constraint method : Reformulation

The problem as a minimization one First recall $a(w,v;\mu) = \displaystyle\sum_{q=1}^{Q_{a}} \theta_q(\mu)\ a_q(w,v)$. Hence we have $\alpha^{\mathcal{N}}(\mu)= \displaystyle\inf_{w \in X^{\mathcal{N}}} \displaystyle\sum_{q=1}^{Q_a} \theta_q(\mu) \frac{a_q(w,w)}{\|w\|_X^2}$ and we note $\mathcal{J}^{\mathrm{obj}}(w;\mu) = \displaystyle\sum_{q=1}^{Q_a} \theta_q(\mu) \frac{a_q(w,w)}{\|w\|_X^2}$

Reformulation : We have the following optimisation problem $\alpha^{\mathcal{N}}(\mu)= \displaystyle\inf_{y \in \mathcal{Y}} \mathcal{J}^{\mathrm{obj}}(\mu; y)$, where $\mathcal{J}^{\mathrm{obj}}(\mu; y) \equiv \displaystyle\sum_{q=1}^{Q_a} \theta_q(\mu) y_q$ and $\mathcal{Y} = \left\{ y \in \mathbb{R}^{Q_a} |\ \exists w \in X^{\mathcal{N}}\ \mathrm{s.t.}\ y_q = \frac{a_q(w,w)}{\|w\|_{X^{\mathcal{N}}}^2}, 1 \leq q \leq Q_a \right\}$

We now need to characterize $\mathcal{Y}$, to do this we construct two sets $\mathcal{Y}_{\mathrm{LB}}$ and $\mathcal{Y}_{\mathrm{UB}}$ such that $\mathcal{Y}_{\mathrm{UB}} \subset \mathcal{Y} \subset \mathcal{Y}_{\mathrm{LB}}$ over which finding $\alpha^{\mathcal{N}}(\mu)$ is feasible.

Successive Constraint method : Ingredients

First we set the design space for the minimisation problem. We introduce

$\mathcal{B} = \prod_{q=1}^{Q_a} \left[ \inf_{w\in X^{\mathcal{N}}} \frac{a_q(w,w)}{\|w\|_X^2}; \sup_{w\in X^{\mathcal{N}}} \frac{a_q(w,w)}{\|w\|_X^2} \right]$
$\Xi = \left\{ \mu_i \in \mathcal{D}; i=1,...,J \right\}$

and

$C_K = \left\{ \mu_i \in \Xi; i=1,...,K \right\} \subset \Xi$

The set $\Xi$ is constructed using a $\frac{1}{2^p}$ division of $\mathcal{D}$: in 1D, $0, 1; \frac{1}{2}; \frac{1}{4}, \frac{3}{4};...$. $C_K$ will be constructed using a greedy algorithm.

Finally we shall denote $P_M(\mu;E)$ the set of $M$ points closest to $\mu$ in the set $E$. We shall need this type of set to construct the lower bounds.

### 2.3. Successive Constraint method: Lower bounds $\mathcal{Y}_{\mathrm{LB}}$

Given $M_\alpha, M_+ \in \mathbb{N}$ we are now ready to define $\mathcal{Y}_{\mathrm{LB}}$

\begin{align} \mathcal{Y}_{\mathrm{LB}}(\mu; C_K) = \Big\{ y \in \mathbb{R}^{Q_a}|& y \in \mathcal{B}, \\ &\sum_{q=1}^{Q_a} \theta_q(\mu') y_q \geq \alpha^{\mathcal{N}}(\mu'),\ \forall \mu' \in P_{M_\alpha}(\mu;C_K) \\ &\sum_{q=1}^{Q_a} \theta_q(\mu') y_q \geq \alpha_{\mathrm{LB}}(\mu';C_{K-1}),\ \forall \mu' \in P_{M_+}(\mu;\Xi\backslash C_K) \Big\} \end{align}

Computing $\alpha_{\mathrm{LB}}(\mu;C_K)$ is in fact a linear program with $Q_a$ design variables, $y_q$, and $2 Q_a+M_\alpha+M_+$ constraints online. It requires the construction of $C_K$ offline.

### Successive Constraint method: Upper bounds $\mathcal{Y}_{\mathrm{UB}}$

Let $\mathcal{Y}_{\mathrm{UB}}( C_K ) = \left\{ y^*(\mu_k), 1 \leq k \leq K \right\}$ with $y^*(\mu) = \mathrm{arg}\mathrm{min}_{y \in \mathcal{Y}}\ \mathcal{J}^{\mathrm{obj}}( \mu; y )$ We set $\alpha_{\mathrm{UB}}( \mu; C_K) = \inf_{y \in \mathcal{Y}_{\mathrm{UB}}(C_K)}\ \mathcal{J}^{\mathrm{obj}}(\mu;y)$

$\mathcal{Y}_{\mathrm{UB}}$ requires $K$ eigensolves to compute the eigenmode $\eta_k$ associated with $w_k, k=1,...,K$ and $K Q \mathcal{N}$ inner products to compute the $y^*_q(w_k)=\frac{a_q(\eta_k,\eta_k;\mu)}{\|\eta_k\|_{X^{\mathcal{N}}}^2}, k=1,...,K$ offline . Then computing $\alpha_{\mathrm{UB}}( \mu; C_K)$ is a simple enumeration online.

Successive Constraint method : $C_K$

Given $\Xi$ and $\epsilon \in [0;1$], $C_{K_\mathrm{max}} = \mathrm{Greedy}(\Xi, \epsilon$).

• While $\displaystyle\max_{\mu \in \Xi}\ \frac{\alpha_{\mathrm{UB}}( \mu; C_K) - \alpha_{\mathrm{LB}}( \mu; C_K)}{\alpha_{\mathrm{UB}}( \mu; C_K)} > \epsilon$

• $\mu_{K+1} = \mathrm{arg} \mathrm{max}_{\mu \in \Xi}\ \frac{\alpha_{\mathrm{UB}}( \mu; C_K) - \alpha_{\mathrm{LB}}( \mu; C_K)}{\alpha_{\mathrm{UB}}( \mu; C_K)}$

• $C_{K+1} = C_K \cup \{ \mu_{K+1} \}$

• $K \leftarrow K+1$

• set $K_{\mathrm{max}} = K$

Successive Constraint method : Offline-Online

$\mathrm{Offline}$

• $2Q_a+M_\alpha+M_+$ eigensolves $\alpha^{\mathcal{N}}(\mu), y^*(\mu_k)$

• $n_\Xi K_{\mathrm{max}} LP(Q,M_\alpha,M_+)$ to build $C_{K_{\mathrm{max}}}$

• $K_{\mathrm{max}} Q$ inner products over $X^{\mathcal{N}} \Rightarrow \mathcal{Y}_{\mathrm{UB}}$

Given $\mu \in \mathcal{D}$, $\alpha_{\mathrm{LB}}(\mu) = \mathrm{Online}( \mu, C_{K_{\mathrm{max}}}, M_\alpha, M_+ )$.

• sort over $C_{K_{\mathrm{max}}} \Rightarrow P_{M_\alpha}(\mu;C_{K_{\mathrm{max}}})$ and $P_{M_+}(\mu;\Xi\backslash C_{K_{\mathrm{max}}})$

• $(M_\alpha+M_++2) Q_a$ evaluation of $\theta_q(\mu')$

• $M_\alpha$ lookups to get $\mu' \rightarrow \alpha^{\mathcal{N}}(\mu')$

• $LP(Q_a,M_\alpha,M+)$ to get $\alpha_{\mathrm{LB}} (\mu)$