2.1 Metric structure

We must first specify the metric used in measuring perturbations of the input and the norm used on the relevant gradients. For every $n \in {\mathbb{N}}$ and $p \in [1,\infty]$ , let $\ell_p^n$ denote the normed space $({\mathbb{R}}^n, {\left\lVert{.}\right\rVert}^{(n)}_p)$ , in which the norm ${\left\lVert{.}\right\rVert}^{(n)}_p$ is defined for all $x = (x_1, \ldots, x_n) \in {\mathbb{R}}^n$ by ${\left\lVert{x}\right\rVert}^{(n)}_p := \left( \sum_{i=1}^{n} {\lvert{x_i}\rvert}^p\right)^{1/p}$ , if $ p \in [1, \infty)$ , and ${\left\lVert{x}\right\rVert}^{(n)}_p := \max\{{{\lvert{x_i}\rvert}}\mid{ 1 \leq i \leq n}\}$ , if $p = \infty$ . We let $d_p^{(n)} : {\mathbb{R}}^n \times {\mathbb{R}}^n \to [0,\infty)$ denote the metric induced by ${\left\lVert{.}\right\rVert}^{(n)}_p$ , i.e., $d_p^{(n)}(x,y) = {\left\lVert{x-y}\right\rVert}^{(n)}_p$ . When n is clear from the context, we drop the superscript and write ${\left\lVert{.}\right\rVert}_p$ and $d_p$ . For every $p \in [1,\infty]$ , define the conjugate $p' \in [1,\infty]$ as follows:

(1) \begin{equation} p' := \left\{ \begin{array}{l@{\quad}l} \infty, & \text{if } p = 1,\\[3pt] p/(p-1), & \text{if } p \in (1,\infty), \\[3pt] 1, & \text{if } p = \infty.\\ \end{array} \right.\end{equation}

In our discussion, whenever the perturbation of the input is measured using ${\left\lVert{.}\right\rVert}^{(n)}_p$ -norm, then the norm of the gradient must be taken to be ${\left\lVert{.}\right\rVert}^{(n)}_{p'}$ . Furthermore, as the spaces $\ell^n_p$ are finite-dimensional, the topology on these spaces does not depend on p, a fact which is not true in infinite-dimensional $\ell_p$ spaces. Thus, in the current article, whenever we mention a closed, open, or compact subset of ${\mathbb{R}}^n$ , the topology is assumed to be that of $\ell^n_\infty$ .

2.2 Compact subsets

Assume that $f: {\mathbb{R}}^n \to {\mathbb{R}}$ is (the semantic model of) a regressor neural network with n input neurons. In robustness analysis, we must consider the action of f over suitable subsets of the input space. Specifically, we work with closed subsets of both the input and output spaces. Let us justify this choice formally.

Let $({\mathbb{S}},d)$ be a metric space with distance $d: {\mathbb{S}} \times {\mathbb{S}} \to {\mathbb{R}}$ . For any $X \subseteq {\mathbb{S}}$ and $\delta>0$ , we define the open neighborhood:

(2) \begin{equation} B(X,\delta) := \{y \in {\mathbb{S}} \mid \exists x \in X : d(x,y)<\delta\}.\end{equation}

The set $B(X,\delta)$ is indeed open, because it is the union of open balls $B(\left\{{s}\right\},\delta)$ with $s \in X$ . Intuitively, $B(X,\delta)$ is the set of points in X with imprecision less than $\delta$ . For all $X \subseteq {\mathbb{S}}$ and $\delta>0$ , we have $X\subseteq \overline{X}\subseteq B(X,\delta)=B(\overline{X},\delta)$ , in which $\overline{X}$ denotes the closure of X. Thus, in the presence of imprecision, X and $\overline{X}$ are indistinguishable. Furthermore, it is straightforward to verify that $\overline{X}=\bigcap_{\delta>0}B(X,\delta)=\bigcap_{\delta>0}\overline{B(X,\delta)}$ . Intuitively, $\overline{B(X,\delta)}$ is the set of points in X with imprecision less than or equal to $\delta$ . Thus, the closure $\overline{X}$ is the set of points that are in X with arbitrarily small imprecision.

In practice, the input space of a network is a bounded region such as ${\mathbb{S}} = [{-}M,M]^n$ . As $[{-}M,M]^n$ is compact, and closed subsets of compact spaces are compact, in this paper, we will be working with compact subsets. A more detailed discussion of the choice of closed subsets, in the broader context of robustness analysis, may be found in Moggi et al. (Reference Moggi, Farjudian and Taha2019b).

2.3 Domain theory

Domain theory has its roots in topological algebra (Gierz et al., Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott1980; Keimel,Reference Keimel2017), and it has enriched computer science with powerful methods from order theory, algebra, and topology. Domains gained prominence when they were introduced as a mathematical model for lambda calculus by Scott (Reference Scott1970). We have already mentioned that our domain-theoretic framework provides a unified foundation for validated methods of robustness and security analysis of neural networks. Domain theory, however, provides more than just a unifying framework:

1. (1) Domain-theoretic models are ideal for designing algorithms which are correct by construction, as will be demonstrated in Section 4.

2. (2) Domains provide a denotational semantics for computable analysis according to Type-II Theory of Effectivity (TTE) (Weihrauch, Reference Weihrauch2000, Theorem 9.5.2). Our computability results (Theorems 4.16, 4.19, and 4.21) are obtained within the framework of effectively given domains (Smyth, Reference Smyth1977). As a consequence, our constructs can be implemented directly on digital computers.

We present a brief reminder of the concepts and notations that will be needed later. The interested reader may refer to Gierz et al. (Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott1980), Abramsky and Jung (Reference Abramsky, Jung, Abramsky, Gabbay and Maibaum1994), Gierz et al. (Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott2003), Goubault-Larrecq (Reference Goubault-Larrecq2013) for more on domains in general and refer to Escardó (Reference Escardó1996), Edalat (Reference Edalat1997) for the interval domain, in particular. A succinct and informative survey of the theory may be found in Keimel (Reference Keimel2017).

For any subset X of a partially ordered set (poset) $(D, \sqsubseteq)$ , we write $\bigsqcup X$ and to denote the least upper bound, and the greatest lower bound, of X, respectively, whenever they exist. In our discussion, $x \sqsubseteq y$ may be interpreted as “y contains more information than x.” A subset $X \subseteq D$ is said to be directed if $X\neq \emptyset$ and every two elements of X have an upper bound in X, i.e., $\forall x, y \in X: \exists z \in X: (x \sqsubseteq z) \wedge (y\sqsubseteq z)$ . An $\omega$ -chain – i.e., a set of the form $x_0 \sqsubseteq x_1 \sqsubseteq \cdots \sqsubseteq x_n \sqsubseteq\cdots$ – is an important special case of a directed set. A poset $(D, \sqsubseteq)$ is said to be a DCPO if every directed subset $X \subseteq D$ has a least upper bound in D. The poset $(D, \sqsubseteq)$ is said to be pointed if it has a bottom element, which we usually denote by $\bot_D$ , or if D is clear from the context, simply by $\bot$ . Directed-completeness is a basic requirement for almost all the posets in our discussion.

We let ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ denote the poset with the carrier set $\{{C \subseteq {\mathbb{R}}^n}\mid{C \text{ is non-empty and compact}}\}\cup \left\{{{\mathbb{R}}^n}\right\}$ , ordered by reverse inclusion, i.e., $\forall X, Y \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}: X \sqsubseteq Y \Longleftrightarrow Y \subseteq X$ . By further requiring the subsets to be convex, we obtain the sub-poset ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ . Finally, we let ${\mathbb{I}}{\mathbb{R}}^n_\bot$ denote the poset of hyperboxes of ${\mathbb{R}}^n$ – i.e., subsets of the form $\prod_{i=1}^n [a_i,b_i]$ – ordered under reverse inclusion, with ${\mathbb{R}}^n$ added as the bottom element. The three posets are pointed dcpos and $\bigsqcup X = \bigcap X$ , for any directed subset X.

A central concept in domain theory is that of the way-below relation. Assume that $(D, \sqsubseteq)$ is a dcpo and let $x,y \in D$ . The element x is said to be way-below y – written as $x \ll y$ – iff for every directed subset X of D, if $y \sqsubseteq \bigsqcup X$ , then there exists an element $d \in X$ such that $x \sqsubseteq d$ . Informally, x is way-below y iff it is impossible to get past y (by suprema of directed sets) without first getting past x. We may also interpret $x \ll y$ as ‘x is a finitary approximation of y’, or even as, ‘x is a lot simpler than y’ (Abramsky and Jung, Reference Abramsky, Jung, Abramsky, Gabbay and Maibaum1994, Section 2.2.1).

It is easy to prove that $\forall x, y \in D: x \ll y \implies x \sqsubseteq y$ . The converse, however, is not always true. For instance, over ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ (hence, also over $ {\mathbb{I}}{\mathbb{R}}^n_\bot$ and ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ ) we have the following characterization:

(3) \begin{equation} \forall K_1, K_2 \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}: K_1 \ll K_2 \Longleftrightarrow K_2 \subseteq K_1^\circ,\end{equation}

in which $K_1^\circ$ denotes the interior of $K_1$ .

For every element x of a dcpo $(D, \sqsubseteq)$ , let . In domain-theoretic terms, the elements of are the true approximants of x. In fact, the way-below relation is also known as the order of approximation (Abramsky and Jung, Reference Abramsky, Jung, Abramsky, Gabbay and Maibaum1994, Section 2.2.1). A subset B of a dcpo $(D, \sqsubseteq)$ is said to be a basis for D, if for every element $x \in D$ , the set is a directed subset with supremum x, i.e., $x = \bigsqcup B_x$ . A DCPO $(D, \sqsubseteq)$ is said to be continuous if it has a basis, and $\omega$ -continuous if it has a countable basis. We call $(D, \sqsubseteq)$ a domain if it is a continuous pointed dcpo. The pointed dcpos ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , and ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ , are all $\omega$ -continuous domains, because:

• $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot} := \left\{{{\mathbb{R}}^n}\right\} \cup \{{C \in {\mathbb{I}}{\mathbb{R}}^n_\bot}\mid{C \text{ is a hyperbox with rational coordinates}}\}$ is a basis for ${\mathbb{I}}{\mathbb{R}}^n_\bot$ ;

• $B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}} := \left\{{{\mathbb{R}}^n}\right\} \cup \{{C \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}}\mid{C \text{ is a finite union of hyperboxes with rational coordinates}}\}$ is a basis for ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ ;

• $B_{{\mathbb{C}}{{\mathbb{R}}^n_\bot}} := \left\{{{\mathbb{R}}^n}\right\} \cup \{{C \in {\mathbb{C}}{{\mathbb{R}}^n_\bot}}\mid{C \text{ is a convex polytope with rational coordinates}}\}$ is a basis for ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ .

The poset $\Sigma^\infty$ of Example 2.3 is also an $\omega$ -continuous domain, with $\Sigma^*$ as a countable basis. There is a significant difference between $\Sigma^\infty$ and the domains ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , and ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ , in that, for the elements of the basis $\Sigma^*$ of $\Sigma^\infty$ , we have $\forall x \in \Sigma^*: x \ll x$ . By (3), this is clearly not true of the bases $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot}$ , $B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ and $B_{{\mathbb{C}}{{\mathbb{R}}^n_\bot}}$ , or indeed, any other bases for ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , and ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ . As such, the domain $\Sigma^\infty$ is an example of an algebraic domain, whereas the domains that we use in the current paper are non-algebraic. Although algebraic domains have been used in real number computation (Di Gianantonio, Reference Di Gianantonio1996; Farjudian, Reference Farjudian2007) and, more broadly, representation of topological spaces (Stoltenberg-Hansen and Tucker, Reference Stoltenberg-Hansen and Tucker1999, Reference Stoltenberg-Hansen, Tucker, Abramsky, Gabbay and Maibaum1995), non-algebraic domains have proven more suitable for computation over continuous spaces, e.g., in dynamical systems (Edalat, Reference Edalat1995b), exact real number computation (Edalat, Reference Edalat1997; Escardó, Reference Escardó1996), differential equation solving (Edalat and Pattinson, 2007b; Edalat et al., Reference Edalat, Farjudian, Mohammadian and Pattinson2020), and reachability analysis of hybrid systems (Edalat and Pattinson, 2007a; Moggi et al., Reference Moggi, Farjudian, Duracz and Taha2018), to name a few. Hence, in this article, we will be working in the framework of non-algebraic $\omega$ -continuous domains.

Apart from the order-theoretic structure, domains also have a topological structure. Assume that $(D, \sqsubseteq)$ is a poset. A subset $O \subseteq D$ is said to be Scott open if it has the following properties:

1. (1) It is an upper set, that is, $\forall x \in O, \forall y \in D: x \sqsubseteq y \implies y \in O$ .

2. (2) It is inaccessible by suprema of directed sets, that is, for every directed set $X \subseteq D$ for which $\bigsqcup X$ exists, if $\bigsqcup X \in O$ then $X \cap O \neq \emptyset$ .

The collection of all Scott open subsets of a poset forms a $T_0$ topology, referred to as the Scott topology. A function $f: (D_1, \sqsubseteq_1) \to (D_2, \sqsubseteq_2)$ is said to be Scott-continuous if it is continuous with respect to the Scott topologies on $D_1$ and $D_2$ . Scott continuity can be stated purely in order-theoretic terms. A map $f: (D_1, \sqsubseteq_1) \to (D_2, \sqsubseteq_2)$ between two posets is Scott-continuous if and only if it is monotonic and preserves the suprema of directed sets, i.e., for every directed set $X \subseteq D_1$ for which $\bigsqcup X$ exists, we have $f(\bigsqcup X) = \bigsqcup f(X)$ (Goubault-Larrecq, Reference Goubault-Larrecq2013, Proposition 4.3.5). When $D_1$ and $D_2$ are $\omega$ -continuous domains, then we have an even simpler formulation:

For every element x of a dcpo $(D, \sqsubseteq)$ , let .

The maximal elements of ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , and ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ are singletons, and the sets of maximal elements may be identified with ${\mathbb{R}}^n$ . For simplicity, we write x to denote a maximal element $\left\{{x}\right\}$ . As a corollary of Proposition 2.10 and characterization (3), we obtain:

Thus, the sets of maximal elements are homeomorphic to ${\mathbb{R}}^n$ . For any $K \geq 0$ , by restricting to $[{-}K,K]^n$ , we obtain the $\omega$ -continuous domains ${\mathbb{I}}{[{-}K,K]^n}$ , ${\mathbb{K}}{[{-}K,K]^n}$ , and ${\mathbb{C}}{[{-}K,K]^n}$ , respectively.

We say that a poset $(D,\sqsubseteq)$ is bounded-complete if each bounded pair $x,y \in D$ has a supremum. Assume that $(X, \Omega(X))$ is a topological space, and let $(D, \sqsubseteq_D)$ be a bounded-complete domain. We let $(D, \Sigma(D))$ denote the topological space with carrier set D under the Scott topology $\Sigma(D)$ . The space $[X \to D]$ of functions $f: X \to D$ which are $(\Omega(X), \Sigma(D))$ continuous can be ordered pointwise by defining:

\begin{equation*} \forall f, g \in [X \to D]: \quad f \sqsubseteq g \Longleftrightarrow \forall x \in X: f(x) \sqsubseteq_D g(x).\end{equation*}

It is straightforward to verify that the poset $([X \to D], \sqsubseteq)$ is directed-complete and $\forall x \in X: (\bigsqcup_{i \in I} f_i)(x) = \bigsqcup\{{f_i(x)}\mid{i \in I}\}$ , for any directed subset $\{{f_i}\mid{i \in I}\}$ of $[X \to D]$ . For the poset $([X \to D], \sqsubseteq)$ to be continuous, however, the topological space $(X, \Omega(X))$ must be core-compact, as we explain briefly. Consider the poset $(\Omega(X), \subseteq )$ of open subsets of X ordered under subset relation. For any topological space X, this poset is a complete lattice, with $\emptyset$ as the bottom element, and X as the top element. Furthermore, we have:

A topological space $(X, \Omega(X))$ is said to be core-compact if and only if the lattice $(\Omega(X), \subseteq )$ is continuous.

All the domains that will be used in the framework developed in this article – including ${\mathbb{I}}{[{-}K,K]^n}$ , ${\mathbb{K}}{[{-}K,K]^n}$ , and ${\mathbb{C}}{[{-}K,K]^n}$ – are bounded-complete, and they are core-compact under their respective Scott topologies. Furthermore, the space $[{-}K,K]^n$ under the Euclidean topology is core-compact. For more on core-compact spaces, the interested reader may refer to, e.g., Gierz et al. (Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott1980, Chapter II) and Goubault-Larrecq (Reference Goubault-Larrecq2013, Chapter 5).

Throughout this article, by an interval function over a set X we mean a function $f: X \to {\mathbb{I}}{\mathbb{R}}^n_\bot$ for some $n \geq 1$ . For any set $X \subseteq {\mathbb{R}}^n$ , we let $D^{(0)}(X)$ denote the continuous domain of interval functions $X \Rightarrow {\mathbb{I}}{\mathbb{R}}_\bot$ . Each function in $D^{(0)}(X)$ is Euclidean-Scott-continuous, i.e., continuous with respect to the Euclidean topology on X and Scott topology on ${\mathbb{I}}{\mathbb{R}}_\bot$ . The functions in $D^{(0)}(X)$ have a useful characterization in terms of semi-continuity. Recall that, if X is any topological space, then:

• $f: X \to {\mathbb{R}}$ is said to be upper semi-continuous at $x_0 \in X$ iff, for every $y > f(x_0)$ , there exists a neighborhood U of $x_0$ (in the topology of X) such that $\forall x \in U: f(x) < y$ .

• $f: X \to {\mathbb{R}}$ is said to be lower semi-continuous at $x_0 \in X$ iff, for every $y < f(x_0)$ , there exists a neighborhood U of $x_0$ such that $\forall x \in U: f(x) > y$ .

• $f: X \to {\mathbb{R}}$ is said to be upper (respectively, lower) semi-continuous iff it is upper (respectively, lower) semi-continuous at every $x_0 \in X$ .

For any compact set $K \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , we define the hyperbox closure as:

(4) \begin{equation} K^\Box := \bigsqcup{\{{R \in {\mathbb{I}}{\mathbb{R}}^n_\bot}\mid{K \subseteq R}\}}. \end{equation}

As $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot}$ is a basis for ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , it is straightforward to show that:

For each $i \in \left\{{1, \ldots, n}\right\}$ , we let $\pi_i: {\mathbb{R}}^n \to {\mathbb{R}}$ denote projection over the i-th component, that is, $\pi_i( x_1, \ldots, x_n) := x_i$ . The projections are all continuous functions, so they map compact sets to compact sets. Hence, for any compact set $K \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , the set $\pi_i(K)$ is a compact subset of ${\mathbb{R}}$ , for any given $i \in \left\{{1, \ldots, n}\right\}$ . For each $i \in \left\{{1, \ldots, n}\right\}$ , we define $\underline{K_i} := \min \pi_i(K)$ and $\overline{K_i} := \max \pi_i(K)$ . It is straightforward to verify that:

(5) \begin{equation} \forall K \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}: \quad K^\Box = \prod_{i=1}^n [\underline{K_i}, \overline{K_i}]. \end{equation}

Proof. As both ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ and ${\mathbb{I}}{\mathbb{R}}^n_\bot$ are $\omega$ -continuous, by Proposition 2.9, it suffices to show that the map ${(\cdot)}^\Box$ is monotonic and preserves the suprema of $\omega$ -chains. Monotonicity is straightforward. Next, assume that $(K_i)_{i \in {\mathbb{N}}}$ is an $\omega$ -chain in ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ . We must show that ${\left( \bigsqcup_{i \in {\mathbb{N}}} K_i\right)}^\Box = \bigsqcup_{i \in {\mathbb{N}}} K_i^\Box$ . The $\sqsupseteq$ direction follows from monotonicity. To prove the $\sqsubseteq$ direction, by Proposition 2.15, we have:

Take any arbitrary $b \in B_{{\mathbb{I}}{\mathbb{R}}^n_\bot}$ satisfying $b \ll \bigsqcup_{i \in {\mathbb{N}}} K_i$ . By Proposition 2.10, the set

is Scott open, which entails that $\exists i_0 \in {\mathbb{N}}: b \ll K_{i_0}$ . By Lemma 2.16, we must have $b \ll {K_{i_0}}^\Box$ , from which we deduce that $b \ll \bigsqcup_{i \in {\mathbb{N}}} K_i^\Box$ . As b was chosen arbitrarily, then we must have ${\left( \bigsqcup_{i \in {\mathbb{N}}} K_i\right)}^\Box \sqsubseteq \bigsqcup_{i \in {\mathbb{N}}} K_i^\Box$ .

Although the dcpos ${\mathbb{I}}{\mathbb{R}}^n_\bot$ and ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ are both continuous, and ${\mathbb{I}}{\mathbb{R}}^n_\bot$ is a sub-poset of ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , the relationship between the two is stronger than mere sub-poset relationship. Let D and E be two dcpos and assume that $e: D \to E$ and $p: E \to D$ are two Scott continuous maps. The pair (e,p) is said to be a continuous section-retraction pair iff $p \circ e = {\text{id}}_D$ . In this case, D is said to be a continuous retract of E. It is straightforward to verify that in a section-retraction pair, the section must be injective and the retraction must be surjective. When E is a continuous domain, the continuous retract D must also be a continuous domain (Abramsky and Jung, Reference Abramsky, Jung, Abramsky, Gabbay and Maibaum1994, Lemma 3.1.3). If, furthermore, the condition $e \circ p \sqsubseteq {\text{id}}_E$ is satisfied, then the pair (e,p) is said to be a continuous embedding-projection pair. In this case, if E is a continuous domain, then D is said to be a sub-domain of E. ^{Footnote 1}

A straightforward modification of the arguments presented so far implies the following variant of Corollary 2.18, formulated for the restricted variants of ${\mathbb{I}}{\mathbb{R}}^n_\bot$ and ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ :

Proof. The proof is similar to that of Corollary 2.18, except that the hyperbox closure of (4) must be reformulated as:

\begin{equation*} \forall C \in {\mathbb{K}}{[{-}K,K]^n}: \quad {C}^\Box := \bigsqcup{\{{R \in {\mathbb{I}}{[{-}K,K]^n}}\mid{C \subseteq R}\}}, \end{equation*}

and, in the other direction, the embedding $\iota : {\mathbb{I}}{[{-}K,K]^n} \to {\mathbb{K}}{[{-}K,K]^n}$ defined by $\forall x \in {\mathbb{I}}{[{-}K,K]^n}: \iota( x) := x$ must be considered.

1. (i) A map $u: {\mathbb{K}}{{\mathbb{R}}^n_\bot} \to {\mathbb{K}}{{\mathbb{R}}^m_\bot}$ is said to be an extension of $f: {\mathbb{R}}^n \to {\mathbb{R}}^m$ iff $\forall x \in {\mathbb{R}}^n: u(\left\{{x}\right\}) = \left\{{f(x)}\right\}$ .

2. (ii) A map $u: {\mathbb{I}}{\mathbb{R}}^n_\bot \to {\mathbb{I}}{\mathbb{R}}^m_\bot$ is said to be an interval extension of $f: {\mathbb{R}}^n \to {\mathbb{K}}{{\mathbb{R}}^m_\bot}$ iff $\forall x \in {\mathbb{R}}^n: u(\left\{{x}\right\}) = {f(x)}^\Box$ .

3. (iii) For any $f : {\mathbb{R}}^n \to {\mathbb{K}}{{\mathbb{R}}^m_\bot}$ , we define the canonical interval extension ${If} : {\mathbb{I}}{\mathbb{R}}^n_\bot \to {\mathbb{I}}{\mathbb{R}}^m_\bot$ by:

   (6)

   

4. (iv) A map $u: {\mathbb{I}}{\mathbb{R}}^n_\bot \to {\mathbb{I}}{\mathbb{R}}^m_\bot$ is said to be an interval approximation of $f: {\mathbb{R}}^n \to {\mathbb{K}}{{\mathbb{R}}^m_\bot}$ if $u \sqsubseteq {If}$ .

• Any continuous map $f: X \to Z$ has a continuous extension $f^*: Y \to Z$ satisfying $f = f^* \circ j$ .

It turns out that bounded-complete continuous domains (equipped with the Scott topology) are exactly densely injective $T_0$ -spaces (Gierz et al., Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott2003, Proposition II-3.11). Furthermore, when Z is a bounded-complete continuous domain, the function $f^*: Y \to Z$ defined by formula (7) below is a continuous extension of f to Y and is indeed the supremum of all such extensions (Gierz et al., Reference Gierz, Hofmann, Keimel, Lawson, Mislove and Scott2003, Proposition II-3.9):

(7)

In case Z is a continuous -semilattice with unit – of which ${\mathbb{K}}{{\mathbb{R}}^m_\bot}$ and ${\mathbb{I}}{\mathbb{R}}^m_\bot$ are two instances – and the map j satisfies a fairly mild condition (i.e., j is finitary, Escardó Reference Escardó1998, Definition 2.7.1) then (7) can be simplified to:

(8)

in which $\uparrow \alpha := \{{y \in Y}\mid{\alpha \sqsubseteq y}\}$ (Escardó, Reference Escardó1998, Theorem 4.5.3). Here, $\alpha \sqsubseteq y$ denotes the specialization order defined by $\alpha \sqsubseteq y \Longleftrightarrow \forall U \in \Omega(Y): \left( \alpha \in U \implies y \in U \right)$ , i.e., whenever an open set U contains $\alpha$ , it must also contain y. When $Y={\mathbb{I}}{\mathbb{R}}^n_\bot$ with the Scott topology, the specialization order on Y is the reverse inclusion order of the current article, i.e., $\forall \alpha, y \in {\mathbb{I}}{\mathbb{R}}^n_\bot: \left( \alpha \sqsubseteq y \Longleftrightarrow y \subseteq \alpha \right)$ .

Taking $Z = {\mathbb{I}}{\mathbb{R}}^m_\bot$ , and defining $j : {\mathbb{R}}^n \to {\mathbb{I}}{\mathbb{R}}^n_\bot$ as $\forall x \in {\mathbb{R}}^n: j(x) := \left\{{x}\right\}$ , it can be shown that j is indeed finitary, and clearly, formula (6) can be obtained from (8). Furthermore, the very map $f \mapsto {If}$ from $({\mathbb{R}}^n \Rightarrow {\mathbb{I}}{\mathbb{R}}^m_\bot)$ to $({\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^m_\bot)$ is a Scott continuous sub-space embedding (Escardó, Reference Escardó1998, Theorem 4.5.6).

As the restriction of the Scott topology of ${\mathbb{K}}{{\mathbb{R}}^m_\bot}$ over ${\mathbb{R}}^m$ is the Euclidean topology, we may consider any continuous map $f: {\mathbb{R}}^n \to {\mathbb{R}}^m$ also as a function of type ${\mathbb{R}}^n \to {\mathbb{K}}{{\mathbb{R}}^m_\bot}$ , and construct its canonical interval extension accordingly, which will be Scott-continuous.

3.1 Classifiers

Let $C = \left\{{c_1, \ldots, c_k}\right\}$ be a set of classes, with $k \geq 1$ , and assume that the function $N: [{-}M,M]^n \to C$ is (the semantic model of) a classifier. As C is discrete, it is natural to metrize C using the discrete metric:

\begin{equation*} \forall x,y \in C: \quad d(x,y) := \left\{ \begin{array}{l@{\quad}l} 0, & \text{if } x = y,\\ 1, & \text{if } x \neq y.\\ \end{array} \right.\end{equation*}

The main problem with a classifier of type $N : [{-}M,M]^n \to C$ is the mapping of a connected space to a discrete one, where the topology on $[{-}M,M]^n$ is the Euclidean topology, and the topology on C is the discrete topology:

Proposition 3.4 implies that a classifier may not be computable – in the framework of TTE (Weihrauch, Reference Weihrauch2000) – unless if it is constant. This is because, by Ko (Reference Ko1991, Theorem 2.13), if N is computable, then it must be continuous.

By moving to compact subsets, however, it is possible to obtain tight robust approximations of classifiers, even in the presence of discontinuities. As C is a finite set, it is compact under the discrete topology, and ${\mathbb{K}}{C}$ is the set of non-empty subsets of C, ordered under reverse inclusion.

Example 3.6. Assume that the classifier $N: [{-}1,1] \to \left\{{c_1, c_2}\right\}$ satisfies:

\begin{equation*} \forall x \in [{-}1,1]: \quad N(x) = \left\{ \begin{array}{l@{\quad}l} c_1, & \text{if } x \in [{-}1,0],\\ c_2, & \text{if } x \in (0,1].\\ \end{array} \right. \end{equation*}

Then, according to (9), we have:

\begin{equation*} \forall x \in [{-}1,1]: \quad A_N(\left\{{x}\right\}) = \left\{ \begin{array}{l@{\quad}l} \left\{{c_1}\right\}, & \text{if } x \in [{-}1,0],\\ \left\{{c_2}\right\}, & \text{if } x \in (0,1],\\ \end{array} \right. \end{equation*}

while according to (10), we must have:

\begin{equation*} \forall x \in [{-}1,1]: \quad A_N^\circledcirc(\left\{{x}\right\}) = \left\{ \begin{array}{l@{\quad}l} \left\{{c_1}\right\}, & \text{if } x \in [{-}1,0),\\ \left\{{c_1,c_2}\right\}, & \text{if } x = 0,\\ \left\{{c_2}\right\}, & \text{if } x \in (0,1].\\ \end{array} \right. \end{equation*}

As can be seen from Example 3.6, the tightest robust approximation of a non-constant classifier may be multivalued over certain parts of the input domain.

In Theorem 3.7, assume that $C = \left\{{c_1, \ldots, c_k}\right\}$ . In case $\hat{N}$ is the tightest robust approximation $A_N^\circledcirc$ of $A_N$ , then $\hat{N}$ is required to be multivalued only over the boundaries of $N^{-1}(c_1), \ldots, N^{-1}(c_k)$ . In typical applications, these boundaries have Lebesgue measure zero, and the restriction of $A_N^\circledcirc$ over $[{-}M,M]^n$ is single-valued and equal to N, almost everywhere, e.g., as in Example 3.6. Computability of $A_N^\circledcirc$ , however, is an open problem in general (Moggi et al., Reference Moggi, Farjudian, Duracz and Taha2018, Reference Moggi, Farjudian, Taha, Margaria, Graf and Larsen2019a, b), and even in cases where it is known to be computable, little is known about complexity of its computation (Farjudian and Moggi, Reference Farjudian and Moggi2022). Thus, in practice, one aims for robust approximations $\hat{N}$ that are “sufficiently” tight, and over $[{-}M,M]^n$ , may be multivalued on sets that are not Lebesgue measure zero.

3.2 Regressors

Assume that a given feedforward regressor $N: [{-}M,M]^n \to {\mathbb{R}}$ has H hidden layers. We let $n_0 := n$ , and for each $1 \leq i \leq H$ , let $n_i$ denote the number of neurons in the i-th layer. Then, for each $1 \leq i \leq H$ , we represent:

• the weights as a matrix $W_i$ , with $\dim(W_i) = n_i \times n_{i-1}$ ;

• the biases as a vector $b_i$ , with $\dim(b_i) = n_i \times 1$ ;

• and the activation function (seen as a vector field) by $\sigma_i: {\mathbb{R}}^{n_i} \to {\mathbb{R}}^{n_i}$ .

From the last hidden layer to the single output neuron, the weights are represented as the vector $W_{H+1}$ , with $\dim(W_{H+1}) = 1 \times n_H$ , and there are no biases or activation functions. For any input $x \in [{-}M,M]^n$ , the output of the neural network may be calculated layer-by-layer as follows:

(11) \begin{equation} \left\{ \begin{array}{l@{\quad}l} Z_0( x) = x,\\ \hat{Z}_i(x) = W_i Z_{i-1}(x) + b_i, & (1 \leq i \leq H),\\ Z_i(x) = \sigma_i \left(\hat{Z}_i(x) \right), & (1 \leq i \leq H),\\ N(x) = W_{H+1} Z_H (x). \end{array} \right.\end{equation}

We let ${\mathcal F}$ denote the class of feedforward networks with a single output neuron and Lipschitz continuous activation functions, whose output may be described using (11). Each $N \in {\mathcal F}$ is continuous. Hence, it maps the compact set $[{-}M,M]^n$ to a compact set, which we assume is included in $[{-}M',M']$ , for M’ large enough.

This is in contrast with the case of classifiers, where the network itself did not have to be even continuous, and we had to be content with tightest robust approximations.

Of particular interest is the interval enclosure of a given regressor, which may not be the tightest robust extension of the regressor, but it is a robust extension nonetheless, which is commonly used in interval analysis of neural networks:

• ${(\cdot)}^\Box : {\mathbb{K}}{[{-}M,M]^n} \to {\mathbb{I}}{[{-}M,M]^n}$ is the hyperbox closure map of (4).

• $\tilde{N}: {\mathbb{I}}{[{-}M,M]^n} \to {\mathbb{I}}{[{-}M',M']}$ is any Scott-continuous extension of N, e.g., the canonical interval extension of Definition 2.20 (iii).

• $\iota : {\mathbb{I}}{[{-}M',M']} \to {\mathbb{K}}{[{-}M',M']}$ is the embedding of the sub-domain ${\mathbb{I}}{[{-}M',M']}$ into ${\mathbb{K}}{[{-}M',M']}$ , as in Proposition 2.19.

Then, the map $\hat{N}: {\mathbb{K}}{[{-}M,M]^n} \to {\mathbb{K}}{[{-}M',M']}$ defined by $\forall X \in {\mathbb{K}}{[{-}M,M]^n}: \hat{N}(X) := \iota(\tilde{N}({X}^\Box))$ is a robust extension of N.

4.1 Lipschitz continuity and Lipschitz constant

Assume that $(X, \delta_X)$ and $(Y, \delta_Y)$ are two metric spaces, $f : X \to Y$ , and $A \subseteq X$ . A real number $L \geq 0$ is said to be a Lipschitz bound for f over A if:

(12) \begin{equation} \forall a, b \in A: \delta_Y(f(a), f(b)) \leq L \delta_X( a, b).\end{equation}

A function for which such a bound exists is said to be Lipschitz continuous over A, and the smallest L satisfying (12) – which must exist – is called the Lipschitz constant of f over A.

If f is Lipschitz continuous over A, then it is also continuous over A, although the converse is not true. For instance, the function $h : \ell^1_\infty \to \ell^1_\infty$ , defined by $h(x) := \sqrt{{\lvert{x}\rvert}}$ , is continuous everywhere, but not Lipschitz continuous over any neighborhood of 0. Whenever X is a compact subset of $\ell^n_\infty$ and $f: X \to {\mathbb{R}}$ is continuously differentiable, then f is Lipschitz over X, but again, the converse is not true. For example, the function $h: [{-}1,1] \to {\mathbb{R}}$ , defined by $h(x) := {\lvert{x}\rvert}$ , is Lipschitz continuous, but not differentiable at 0. To summarize, we have the following inclusions for functions over compact subsets of ${\mathbb{R}}^n$ :

\begin{equation*} \text{Continuously Differentiable} \subset \text{Lipschitz Continuous} \subset \text{Continuous}.\end{equation*}

Even though the inclusions are strict, there is a close relationship between Lipschitz continuity and differentiability. Rademacher’s theorem states that if f is Lipschitz continuous over an open subset A of ${\mathbb{R}}^n$ , then it is (Fréchet) differentiable almost everywhere (with respect to the Lebesgue measure) over A (Clarke et al., Reference Clarke, Ledyaev, Stern and Wolenski1998, Corollary 4.19). For example, the function ${\mathrm{ReLU}}$ – defined by $\forall x \in {\mathbb{R}}: {\mathrm{ReLU}}(x) := \max( 0, x)$ – is (classically) differentiable everywhere except at zero:

\begin{equation*} ({\mathrm{ReLU}})'(x) = \left\{ \begin{array}{l@{\quad}l} 1, & \text{if } x > 0, \\ 0, & \text{if } x < 0, \\ \mathrm{undefined}, & \text{if } x = 0. \end{array} \right.\end{equation*}

4.2 Clarke-gradient

Although ${\mathrm{ReLU}}$ is not differentiable at 0, one-sided limits of the derivative exist, i.e.,:

(13) \begin{equation} \lim_{x \to 0^-} ({\mathrm{ReLU}})'(x) = 0 \quad \text{and} \quad \lim_{x \to 0^+} ({\mathrm{ReLU}})'(x) = 1.\end{equation}

As shown in Figure 1 (Left), in geometric terms, for every $\lambda \in [0,1]$ , the line $y_\lambda(x) := \lambda x$ satisfies:

(14) \begin{equation} y_\lambda (0) = {\mathrm{ReLU}} (0) \quad \wedge \quad \forall x \in {\mathbb{R}}: y_\lambda(x) \leq {\mathrm{ReLU}} (x).\end{equation}

The interval [0, 1] is the largest set of $\lambda$ values which satisfy (14) and may be considered as a gradient set for ${\mathrm{ReLU}}$ at zero. The concept of generalized (Clarke) gradient formalizes this idea. The Clarke-gradient may be defined for functions over any Banach space X (Clarke, Reference Clarke1990, p. 27). When X is finite-dimensional, however, the Clarke-gradient of $f: X \to {\mathbb{R}}$ has a simpler characterization. Assume that X is an open subset of ${\mathbb{R}}^n$ , and $f: X \to {\mathbb{R}}$ is Lipschitz continuous over X. By Rademacher’s theorem, we know that the set ${\mathcal N}_f := \{{z \in X}\mid{f \text{ is not differentiable at } z \, }\}$ has Lebesgue measure zero. The Clarke-gradient $\partial{f}(x)$ of f at any $x \in X$ satisfies:

(15)

Equation (15) should be interpreted as follows: take any sequence $(x_i)_{i \in {\mathbb{N}}}$ , which completely avoids the set ${\mathcal N}_f$ of points where f is not differentiable, converges to x, and for which $\lim_{i \to \infty} \nabla f(x_i)$ exists. The Clarke-gradient is the convex hull of these limits (Clarke, Reference Clarke1990, p. 63). It should be clear from (13) and (15) that $\partial{{\mathrm{ReLU}}}(0) = [0,1]$ .

Figure 1. Left: The function ${\mathrm{ReLU}}: (-2,2) \to {\mathbb{R}}$ (solid black) and the line $y_\lambda$ (in ) from (14) for some $\lambda \in [0,1]$ , which lies entirely in the shaded cone. Right: The step function $b \chi_O: (-2,2) \to {\mathbb{I}}{\mathbb{R}}_\bot$ in which $b = [0,1]$ and $O = (-1,1)$ . We have ${\mathrm{ReLU}} \in \delta(O,b)$ .

1. (i) $\partial{f}(x)$ is a non-empty, convex, and compact subset of ${\mathbb{R}}^n$ .

2. (ii) $\forall \xi \in \partial{f}(x): {\left\lVert{\xi}\right\rVert}_{p'} \leq L$ , in which p’ is the conjugate of p as defined in (1).

The following chain rule for the Clarke-gradient is crucial for proving the soundness of methods such as Chaudhuri et al. (Reference Chaudhuri, Gulwani and Lublinerman2012, Reference Chaudhuri, Gulwani, Lublinerman and Navidpour2011), Jordan and Dimakis (Reference Jordan and Dimakis2020, Reference Jordan, Dimakis, Meila and Zhang2021), Laurel et al. (Reference Laurel, Yang, Singh and Misailovic2022):

Proposition 4.3. (Chain Rule, Clarke Reference Clarke1990, Theorem 2.3.9). Assume that $X \subseteq {\mathbb{R}}^n$ is open, $h = (h_1, \ldots, h_m): X \to {\mathbb{R}}^m$ is Lipschitz continuous in a neighborhood of $x \in X$ , and $g : {\mathbb{R}}^m \to {\mathbb{R}}$ is Lipschitz continuous in a neighborhood of $h(x) \in {\mathbb{R}}^m$ . Then, $f := g \circ h$ is Lipschitz in a neighborhood of x, and we have:

(16)

in which, $\overline{\text{co}}$ denotes the closed convex hull.

Unlike the chain rule for the classical derivative, the chain rule of (16) provides only a subset relation, which, in general, is not an equality. A simple example of lack of equality is shown in Figure 3, which demonstrates an instance of the dependency problem (see Section 4.6.2).

4.3 Domain-theoretic L-derivative

We recall the concept of a domain-theoretic derivative for a function $f: U \to {\mathbb{R}}$ defined on an open set $U \subseteq {\mathbb{R}}^n$ . Assume that $(X, \Omega)$ is a topological space, and $(D, \sqsubseteq)$ is a pointed dcpo, with bottom element $\bot$ . Then, for any open set $O \in \Omega$ , and any element $b \in D$ , we define the single-step function $b \chi_O: X \to D$ as follows:

(17) \begin{equation} b \chi_O (x) := \left\{ \begin{array}{l@{\quad}l} b, & \text{if } x \in O,\\ \bot, & \text{if } x \in X \setminus O. \end{array} \right.\end{equation}

1. (i) The single-step tie $\delta(O, b)$ is the set of all functions $f : U \to {\mathbb{R}}$ that satisfy $\forall x, y \in O: b(x-y) \sqsubseteq f(x) - f(y)$ . The set $b(x-y)$ is obtained by taking the inner product of every element of b with the vector $x-y$ , and $\sqsubseteq$ is the reverse inclusion order on ${\mathbb{C}}{{\mathbb{R}}^1_\bot}$ .

2. (ii) The L-derivative of any function $f: U \to {\mathbb{R}}$ is defined as:

   $$L(f): = \bigsqcup \{ b{\chi _O}|O \subseteq U,b \in _ \bot ^n,f \in \delta (O,b)\} .$$
   As such, the map L has type $L: (U \to {\mathbb{R}}) \to (U \to {\mathbb{C}}{{\mathbb{R}}^n_\bot})$ .

Example 4.5. Assume that $U = (-2,2)$ . Consider the function ${\mathrm{ReLU}}: (-2,2) \to {\mathbb{R}}$ and the step function $b \chi_O: (-2,2) \to {\mathbb{I}}{\mathbb{R}}_\bot$ with $b = [0,1]$ and $O = (-1,1)$ , as depicted in Figure 1. We have ${\mathrm{ReLU}} \in \delta(O,b)$ . In fact, we have ${\mathrm{ReLU}} \in \delta(O,b)$ for any open set O containing zero, e.g., the interval $(-2^n, 2^n)$ , for any $n \in {\mathbb{N}}$ . Thus, one may verify that:

\begin{equation*} \forall x \in (-2,2): \quad L({\mathrm{ReLU}})(x) = \left\{ \begin{array}{l@{\quad}l} 0, & \text{if } x < 0,\\ {[0,1]}, & \text{if } x = 0,\\ 1, & \text{if } x > 0. \end{array} \right. \end{equation*}

For any $f: U \to {\mathbb{R}}$ , the L-derivative $L(f): U \to {\mathbb{C}}{{\mathbb{R}}^n_\bot}$ is Euclidean-Scott-continuous. When f is classically differentiable at $x \in U$ , the L-derivative and the classical derivative coincide (Edalat, Reference Edalat2008). Many of the fundamental properties of the classical derivative can be generalized to the domain-theoretic one, e.g., additivity and the chain rule (Edalat and Pattinson, Reference Edalat and Pattinson2005). Of particular importance to the current paper is the following result:

In this paper, instead of working with the general convex sets in ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ , we work with the simpler hyperboxes in ${\mathbb{I}}{\mathbb{R}}^n_\bot$ :

Definition 4.7. ( $\hat{L}$ -derivative). Let $U \subseteq {\mathbb{R}}^n$ be an open set. In the definition of L-derivative (Definition 4.4), if ${\mathbb{C}}{{\mathbb{R}}^n_\bot}$ is replaced with ${\mathbb{I}}{\mathbb{R}}^n_\bot$ , then we obtain the concept of $\hat{L}$ -derivative for functions of type $U \to {\mathbb{R}}$ . For vector valued functions $f = ( f_1, \ldots, f_m): U \to {\mathbb{R}}^m$ , we define:

\begin{equation*} \hat{L}(f) := \left( \hat{L}(f_1), \ldots, \hat{L}(f_m) \right)^\intercal, \end{equation*}

in which, $(\cdot)^\intercal$ denotes the transpose of a matrix. In other words, for each $1 \leq i \leq m$ and $x \in U$ , let $\hat{L}(f_i)(x) \equiv ( \alpha_{i,1}, \ldots, \alpha_{i,n}) \in {\mathbb{I}}{\mathbb{R}}^n_\bot$ . Then, for each $x \in U$ , $\hat{L}(f)(x)$ is the $m \times n$ interval matrix $[\alpha_{i,j}]_{1 \leq i \leq m, 1 \leq j \leq n}$ .

The $\hat{L}$ -derivative is, in general, coarser than the L-derivative, as the following example demonstrates:

As such, we lose some precision by using hyperboxes. On the other hand, on hyperboxes, the chain rule has a simpler form:

Lemma 4.9. (Chain Rule on Hyperboxes, Edalat and Pattinson Reference Edalat and Pattinson2005, Lemma 3.3). For any two functions $g : {\mathbb{R}}^k \to {\mathbb{R}}^m$ and $f: {\mathbb{R}}^m \to {\mathbb{R}}^n$ , we have:

\begin{equation*} \hat{L}(f)(g(x)) \times \hat{L}(g)(x) \sqsubseteq \hat{L}(f \circ g)(x). \end{equation*}

4.4 Maximization algorithm

For any closed compact interval I, we write $I = [\underline{I}, \overline{I}]$ . Assume that $f : {\mathbb{R}}^n \to {\mathbb{I}}{\mathbb{R}}^1_\bot$ and $X \subseteq {\mathbb{R}}^n$ is a compact set. We define the maximum value and maximum set of f over X as follows:

(18) $$\left\{ {\matrix{ {{\rm{ma}}{{\rm{x}}_{\rm{V}}}(f,X): = [\underline{z}, \bar z],\quad {\rm{where}}\, \underline{z} : = {{\sup }_{x \in X}}\underline {f(x)} \,{\rm{and}}\,\bar z: = {{\sup }_{x \in X}}\overline {f(x)} } \hfill \cr {{\rm{ma}}{{\rm{x}}_{\rm{S}}}(f,X): = \{ x \in X|f(x) \cap {\rm{ma}}{{\rm{x}}_{\rm{V}}}(f,X) \ne \emptyset \} .} \hfill \cr } } \right.$$

For any $\hat{f}: {\mathbb{I}}{\mathbb{R}}^n_\bot \to {\mathbb{I}}{\mathbb{R}}^1_\bot$ , the maximum value and maximum set are defined by considering the restriction of $\hat{f}$ over the maximal elements ${\mathbb{R}}^n$ .

Thus, in the sequel, we consider the functionals:

$$\left\{ {\matrix{ {{\rm{ma}}{{\rm{x}}_{\rm{V}}}:\quad (\mathbb{IR}_ \bot ^n \Rightarrow \mathbb{IR}_ \bot ^1) \to \mathbb{KR}_ \bot ^n \to \mathbb{IR}_ \bot ^1,} \hfill \cr {{\rm{ma}}{{\rm{x}}_{\rm{S}}}:\quad (\mathbb{IR}_ \bot ^n \Rightarrow \mathbb{IR}_ \bot ^1) \to \mathbb{KR}_ \bot ^n \to \mathbb{KR}_ \bot ^n,} \hfill \cr } } \right.$$

which, given any Scott-continuous $f \in {\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ and compact set $X \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , return the maximum value and the maximum set of f over X, respectively. As an example, assume that $f \in {\mathbb{I}}{\mathbb{R}}^1_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ is the canonical interval extension of $\partial{{\mathrm{ReLU}}}: {\mathbb{R}} \to {\mathbb{I}}{\mathbb{R}}^1_\bot$ . Then:

(19) \begin{equation} \left\{ \begin{array}{l@{\quad}l} \text{max}_{\rm V}(f,[{-}1,1]) = [1,1], & \text{max}_{\rm S}(f,[{-}1,1]) = [0,1], \\\text{max}_{\rm V}(f,[{-}1,0]) = [0,1], & \text{max}_{\rm S}(f,[{-}1,0]) = [{-}1,0], \\ \end{array} \right.\end{equation}

which shows that the functionals $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ are not even monotone in their second argument. For fixed second arguments, however, both are Scott-continuous in their first argument:

To prove monotonicity, assume that $f,g \in {\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ , and $f \sqsubseteq g$ . Thus, $\forall x \in X: (\underline{f(x)} \leq \underline{g(x)}) \wedge (\overline{g(x)} \leq\overline{f(x)})$ . This implies that $\sup_{x \in X} \underline{f(x)} \leq \sup_{x \in X} \underline{g(x)}$ and $\sup_{x \in X} \overline{g(x)} \leq \sup_{x \in X} \overline{f(x)}$ . Therefore:

(20) \begin{equation} \text{max}_{\rm V}(f,X) \sqsubseteq \text{max}_{\rm V}(g,X). \end{equation}

Take an arbitrary $x \in \text{max}_{\rm S}(g,X)$ . By definition, we must have $g(x) \cap \text{max}_{\rm V}(g,X) \neq \emptyset$ , which, combined with the assumption $f \sqsubseteq g$ , implies $f(x) \cap \text{max}_{\rm V}(g,X) \neq \emptyset$ . This, combined with (20), entails that $f(x) \cap \text{max}_{\rm V}(f,X) \neq \emptyset$ . Hence, $x \in \text{max}_{\rm S}(f,X)$ , and we have $\text{max}_{\rm S}(f,X) \sqsubseteq \text{max}_{\rm S}(g,X)$ . The proof of monotonicity is complete.

Next, let $(f_n)_{n \in {\mathbb{N}}}$ be an $\omega$ -chain in ${\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ , with $f = \bigsqcup_{n \in {\mathbb{N}}} f_n$ . As both $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ are monotone, we have $\bigsqcup_{n \in {\mathbb{N}}} \text{max}_{\rm V}(f_n,X) \sqsubseteq \text{max}_{\rm V}(f,X)$ and $\bigsqcup_{n \in {\mathbb{N}}} \text{max}_{\rm S}(f_n,X) \sqsubseteq \text{max}_{\rm S}(f,X)$ . Thus, it only remains to prove the $\sqsupseteq$ direction in both cases. In what follows, we let $z \equiv [\underline{z}, \overline{z}] := \text{max}_{\rm V}(f,X)$ , and for each $n \in {\mathbb{N}}$ , $z_n \equiv [\underline{z_n}, \overline{z_n}] := \text{max}_{\rm V}(f_n,X)$ .

maxV: First, consider an arbitrary $t < \underline{z}$ and let $\epsilon := (\underline{z}-t)/4$ . By definition, $\exists x_0 \in X: \underline{f(x_0)} > \underline{z} - \epsilon$ . Since $f = \bigsqcup_{n \in {\mathbb{N}}} f_n$ , we must have $\exists N_0 \in {\mathbb{N}}: \forall n \geq N_0: \underline{f_n(x_0)} > \underline{f(x_0)} - \epsilon > \underline{z}- 2\epsilon > t$ . Therefore:

(21) \begin{equation} \forall t \in {\mathbb{R}}: \quad t < \underline{z} \implies \exists n \in {\mathbb{N}}: t < \underline{z_n}. \end{equation}

Next, we consider an arbitrary $t > \overline{z}$ and let $\epsilon := (t - \overline{z})/4$ . Since $f = \bigsqcup_{n \in {\mathbb{N}}} f_n$ , we must have:

\begin{equation*} \forall x \in X: \exists N_x \in {\mathbb{N}}: \forall n \geq N_x: \quad \overline{f_n(x)} < \overline{f(x)} + \epsilon. \end{equation*}

For each fixed $n \in {\mathbb{N}}$ , the map $\overline{f_n}$ is upper semi-continuous. Hence, for any $x \in X$ , there exists a neighborhood $O_x$ of x for which we have

\begin{equation*} \forall y \in O_x: \quad \overline{f_{N_x}(y)} < \overline{f_{N_x}(x)} + \epsilon < \overline{f(x)} + 2\epsilon \leq \overline{z} + 2\epsilon. \end{equation*}

As the sequence $(f_n)_{n \in {\mathbb{N}}}$ is assumed to be an $\omega$ -chain, the upper bounds $(\overline{f_n})_{n \in {\mathbb{N}}}$ are non-increasing, and we obtain:

\begin{equation*} \forall x \in X: \forall y \in O_x: \forall n \geq N_x: \quad \overline{f_n(y)} \leq \overline{z} + 2\epsilon. \end{equation*}

As X is compact and $X \subseteq \bigcup_{x \in X} O_x$ , then for finitely many neighborhoods $\left\{{O_{x_1}, \ldots, O_{x_m}}\right\}$ we must have $X \subseteq \bigcup_{i=1}^m O_{x_i}$ . Thus, if we let $N_0 := \max\left\{{N_{x_1}, \ldots, N_{x_m}}\right\}$ , we must have:

\begin{equation*} \forall n \geq N_0: \forall y \in X: \quad \overline{f_n(y)} \leq \overline{z} + 2\epsilon, \end{equation*}

which entails that:

(22) \begin{equation} \forall t \in {\mathbb{R}}: \quad \overline{z} < t \implies \exists N_0 \in {\mathbb{N}}: \forall n \geq N_0: \overline{z_n} \leq \overline{z} + 2\epsilon < t. \end{equation}

From (21) and (22), we deduce that $\bigsqcup_{n \in {\mathbb{N}}} \text{max}_{\rm V}(f_n,X) \sqsupseteq \text{max}_{\rm V}(f,X)$ .

maxS: Assume that $x \in X \setminus \text{max}_{\rm S}(f,X)$ , which implies that $\overline{f(x)} < \underline{z}$ . Take $\epsilon = \left(\underline{z} - \overline{f(x)}\right)/4$ . Since $\underline{z} = \sup_{y \in X} \underline{f(y)}$ , there exists $x_1 \in X$ such that $\underline{f(x_1)} > \underline{z} - \epsilon$ . As $f = \bigsqcup_{n \in {\mathbb{N}}} f_n$ , there exists $N_1 \in {\mathbb{N}}$ , such that:

(23) \begin{equation} \forall n \geq N_1: \underline{f_n(x_1)} > \underline{f(x_1)} - \epsilon > \underline{z} - 2\epsilon. \end{equation}

Furthermore, there exists $N_2 \in {\mathbb{N}}$ , such that:

(24) \begin{equation} \forall n \geq N_2: \overline{f_n(x)} < \overline{f(x)} + \epsilon = \underline{z} - 3\epsilon. \end{equation}

Taking $N_3 = \max\left\{{N_1,N_2}\right\}$ , from (23) and (24), we obtain $\forall n \geq N_3: \overline{f_n(x)} < \underline{f_n(x_1)} \leq \underline{z_n}$ , which implies that $x \not \in \text{max}_{\rm S}(f_n,X)$ . Therefore, $\bigsqcup_{n \in {\mathbb{N}}} \text{max}_{\rm S}(f_n,X) \sqsupseteq \text{max}_{\rm S}(f,X)$ .

The proof is complete.

Algorithm 1 presents an implementation of $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ , which we use in our experiments. As pointed out before, the set $B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ , consisting of finite unions of rational hyperboxes, forms a countable basis for the $\omega$ -continuous domain ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ . We have used this fact in Algorithm 1, which outputs enclosures of the maximum set using elements of $B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ .

Apart from the function f and the neighborhood X, Algorithm 1 requires three other input parameters: $m_{\iota}$ , $m_b$ , and $\delta$ . These are crucial for making sure that the algorithm stops below a reasonable threshold. In line 9, we discard all the boxes in $B_i$ which cannot possibly contain any maximum points. How many boxes are discarded depends on a number of factors, most importantly, whether the function f has a flat section around the maximum set or not. The condition $2^n{\lvert{\hat{B}_i}\rvert} \geq m_b$ in line 13 is necessary, because in cases where flat sections exist, the number of elements of $\hat{B}_i$ grows exponentially with each iteration. In the if statement of line 10, we make sure that bisecting $\hat{B}_i$ will not create too many boxes before executing the bisection command in the first place. In the current article, by bisecting a box $b \subseteq {\mathbb{R}}^n$ we mean bisecting along all the n coordinates, which generates $2^n$ boxes. As such, in line 11 of the algorithm, every box in $\hat{B}_i$ is bisected along all the n coordinates, and the result is assigned to $B_{i+1}$ . Figure 2 depicts the first three bisections for a scenario in which $n=2$ . Note that, due to the overlap of the interval values of the function f in adjacent boxes, when extracting $\hat{B}_2$ from $B_2$ (which is performed in line 9) no boxes may be discarded, and the earliest that a box may be discarded is when $\hat{B}_3$ is extracted from $B_3$ .

Figure 2. The dynamics of the first three bisections of Algorithm 1 for a two-dimensional scenario. The starting search region X is shown in blue color for reference.

Algorithm 1 closely follows the definition of $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ given in (18). Hence, the proof of soundness is straightforward:

For the inductive step, assume that at iteration i, before the start of the loop, the finite set of hyperboxes $B_i$ satisfies $\text{max}_{\rm S}(f,X) \subseteq \bigcup B_i$ . Assume that $\tilde{f} \in X \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ is the restriction of the interval function f over the maximal elements X, which entails that f is an interval approximation of $\tilde{f}$ . As a result, $I_i$ is an overapproximation of $\text{max}_{\rm V}(\tilde{f},X)$ , which entails that the set $\hat{B}_i$ – obtained in line 9 – is an overapproximation of $\text{max}_{\rm S}(\tilde{f},X)$ .

The fact that the algorithm always halts in finite time follows from the stopping condition $i \geq m_{\iota}$ of line 13.

The algorithm combines the definition of $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ given in (18) with a bisection operation that increases the accuracy at each iteration. We prove that the result can be obtained to any degree of accuracy. To that end, we first define the following sequence:

(25) \begin{equation} \left\{ \begin{array}{l@{\quad}l} X_0 := X, &\\ X_{i+1} := \texttt{bisect}(X_i), & (\forall i \in {\mathbb{N}}). \end{array} \right.\end{equation}

It is straightforward to verify that $\forall i \in {\mathbb{N}}: \hat{B}_i \subseteq B_i \subseteq X_i$ .

Theorem 4.13. (Completeness). Assume that $\Psi(\hat{f}, X, m_{\iota}, m_b, \delta)$ is the function implemented by Algorithm 1. For any given $\hat{f}$ and X:

\begin{equation*} \lim_{m_{\iota} \to \infty, m_b \to \infty, \delta \to 0} \Psi(\hat{f}, X, m_{\iota}, m_b, \delta) = \left( \text{max}_{\rm V}(\hat{f},X), \text{max}_{\rm S}(\hat{f},X) \right). \end{equation*}

maxV: Let $z \equiv [\underline{z}, \overline{z}] := \text{max}_{\rm V}(\hat{f},X)$ . As $\overline{f}$ is upper semi-continuous, then it must attain its maximum over the compact set X at some point $x_0 \in X$ . Thus, $\forall x \in X: \overline{f}(x) \leq \overline{f}(x_0)$ . Suppose that $\epsilon > 0$ is given. By upper semi-continuity of $\overline{f}$ , for each $x \in X$ , there exists a neighborhood $O_x$ such that $\forall y \in O_x: \overline{f}(y) \leq \overline{f}(x) + \epsilon \leq \overline{f}(x_0) + \epsilon$ . By referring to the sequence $(X_i)_{i \in {\mathbb{N}}}$ defined in (25), and using Scott continuity of $\hat{f}$ , we deduce that, for each $x \in X$ , there exists a sufficiently large index $N_x \in {\mathbb{N}}$ that satisfies the following property:

• There exist $k_x \in {\mathbb{N}}$ and finitely many boxes $\{{B_{N_x, j}}\mid{ 1 \leq j \leq k_x}\}$ such that:

  (26) \begin{equation} x \in \left(\bigcup_{1 \leq j \leq k_x} B_{N_x, j}\right)^{\circ} \text{ and } \left(\bigcup_{1 \leq j \leq k_x} B_{N_x, j}\right) \subseteq O_x, \end{equation}
  in which $(\cdot)^{\circ}$ denotes the interior operator, and:
  (27) \begin{equation} \forall j \in \left\{{1, \ldots, k_x}\right\}: \quad \overline{\hat{f}(B_{N_x, j})} \leq \overline{f}(x_0) + \epsilon. \end{equation}

  The reason for considering $k_x$ boxes $\{{B_{N_x, j}}\mid{ 1 \leq j \leq k_x}\}$ – rather than just one box – is that, if at some iteration $i_0 \in {\mathbb{N}}$ , the point x lies on the boundary of a box in $X_{i_0}$ , then it will remain on the boundary of boxes in $X_i$ for all $i \geq i_0$ . In other words, it will not be in the interior of any boxes in subsequent bisections. Of course, as each point $x \in X$ may be on the boundaries of at most $2^n$ adjacent boxes in $X_{N_x}$ , then we can assume that $k_x \leq 2^n$ .

  For each $x \in X$ , we define the neighborhood $U_x := (\bigcup_{1 \leq j \leq k_x} B_{N_x, j})^{\circ}$ . As X is compact, then it may be covered by finitely many of such neighborhoods (say) $\{{U_{x_i}}\mid{1 \leq i \leq m}\}$ . By taking $N = \max_{1 \leq i \leq m} N_{x_i}$ , and considering (27), we deduce that, at the latest, at iteration N, we must have $\overline{z_i} \leq \overline{z} + \epsilon$ .

  One the other hand, as $\underline{z} := \sup_{x \in X} \underline{f(x)}$ , for some $x'_0 \in X$ we must have $\underline{f}(x'_0) > \underline{z} -\epsilon/2$ . Furthermore, as $\underline{f}$ is lower semi-continuous, then for some neighborhood $X'_0$ of $x'_0$ , we must have $\forall y \in X'_0: \underline{f}(y) > \underline{f}(x'_0) - \epsilon/2 > \underline{z}- \epsilon$ . Similar to the previous argument, we can show that, after a sufficient number of bisections, we must have $\underline{z_i} \geq \underline{z} - \epsilon$ .

maxS: Assume that $x \in X \setminus \text{max}_{\rm S}(\hat{f},X)$ . By Proposition 4.10, the set $\text{max}_{\rm S}(\hat{f},X)$ is compact. Hence, the set $X \setminus \text{max}_{\rm S}(\hat{f},X)$ is relatively open in X and there exists a neighborhood $O_x$ of x which satisfies $\forall y \in O_x: \overline{f}(y) < \underline{z}$ . Once again, we can show that, after a sufficiently large number of bisections (say) $N_x$ , for a finite number of boxes, the relation (26) holds. This entails that all these boxes must be discarded in line 9 of the algorithm after at most $N_x$ iterations.

4.5 Computability

For computable analysis of the operators discussed in this work, we need an effective structure on the underlying domains. To that end, we use the concept of effectively given domains (Smyth, Reference Smyth1977). Specifically, we follow the approach taken in Edalat and Sünderhauf (Reference Edalat and Sünderhauf1999, Section 3).

Assume that $(D, \sqsubseteq)$ is an $\omega$ -continuous domain, with a countable basis B that is enumerated as follows:

(28) \begin{equation} B = \left\{{ b_0 = \bot, b_1, \ldots, b_n, \ldots}\right\}. \end{equation}

We say that the domain D is effectively given with respect to the enumeration (28), if the set $\{{(i,j) \in {\mathbb{N}} \times {\mathbb{N}}}\mid{ b_i \ll b_j}\}$ is recursively enumerable, in which $\ll$ is the way-below relation on D.

In the following proposition, computability is to be understood according to Type-II Theory of Effectivity (Weihrauch, Reference Weihrauch2000).

Arithmetic operators have computable domain-theoretic extensions, so do the common activation functions (e.g., ${\mathrm{ReLU}}$ , $\tanh$ , or sigmoidal) and their Clarke-gradients (Edalat, Reference Edalat1995b; Edalat et al., Reference Edalat, Lieutier and Pattinson2013; Escardó,Reference Escardó1996; Farjudian, Reference Farjudian2007). Our aim here is to study the computability of $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ . From (19), we know that $\text{max}_{\rm V}$ and $\text{max}_{\rm S}$ are not even monotone in their second argument, while, by Theorem 4.11, if we fix $X \in {\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , then the functionals $\text{max}_{\rm V}(\cdot , X): ({\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow{\mathbb{I}}{\mathbb{R}}^1_\bot) \to {\mathbb{I}}{\mathbb{R}}^1_\bot$ and $\text{max}_{\rm S}( \cdot, X): ({\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow{\mathbb{I}}{\mathbb{R}}^1_\bot) \to {\mathbb{K}}{{\mathbb{R}}^n_\bot}$ are Scott-continuous. By referring to Proposition 4.14, to study computability of these two operators, we need to enumerate specific countable bases for the domains ${\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ , ${\mathbb{I}}{\mathbb{R}}^1_\bot$ , and ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ . We already know that $B_{{\mathbb{I}}{\mathbb{R}}^1_\bot}$ and $B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ form countable bases for ${\mathbb{I}}{\mathbb{R}}^1_\bot$ and ${\mathbb{K}}{{\mathbb{R}}^n_\bot}$ , respectively, and it is straightforward to effectively enumerate them.

For the function space ${\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ , we use the common step function construction. Recall the single-step functions as defined in (17). Assume that D and E are two domains. For any $d \in D$ and $e \in E$ , we must have:

Note that, by Proposition 2.10, the set

is Scott open. By a step function we mean the supremum of a finite set of single-step functions. If the domains D and E are bounded-complete, with countable bases $B_D = \left\{{d_0, \ldots, d_n, \ldots}\right\}$ and $B_E = \left\{{e_0, \ldots, e_n, \ldots}\right\}$ , then the countable collection:

(29)

forms a basis for the function space $D \Rightarrow E$ (Abramsky and Jung, Reference Abramsky, Jung, Abramsky, Gabbay and Maibaum1994, Section 4).

Thus, we consider the bases $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot}$ and $B_{{\mathbb{I}}{\mathbb{R}}^1_\bot}$ , and construct the countable basis $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot}$ for the function space ${\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot$ , according to (29). The key point is that, each step function in $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot}$ maps any given hyperbox with rational coordinates to a rational interval. We formulate this as:

As such, we call the elements of $B_{{\mathbb{I}}{\mathbb{R}}^n_\bot \Rightarrow {\mathbb{I}}{\mathbb{R}}^1_\bot}$ rational step functions.

maxV: Assume that $e \equiv [\underline{e}, \overline{e}]\in B_{{\mathbb{I}}{\mathbb{R}}^1_\bot}$ , that is, e is a rational interval. To decide $e \ll \text{max}_{\rm V}(f, X) = [\underline{z}, \overline{z}]$ , we run Algorithm 1. As is Scott open, then by completeness (Theorem 4.13), after sufficiently large number of iterations (say) $N_f$ , we must have $\forall i \geq N_f: e \ll [{\underline{z_i}, \overline{z_i}}]$ . By (3), the relation $e \ll [{\underline{z_i}, \overline{z_i}}]$ is equivalent to $\underline{e} < \underline{z_i} \leq \overline{z_i} < \overline{e}$ . By Proposition 4.15, both $\underline{z_i}$ and $\overline{z_i}$ are rational numbers. Hence, deciding $e \ll [{\underline{z_i}, \overline{z_i}}]$ reduces to comparing rational numbers, which is decidable. Thus, overall, the problem $e \ll \text{max}_{\rm V}(f, X)$ is semi-decidable.

maxS: The proof for $\text{max}_{\rm S}$ is similar to that for $\text{max}_{\rm V}$ . Assume that $C \in B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ . To decide $C \ll \text{max}_{\rm S}(f, X) = K$ , we run Algorithm 1. As is Scott open, then by completeness (Theorem 4.13), after sufficiently large number of iterations (say) $N_f$ , we must have $\forall i \geq N_f: C \ll \hat{B}_i$ . By (3), the relation $C \ll \hat{B}_i$ is equivalent to $\hat{B}_i \subseteq C^{\circ}$ . As we have assumed that $C \in B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ and $X \in B_{{\mathbb{K}}{{\mathbb{R}}^n_\bot}}$ , then all the hyperboxes in C and $\hat{B}_i$ have rational coordinates. Therefore, deciding $C \ll \hat{B}_i$ reduces to comparing rational numbers, which is decidable. Thus, overall, the problem $C \ll \text{max}_{\rm S}(f, X)$ is semi-decidable.

4.6 Interval arithmetic

We will use arbitrary-precision interval arithmetic for approximation of the $\hat{L}$ -derivative. Interval arithmetic (Moore et al., Reference Moore, Kearfott and Cloud2009) is performed over sets of numbers, in contrast with the classical arithmetic which is performed over individual numbers. This allows incorporating all the possible errors (e.g., round-off and truncations errors) into the result. As an example, note that:

(30) \begin{equation} (x \in [\underline{I},\overline{I}]) \wedge (y \in [\underline{J},\overline{J}]) \implies x+y \in [\underline{I}+\underline{J}, \overline{I}+\overline{J}]. \end{equation}

In practice, inaccuracies enter into interval computations for a variety of reasons. The following are the most relevant to the current article:

4.6.2 Dependency

By default, interval computations do not take into account any dependencies between parameters. For instance, assume that $x \in [0,1]$ . By applying interval subtraction, we obtain $ (x-x) \in [{-}1,1]$ , which is a significant overestimation as $x-x=0$ , regardless of which interval x belongs to.

The dependency problem causes inaccuracies in estimations of the gradient as well. As an example, consider the neural network of Figure 3. This network computes the constant function $N(x)=0$ , for which we must have $\forall x \in {\mathbb{R}}: N'(x)=0$ . We estimate the Clarke-gradient of N using the chain rule, which, at $x = 0$ , leads to the following overestimation:

\begin{equation*}\partial{N}(0) \subseteq \partial{{\mathrm{ReLU}}}(0) - \partial{{\mathrm{ReLU}}}(0) = [0,1] -[0,1] = [-1,1].\end{equation*}

Figure 3. Dependency problem: The network computes the constant function zero. Hence, we must have $N'(x)=0$ for all x. But the Clarke-gradient at $x=0$ is overestimated as $[{-}1,1]$ .

Our framework, however, is not affected by the dependency problem for general position ${\mathrm{ReLU}}$ networks (Theorem 4.19) or differentiable networks (Theorem 4.21).

4.7 General position ReLU networks

We have pointed out the trade-off between accuracy and efficiency in using the hyperbox domain ${\mathbb{I}}{\mathbb{R}}^n_\bot$ and further sources of inaccuracy in interval arithmetic. In this section, we show that, for a broad class of ${\mathrm{ReLU}}$ networks, no accuracy is lost by using hyperboxes. Assume that $N : {\mathbb{R}}^n \to {\mathbb{R}}$ is a feedforward ${\mathrm{ReLU}}$ network with $k \geq 0$ hidden neurons (and an arbitrary number of hidden layers). For convenience, we adopt the set-theoretic definition of natural numbers (Jech, Reference Jech2002) for which we have $k = \left\{{0, \ldots, k-1}\right\}$ . For each $i \in k$ , let $\hat{z}_i : {\mathbb{R}}^n \to {\mathbb{R}}$ be the pre-activation function of the i-th hidden neuron and define:

(31) \begin{equation} H_i := \{{x \in {\mathbb{R}}^n}\mid{\hat{z}_i(x) = 0}\}.\end{equation}

The following definition, which we take from Jordan and Dimakis (Reference Jordan and Dimakis2020), is an adaptation of the concept of hyperplane arrangements in general position (Stanley, Reference Stanley2006):

A ${\mathrm{ReLU}}$ network $N : {\mathbb{R}}^n \to {\mathbb{R}}$ with $k \geq 0$ hidden neurons and only one hidden layer is in general position if for every $S \subseteq k$ , the intersection $\bigcap_{i \in S} H_i$ is an $(n - {\lvert{S}\rvert})$ -dimensional sub-space of ${\mathbb{R}}^n$ . For instance, when $n=2$ , the network is in general position if no two hyperplanes $H_i$ and $H_j$ (with $i \neq j$ ) are parallel and no three such hyperplanes meet at a point.

Recall that $\tilde{L}(N): {\mathbb{I}}{\mathbb{R}}^n_\bot \to {\mathbb{I}}{\mathbb{R}}^n_\bot$ denotes the interval approximation of the $\hat{L}$ -derivative of N obtained by applying the chain rule of Lemma 4.9. In general, we have:

(32) \begin{equation} \forall x \in {\mathbb{R}}^n: \quad \partial{N}(x) \subseteq \hat{L}(N)(x) \subseteq \tilde{L}(N)(\left\{{x}\right\}). \end{equation}

Over general position networks, however, we obtain equality:

As a result, we have an effective procedure which, for any given ${\mathrm{ReLU}}$ network $N : [{-}M,M]^n \to {\mathbb{R}}$ in general position, and any $X \in B_{{\mathbb{K}}{[{-}M,M]^n}}$ , returns the Lipschitz constant of N over X, and the set of points in X where the maximum Lipschitz values are attained, to within any degree of accuracy:

Theorem 4.19. Let $N : [{-}M,M]^n \to {\mathbb{R}}$ be a general position ${\mathrm{ReLU}}$ network and $X \in B_{{\mathbb{K}}{[{-}M,M]^n}}$ . Then:

$$\left\{ {\matrix{ {{\rm{ma}}{{\rm{x}}_{\rm{V}}}(\partial N,X) = {\rm{ ma}}{{\rm{x}}_{\rm{V}}}(\tilde L(N),X),} \hfill \cr {{\rm{ma}}{{\rm{x}}_{\rm{S}}}(\partial N,X){\rm{ }} = {\rm{ ma}}{{\rm{x}}_{\rm{S}}}(\tilde L(N),X),} \hfill \cr } } \right.$$

and both the maximum value and the maximum set can be obtained effectively to any degree of accuracy.

The network of Figure 3 is not in general position, and we have shown that Lemma 4.18 fails on that network. With respect to the Lebesgue measure over the parameter space, however, almost every ${\mathrm{ReLU}}$ network is in general position (Jordan and Dimakis, Reference Jordan and Dimakis2020, Theorem 4). In simple terms, we have a sound and effective procedure for Lipschitz analysis of networks, which provides the results to within any given degree of accuracy on almost every ${\mathrm{ReLU}}$ network.

4.8 Differentiable networks

One of the main strengths of our domain-theoretic framework is that it can handle differentiable and non-differentiable networks alike seamlessly. For differentiable networks as well, we obtain results similar to those for ${\mathrm{ReLU}}$ networks in general position.

We know that when N is classically differentiable at $x \in [{-}M,M]^n$ , the L-derivative and the classical derivative coincide (Edalat, Reference Edalat2008). Furthermore, for classically differentiable functions, the chain rules of Proposition 4.3 and Lemma 4.9 reduce to equalities which incur no overapproximation, because both sides are single-valued. As a result, we obtain:

Lemma 4.20. Let $N : [{-}M,M]^n \to {\mathbb{R}}$ be a differentiable feedforward network in ${\mathcal F}$ . Then:

\begin{equation*} \forall x \in [{-}M,M]^n:\quad \partial{N}(x) = \hat{L}(N)(x) = \tilde{L}(N)(\left\{{x}\right\}). \end{equation*}

In other words, $\hat{L}(N)$ is an interval extension of $\partial{N}$ .

Theorem 4.21. Let $N : [{-}M,M]^n \to {\mathbb{R}}$ be a differentiable feedforward network in ${\mathcal F}$ . Then:

$$\left\{ {\matrix{ {{\rm{ma}}{{\rm{x}}_{\rm{V}}}(\partial N,X){\rm{ }} = {\rm{ ma}}{{\rm{x}}_{\rm{V}}}(\tilde L(N),X),\,} \hfill \cr {{\rm{ma}}{{\rm{x}}_{\rm{S}}}(\partial N,X){\rm{ }} = {\rm{ ma}}{{\rm{x}}_{\rm{S}}}(\tilde L(N),X),} \hfill \cr } } \right.$$

and both the maximum value and the maximum set can be obtained effectively to any degree of accuracy.

5.1 Scalability: Input dimension

When no bisections are required, lipDT can be useful for analysis of larger networks as well, as shown in Experiment 2 on the MNIST dataset (Table 3). We consider a ${\mathrm{ReLU}}$ network of size [784, 10, 10, 2], only focusing on the digits 1 and 7 in the output. The radius of the input domain is $0.001$ . For this experiment, the input domain is not situated on the intersection of the hyperplanes, because lipDT would require bisecting along 784 dimensions, resulting in $2^{784}$ new hyperboxes in each bisection, which is prohibitively costly. Nonetheless, we observe that, even without any bisection, lipDT maintains correctness, while ZLip, lipMIP, and FastLip still underestimate the Lipschitz constant, albeit with small relative errors.

Table 3. Experiment 2: MNIST, ${\mathrm{ReLU}}$ , Layer sizes = [784, 10, 10, 2]

For a network with input dimension n, each bisection generates $2^n$ new hyperboxes. As such, under favorable conditions, our method is applicable to networks with input dimensions up to 10 or so. It is also possible to obtain more accurate results on networks with higher input dimensions using influence analysis (Wang et al., Reference Wang, Pei, Whitehouse, Yang and Jana2018). This can be achieved by a slight modification of Algorithm 1. The influence analysis technique, however, is only applicable on differentiable networks. Furthermore, this technique provides tighter bounds on the Lipschitz constant in a reasonable time, but may still provide a loose bound around the maximum set, i.e., the set where maximum Lipschitz constant is attained. Another simple way of increasing efficiency is through parallelization. The reason is that the algorithm is essentially performing a search. Once several hyperboxes are generated, they can be searched in a perfectly parallel way.

5.2 Scalability: Depth and width

As we have mentioned previously, validated methods of Lipschitz estimation (such as lipDT) cannot be scalable. Nonetheless, such methods are valuable for purposes such as:

1. (i) verification of other Lipschitz estimation methods, as demonstrated in Table 1;

2. (ii) robustness analysis of low-dimensional networks, especially those that appear in safety-critical applications, e.g., in power electronics (Dragičević et al., Reference Dragičević, Wheeler and Blaabjerg2019), analog-to-digital converters (Tankimanova and James, Reference Tankimanova, James and James2018), and solution of partial differential equations (PDEs) (Rudd, Reference Rudd2013);

3. (iii) investigation of how Lipschitz constant is affected by factors such as under-parametrization, over-parametrization, and through successive epochs of a training process.

Item (iii) is particularly relevant for networks with low input dimensions, but with depths and widths that can be very large relative to their input dimensions. The reasons is that, although lipDT does not scale well with input dimension of the network, it has a much better scalability with respect to the depth and width of the network.

To investigate how lipDT scales with the number of layers (i.e., depth) of the network, we generated some multilayer networks randomly with 4 input neurons, 2 output neurons, and various number of hidden layers (from 1 to 10), each containing 10 neurons. We took the input domain with center [1, 1, 1, 1] and radius $0.001$ , and compared the timing of lipDT with the other methods, except for Clever, which takes significantly longer than all the other methods. As can be seen in Figure 6, lipDT scales quite well – almost linearly – with the number of layers of the network.

Figure 6. lipDT scales (almost) linearly with respect to the number of layers.

Similarly, lipDT exhibits an almost linear scaling with respect to the width of the network. We ran an experiment in which we considered networks with two input neurons and one hidden layer of width $2^n$ , where n ranges from 1 to 10. The weights and biases were assigned randomly, and to make the comparison fair, we performed the same number of bisections for each experiment, making sure that the only contributing factor is the width. We recorded the time, and the result is shown in Figure 7.

Figure 7. lipDT scales almost linearly with respect to the width of the network.

5.3 Case study: Benign overfitting

The traditional consensus in machine learning postulated that over-parametrized models would have poor generalization properties. For instance, in a regression task over a dataset of size m, if the capacity K of the model were significantly larger than m, then the model would interpolate the dataset and would oscillate erratically over points outside of the dataset. In a fairly recent development, it has been observed that over-parametrized models can indeed exhibit superior generalization compared with under-parametrized ones, even over noisy data (Muthukumar et al., Reference Muthukumar, Vodrahalli, Subramanian and Sahai2020). An extensive account of this phenomenon – termed “benign overfitting” (Bartlett et al., Reference Bartlett, Long, Lugosi and Tsigler2020) – can be found in Belkin (Reference Belkin2021).

In this section, we investigate benign overfitting from the angle of Lipschitz analysis. To that end, we consider a univariate regression task similar to that depicted in Belkin (Reference Belkin2021, Figure 3.6). The dataset consists of eleven points over the interval $[{-}5,5]$ . We train a set of ${\mathrm{ReLU}}$ networks with one input neuron, one output neuron, and a hidden layer with width ranging from 3 to 3000 (in steps of 3). Even though lipDT scales well with respect to the depth of the network, for this experiment, we consider only networks with one hidden layer and take the width of the single hidden layer as the capacity of the network.

Each network is trained for 100, 000 epochs. Then, the Lipschitz constant is calculated over the interval $[{-}5,5]$ using lipDT. The result is shown in Figure 8 (Left). To show the initial dynamics more clearly, the right hand figure shows capacities only up to 200. The curve exhibits similar qualitative dynamics as that of the “double descent generalization curve” of Belkin (Reference Belkin2021, Figure 3.5). The under-parametrized regime extends from capacity 1 up to capacity 42, during which the Lipschitz constant has an increasing and then decreasing trend. Maximum Lipschitz constant is attained at capacity 21. Interpolation starts at capacity 42, after which the Lipschitz constant hovers around 4 until it flattens after capacity 125, and remains flat for the remaining capacities up to 3000.

Figure 8. Lipschitz constant versus capacity. Left: All the capacities up to 3000. Right: Capacities up to 200. The under-parametrized regime extends to capacity 42, where interpolation starts, and after which, the curve remains almost flat. Maximum Lipschitz constant is attained at capacity 21.

Figure 9 depicts the output of neural networks with capacities 3 and 21 (under-parametrized), 42 (interpolation threshold) and 200 (over-parametrized). The slope of the linear segment connecting the third and fourth data points (from left) is roughly 4. By the mean value theorem, it is clear that once interpolation starts, the Lipschitz constant may not go below this slope. As can be seen in Figure 8, in the over-parametrized regime, the Lipschitz constant stays very close to 4.

Figure 9. The dashed black curve is a piecewise linear interpolation of the data points. The blue curves show the output of the neural networks. Interpolation starts at capacity 42. The highest Lipschitz constant is attained at capacity 21. Also, see, Belkin (Reference Belkin2021, Figure 3.6).

We also investigate how the Lipschitz constant is affected through training. The results are shown in Figure 10. In particular, when the capacity is farther away from the interpolation threshold – e.g., for capacities 3 and 200 – the Lipschitz curve flattens at earlier epochs. In contrast, for capacities 21 and 42, optimization seems a slower and more demanding process, as the curve begins to flatten at much later epochs. We point out, however, that the optimization algorithm is randomized, and the network is initialized randomly. Hence, the exact dynamics of the Lipschitz constant versus epoch number – in particular, at what stage the curve begins to flatten – are subject to the initial weights and biases and the optimization process.

Figure 10. Dynamics of the Lipschitz constant through successive epochs. The network with three neurons reaches its best loss much earlier than 100, 000 epochs, so we stop the training at 20, 000 epochs.

5.3.1 Validated versus non-validated Lipschitz analysis

We ran our experiments using the validated method lipDT, and the non-validated statistical method Clever. The Lipschitz estimates returned by Clever are almost always a slight underestimate of those returned by lipDT. To be more precise, the average relative error of Clever is around $-1\%$ , calculated according to (33). In most cases, qualitatively, the results of Clever and lipDT are similar. Some outliers, however, were observed. For instance, Figure 11 (Left) shows the graph of the result returned by Clever when measuring the Lipschitz constant of the ${\mathrm{ReLU}}$ network with 3000 neurons, up to epoch 2500. At epoch 251, Clever estimated the Lipschitz constant to be around 127, while for all the other epochs, the Lipschitz constant was estimated below 4.

Figure 11. Left: Clever returns an outlier at epoch 251. Right: Comparison with lipDT (for the first 350 epochs) reveals that the Lipschitz constant does not have a jump.

Without recourse to a validated method, it is not possible to determine whether the Lipschitz constant truly has a jump at epoch 251 or not. The reason is that the commonly used optimization methods are randomized, which makes it possible to obtain genuine outliers at certain epochs. Nonetheless, running the same experiment with lipDT reveals that, at epoch 251, Clever indeed provides the wrong result. The interval returned by lipDT at epoch 251 is:

\begin{equation*}[2.445572934050687,2.445572934050933].\end{equation*}

Figure 11 (Right) depicts the results (for the first 350 epochs) of the comparison between lipDT and Clever.

While in experiments of Table 1 and Figure 5, the input domains were chosen deliberately to be small and to straddle the intersection of hyperplanes, in the experiment of Figure 11, the search was performed over the entire interval of $[{-}5,5]$ . This demonstrates that non-validated statistical methods may return misleading results even when the samples are taken from a large and seemingly non-critical part of a very low-dimensional input space.