The Termination Problem of Equality Saturation is Undecidable

Yihong Zhang

Aug 11, 2023

In this note, we study the decidability of the termination of equality saturation and related problems.


Term rewriting

A term rewriting system (TRS) \(R\) consists of a set of rewrite rules. \(R\) defines a rewrite relation \(\rightarrow_R\) as follows: \(u\rightarrow_R v\) if and only if there exists a rule \((l\rightarrow r)\in R\) and a substitution \(\sigma\) such that a subterm of \(u\) at path \(p\) is \(l\sigma\), and \(v\) is \(u\) whose subterm at path \(p\) is substituted with \(r\sigma\). We omit the subscript \(R\) when it’s clear from the context. Let \(\rightarrow^*\) be the transitive closure of binary relation \(\rightarrow\). We define \((\leftarrow_R)=(\rightarrow_R)^{-1}\), \((\leftrightarrow_R)=(\rightarrow_R)\cup (\leftarrow_R)\), and \((\approx_R)=\leftrightarrow_R^*\). \(\approx_R\) is an equivalence relation.

A normal form is a term that cannot be rewritten any further. We say \(n\) is a normal form of \(t\) if \(t\) reduces to \(n\) and \(n\) is a normal form. A TRS \(R\) is terminating if there is no infinite rewriting chain \(t_1\rightarrow_R t_2\rightarrow \ldots\). A TRS \(R\) is confluent if for all \(t, t_1, t_2\), \(t_1\leftarrow_R^*t\rightarrow_R^*t_2\) implies there exists a \(t'\) such that \(t_1\rightarrow_R^* t'\leftarrow_R^* t_2\). We call a confluent and terminating TRS convergent. Terms in a terminating TRS have at least one normal form, terms in a confluent TRS have at most one normal form, and terms in a convergent TRS have exactly one normal form.

We call a term rewriting system left-linear (resp. right-linear) if variables in the left-hand side (resp. right-hand side) pattern of each rewrite rule occur only once. For example, \(R_1=\{f(x,y)\rightarrow g(x)\}\) is left-linear, while \(R_2=\{f(x,x)\rightarrow g(x)\}\) is not left-linear. A TRS is linear if it’s left-linear and right-linear.

Finite tree automata

A finite tree automaton (FTA) is a tuple \(\mathcal{A}=(Q, F, Q_{\textit{final}}, \Delta)\), where \(Q\) is a set of states, \(F\) is a set of function symbols, \(Q_{\textit{final}}\subseteq Q\) is a set of final states, and \(\Delta\) is a set of transitions of the form \(f(q_1,\ldots, q_n)\rightarrow q\) where \(q,q_1,\ldots, q_n\in Q\). A term \(t\) is accepted by \(\mathcal{A}\) if it can be rewritten to a final state \(q_{\textit{final}}\in Q_{\textit{final}}\) of \(\mathcal{A}\), i.e., \(t\rightarrow^* q_{\textit{final}}\in Q_{\textit{final}}\). Since \(Q\) and \(F\) can be determined by \(\Delta\), we omit them and use \((Q_{\textit{final}},\Delta)\) to denote a FTA for brevity. Let \(\mathcal{L(A)}\) be the set of terms accepted by FTA \(\mathcal{A}\). A language \(L\) is called regular if it is accepted by some FTA (\(\exists \mathcal{A}, L=\mathcal{L(A)}\)). When the set of transitions \(\Delta\) is clear from the context, we further omit it and use \(\mathcal{L}(c)\) to denote the language accepted the tree automata \(\mathcal{A}=(c,\Delta)\).

Regular languages and FTAs are closed under union, intersection, and complementation. Moreover, it is possible to define a tree automaton that accepts any term: it is the complement of an empty FTA. To give an explicit construction, define \[\mathcal{A}_\ast=(\{q_\ast\}, \{f(\underbrace{q_\ast,\ldots,q_\ast}_{n} )\rightarrow q_\ast\mid n\text{-ary symbol } f\in F \})\] for a fresh state \(q_\ast\).

Left-regularity of a term rewriting system implies the regularity of its normal forms: Given a term-rewriting system \(R\), the set of normal forms of \(R\) is the complement of the set of rewritable terms, i.e., terms whose subterm match some left-hand side patterns of \(R\). Suppose \(R\) is left-linear. The set of rewritable terms is regular. Since regularity is preserved under complementation, the set of normal forms of \(R\) is regular. Below is an algorithm that computes a tree automata whose language is the set of normal forms. The construction here requires left linearity to ensure that each “hole” in the left-hand side patterns can pick terms independently. For example, the set of rewritable terms of rule \(f(x, x)\rightarrow x\) is not regular.

Procedure \(\textit{termsMatchingPattern}(p)\)

Input: A linear pattern \(p\).

Output: An FTA \(\mathcal{A}\) satisfying \(\mathcal{L(A)}\) contains all terms matching the given pattern.


  1. \(\quad\) case \(p\) of

  2. \(\quad\) \(\quad\) \(f(p_1,\ldots, p_k)\Rightarrow\)

  3. \(\quad\) \(\quad\) \(\quad\) \((q_i, \Delta_i) \gets \textit{termsMatchingPattern}(p_i)\) for \(i=1,\ldots, k\);

  4. \(\quad\) \(\quad\) \(\quad\) \(q\gets \textit{mkFreshState}()\);

  5. \(\quad\) \(\quad\) \(\quad\) \(\Delta \gets \{f(q_1,\ldots, q_k)\rightarrow q\}\cup \bigcup_{i=1,\ldots, k} \Delta_i\);

  6. \(\quad\) \(\quad\) \(\quad\) return \((q, \Delta)\);

  7. \(\quad\) \(\quad\) \(x\Rightarrow\) return \(A_\ast\);


Procedure \(\textit{subtermsMatchingPattern}(p)\)

Input: A linear pattern \(p\).

Output: An FTA \(\mathcal{A}\) satisfying \(\mathcal{L(A)}\) contains all terms containing the given pattern.


  1. \(\quad\) \(q_{\textit{final}}\gets \textit{mkFreshState}()\);

  2. \(\quad\) \((q_p, \Delta)\gets \textit{termsMatchingPattern}(p)\);

  3. \(\quad\) \(\Delta\gets \Delta\cup \{q_p\rightarrow q_{\textit{final}}\}\);

  4. \(\quad\) for each \(n\)-ary symbol \(f\) where \(n > 0\) do

  5. \(\quad\) \(\quad\) for \(i = 1,\ldots,n\) do

  6. \(\quad\) \(\quad\) \(\quad\) \(\Delta \gets \Delta \cup \{ f(\underbrace{q_\ast, \ldots, q_\ast}_{i-1}, q_{\textit{final}}, \underbrace{q_\ast, \ldots, q_\ast}_{n-i}) \rightarrow q_{\textit{final}} \}\);

  7. \(\quad\) return \((q_{\textit{final}}, \Delta)\);


Procedure \(\textit{normalForms}(R)\)

Input: A left-linear TRS \(R\).

Output: An FTA \(\mathcal{A}\) satisfying \(\mathcal{L(A)}\) is the set of normal forms of \(R\).


  1. return \(\overline{\bigcup_{\textit{lhs}\rightarrow \textit{rhs}\;\in\; R} \textit{subtermsMatchingPattern}(\textit{lhs})}\);


E-graphs and equality saturation

We call an FTA deterministic if for every term \(t\), \[t\rightarrow^*q_1\land t\rightarrow^*q_2\rightarrow q_1=q_2.\] We call an FTA reachable for every state \(q\) there exists a ground term \(t\) such that \(t\rightarrow^* q\). An e-graph \(G\) is a deterministic and reachable FTA \((\{q_\textit{final}\}, \Delta)\) with a single final state. Moreover, \(G\) induces a relation \(\approx_G\) defined as follows: for any terms \(t_1\) and \(t_2\), if there exists a state \(q\) in \(G\) such that \(t_1\rightarrow^* q\leftarrow^* t_2\), then \(t_1\approx t_2\)1. \(\approx_G\) is an equivalence relation: \(\approx_G\) is symmetric and reflexive by definition. Moreover, if \(t_1\rightarrow^* q \leftarrow^* t_2\) and \(t_2\rightarrow^* q'\leftarrow^* t_3\), since an E-graph is deterministic, \(t_1\rightarrow^* q=q'\leftarrow^* t_3\), so \(\approx_G\) is also transitive.

Equality saturation is defined as the inductive fixed point of rule applications and rebuilding: \[\textit{EqSat}(R, w) = (\textit{CC}\circ T_p)^{\infty}(\textit{mkEGraph}(w)).\]

We omit the definition of CC and \(T_p\) here, which can be found in the egg and egglog paper. But we remind the readers several properties of equality saturation here: First, \(T_p(G)\) is not necessarily deterministic, even when \(G\) is an E-graph (i.e., deterministic and reachable tree automaton). The congruence closure operator CC recovers the determinicity. Second, \[\{v \mid u\in \mathcal{L}(G), u=v\lor u\rightarrow_R v\}\subseteq \mathcal{L}(T_p(G))\subseteq \mathcal{L}((\textit{CC}\circ T_p)(G));\] that is, the e-graph after rule application at least represents terms derived from the initial e-graph by rewriting zero or one time, and the congruence closure further grows the E-graph. In many cases, the set containment is strict, see my earlier blog post on tree automata completion. As a corollary, \[\{t\mid w\rightarrow_R^* t\}\subseteq \textit{EqSat}(w).\] Finally, in terms of the equivalence relation derived from E-graphs, we have \[\left(\approx_{G}\right)\subseteq \left(\approx_{T_p(G)}\right)\subseteq \left(\approx_{(\texttt{CC}\circ T_p)(G)}\right).\]

Given a term rewriting system \(R\) where every rule preserves the set of variables (i.e., \(\textit{vars}(\textit{lhs})=\textit{vars}(\textit{rhs})\)), let \(R^{-1}\) be the term rewriting system obtained by swapping left-hand sides and right-hand sides of patterns in \(R\). We have \([w]_R= \mathcal{L}(\textit{EqSat}(R\cup R^{-1}, w))\)

Turing machines

A Turing machine \(\mathcal{M}=(Q,\Sigma, \Pi,\Delta,q_0,\beta)\) consists of a set of states \(Q\), the input and the tape alphabet \(\Sigma\) and \(\Pi\) (with \(\Sigma\subseteq \Pi\)), a set of transitions \(\Delta\), an initial state \(q_0\in Q\), and a special blanket symbol \(\beta\in\Pi\). Each transition in \(\Delta\) is a quintuple in \(Q\times \Pi\times \Pi\times \{L,R\} \times Q\). For example, transition \(q_iabRq_j\) means if the current state is \(q_i\) and the symbol being scanned is \(a\), then replace \(a\) with \(b\), move the head to the right, and transit to state \(q_j\). We assume the Turing machine is two-way infinite, so that the head can move in both directions indefinitely. Each configuration of \(\mathcal{M}\) can be represented as \(\rhd uq_i av \lhd\), where \(\rhd\),\(\lhd\) are left and right end markers, \(u\) is the string to the left of the read/write head, \(q_i\) is the current state, \(a\) is the symbol being scanned, and \(v\) is the string to the right. We say \(w_1\vdash_{\mathcal{M}} w_2\) if configuration \(w_1\) can transit to configuration \(w_2\) in a Turing machine \(\mathcal{M}\), and we omit \(\mathcal{M}\) when it’s clear from the context.

Termination of Equality Saturation

Theorem 1. The following problem is R.E.-complete:

Instance: a term rewriting system \(R\), a term \(t\).

Question: does EqSat terminate with \(R\) and \(t\)?

Proof. First, this problem is in R.E. since we can simply run EqSat with \(R\) and \(t\) to test whether it terminates. To show this problem is R.E.-hard, we reduce the termination problem of Turing machines to the termination of EqSat. We use the technique by [@NARENDRAN1985343]. In particular, for each Turing machine \(\mathcal{M}\), we produce a string rewriting system \(R\) such that the equivalence closure of \(R\), \((\approx_R)=\left(R\cup R^{-1}\right)^*\), satisfies that each equivalence class of \(\approx_R\) corresponds to a trace of the Turing machine. As a result, the following statements are equivalent:

In this proof, we consider a degenerate form of EqSat that works with string rewriting systems instead of term rewriting systems. Every string corresponds to a term, and every string rewrite rule corresponds to a rewrite rule. For example, the string \(uvw\) corresponds to a term \(u(v(w(\epsilon)))\), where \(u(\cdot), v(\cdot), w(\cdot)\) are unary functions and \(\epsilon\) is a special constant. A string rewrite rule \(uvw\rightarrow vuw\) corresponds to a (linear) term rewriting rule \(u(v(w(x)))\rightarrow v(u(w(x)))\) where \(x\) is a variable.

It is useful to define several sets of symbols for our construction. For each Turing machine \(\mathcal{M}\), we define \(\overline Q=\{\overline q\mid q\in Q\}\). We also define \(\overline \Sigma\), \(\overline \Pi\) in a similar way. In our encoding, we use \(\overline Q\) to denote states where the symbol being scanned is to the left of the state, and we use \(\overline \Sigma\) and \(\overline \Pi\) to denote alphabets that are to the left of the states. Moreover, we introduce two sets of “dummy” symbols \(L_z\) and \(R_z\) for \(z\) ranges over \(Q\times (\{\lhd\}\cup \Pi)\) and \((\{\rhd\}\cup \overline\Pi)\times \overline Q\). Let \(D_L\) and \(D_R\) be the set of all \(L_z\) and \(R_z\) respectively. We use these dummy symbols to make the string rewriting system that we will later define Church-Rosser.

The rewriting system we are going to define works over the set of strings \(\textit{CONFIG}=\rhd (\overline\Pi\cup D_L)^*(Q\cup \overline Q)(\Pi\cup D_R)^*\lhd\). Strings in CONFIG is in a many-to-one mapping to configurations of a Turing machine. We denote this mapping as \(\pi\): \(\pi(w)\) converts each \(\overline a\overline q_i\) to \(q_ia\), removes dummy symbols \(L_z\) and \(R_z\), and replace \(\overline a\) with \(a\). For example \(\pi(\rhd L_{q_0,a}\overline{b} L_{q_1,b} \overline{cq_3}dR_{q_i,\lhd}\lhd)=\rhd bq_3cd\lhd\)

Now, for each transitions in \(\mathcal{M}\), we define our string rewriting system \(R\) as follows.

transitions in \(\mathcal{M}\) rewrites in \(R\)
\(q_iabRq_j\) \(q_ia\rightarrow_R L_{q_ia}\overline bq_j\)
\(\overline a\overline q_i\rightarrow_R L_{\overline aq_i}\overline bq_j\)
\(q_i\beta bRq_j\) \(q_i\lhd\rightarrow_R L_{q_i\lhd}\overline b q_j\lhd\)
\(\rhd \overline q_i\rightarrow_R \rhd L_{\rhd\overline q_i}\overline bq_j\)
\(q_iabLq_j\) \(q_ia\rightarrow_R \overline q_j b R_{q_ia}\)
\(\overline a\overline q_i\rightarrow_R \overline q_j b R_{\overline a\overline q_i}\)
\(q_i\beta bLq_j\) \(q_i\lhd\rightarrow_R \overline q_jbR_{q_i\lhd}\lhd\)
\(\rhd \overline q_i\rightarrow_R \rhd\overline q_j b R_{\rhd\overline q_i}\)

Moreover, for each \(z\), we have the following two additional (sets of) auxiliary rewrite rules \[\begin{align*} q_iR_z&\rightarrow_R L_zL_zq_i\\ L_z\overline q_i&\rightarrow_R \overline q_iR_zR_z \end{align*}\] for any \(z\).

To explain what the two rules do, let us define two types of strings. Type-A strings are strings where the symbol being scanned is to the immediate right of \(q_i\) or to the immediate left of \(\overline {q_i}\). In other words, we call a string \(s\) a type-A string if \(s\) contains \(q_ia\) or \(\overline{aq_i}\). Type-B strings are strings that are not type-A: they are strings where there are dummy symbols in between the state and the symbol being scanned. The rewrite rules above convert any type-B strings into type-A in a finite number of steps.

Now, we observe that \(R\) has several properties:

  1. Reverse convergence: the critical pair lemma implies that if a rewriting system is terminating and all its critical pairs are convergent, it is convergent. Define \(R^{-1}\) to be a TRS derived from \(R\) by swapping left and right hand side. \(R^{-1}\) is terminating since rewrite rules in \(R^{-1}\) decreases the sizes of terms (that is, rewrite rules in \(R^{-1}\) increases the sizes of terms), and \(R^{-1}\) has no critical pairs. Therefore, \(R^{-1}\) is convergent.
  2. For each type-A string \(w\), then either
  3. For each type-B string \(w\), there exists a unique \(w'\) such that \(w\rightarrow_R w'\), and \(\pi(w)=\pi(w')\). Moreover, if \(w_0\rightarrow_R w_1\rightarrow_R\ldots\) is a sequence of type-B strings, the sequence must be bounded in length, since the state symbols \(q_i\) and \(\overline q_i\) move towards one end according to the auxillary rules above.
  4. By 2 and 3, \(w\rightarrow_R w_1\) and \(w\rightarrow_R w_2\) implies \(w_1=w_2\). In other words, \(\rightarrow_R\) is a function.

These observations allows us to prove the following lemma

Lemma 2. Let \(w_0=\rhd q_0s\lhd\) be an initial configuration. \(w_0\) is obviously in CONFIG. Moreover, given a Turing machine \(\mathcal{M}\), construct a string rewriting system \(R\) as above. \(\mathcal{M}\) halts on \(w_0\) if and only if \([w_0]_R\), the equivalence class of \(w_0\) in \(R\), is finite.

Proof. Consider \(S: w_0\rightarrow_R w_1\rightarrow_R \ldots\), a sequence of CONFIG starting with \(w_0\). By the above observations, \(S\) must have a subsequence of type-A strings \(w_0\rightarrow_R^* w_{a_1}\rightarrow_R^* w_{a_2}\rightarrow_R^*\ldots\) with \[\pi(w_0)=\ldots =\pi(w_{a_1-1})\vdash \pi(w_{a_1})=\ldots=\pi(w_{a_2-1})\vdash \pi(w_{a_2})= \ldots.\]

An overview of the trace \(w_0,w_{a_1},w_{a_2},\ldots\) and its properties is shown below:

Rw \({w_0}\) \(\rightarrow_R\) \(\underbrace{w_1\rightarrow_R\ldots \rightarrow_R w_{a_1-1}}_{\text{finite}}\) \(\rightarrow_R\) \({w_{a_1}}\) \(\underbrace{w_{a_1+1}\rightarrow_R\ldots \rightarrow_R w_{a_2-1}}_{\text{finite}}\) \(\rightarrow_R\) \({w_{a_2}}\) \(\ldots\)
Type A B \(\ldots\) B A B \(\ldots\) B A
Config \({\pi(w_0)}\) \(=\) \(\pi(w_1)=\ldots =\pi(w_{a_1-1})\) \(\vdash_{\mathcal{M}}\) \({\pi(w_{a_1})}\) \(\pi(w_{a_1+1})=\ldots =\pi(w_{a_2-1})\) \(\vdash_{\mathcal{M}}\) \({\pi(w_{a_2})}\) \(\ldots\)

Now we prove the claim:

We are ready to prove the undecidability of the termination problem of EqSat:

Given a Turing machine \(\mathcal{M}\). We construct the following two-tape Turing machine \(\mathcal{M}'\):

\(\mathcal{M}'\) alternates between the following two steps:

  1. Simulate one transition of \(\mathcal{M}\) on its first tape.

  2. Read the string on its second tape as a number, compute the next prime number, and write it to the second tape.

\(\mathcal{M}'\) halts when the simulation of \(\mathcal{M}\) reaches an accepting state.

It is known that a two-tape Turing machine can be simulated using a standard Turing machine, so we assume \(\mathcal{M}'\) is a standard Turing machine and takes input string \((s_1,s_2)\), where \(s_1\) is the input on its first tape and \(s_2\) is the input on its second tape. Let \(R'\) be the string rewriting system derived from \(\mathcal{M}'\) using the encoding we introduced in the lemma.

Given a string \(s\), let \(w\) be the initial configuration \(\rhd q_0(s, 2)\lhd\). The following conditions are equivalent:

  1. \(\mathcal{M}\) halts on input \(s\).
  2. \(\mathcal{M}'\) halts on input \((s, 2)\).
  3. \([w]_{R'}\) is finite.
  4. \([w]_{R'}\) is regular.

Note that (3) implies (4) trivially, and (4) implies (3) because if \([w]_{R'}\) is infinite, it must not be regular since the trace of \(\mathcal{M}'\) computes every prime number.

Now run EqSat with initial string \(w\) and rewriting system \(\leftrightarrow_{R'}\). EqSat terminates if and only if \(\mathcal{M}\) halts on \(s\):

Because the halting problem of a Turing machine is undecidable, the termination problem of EqSat is undecidable as well. \(\blacksquare\)

The regularity problem of term rewriting systems

In the last section, we have shown that the termination of equality saturation is undecidable. Here, we consider a more general question: Given a term rewriting system and a term, is the equivalence class of the term defined by the term rewriting system regular? In general, for term rewriting systems consisting of variable-preserving rewrite rules, if equality saturation terminates on \(R\cup R^{-1}\), the equivalence class is regular. However, there are cases where the equivalence class of a term is regular, equality saturation does not terminate: an example is term rewriting system \(\{f(x)\rightarrow f(g(x))\}\) and term \(f(a)\).

This regularity problem is undecidable, as we show here.

Theorem 3. The following problem is undecidable.

Instance: a term rewriting system \(R\), a term \(w\).

Problem: Is \([w]_R\) regular?


To show the undecidability, we reduce the halting problem of Turing machines to this problem. As shown in Theorem 1, given a Turing machine \(\mathcal{M}\), \(\mathcal{M}\) halts on an input \(s\) if and only if \([w]_{R'}\) (as constructed in Theorem 1) is regular for \(w=\rhd q_0(s, 2)\lhd\). \(\blacksquare\)

Different from the termination of equality saturation, it is open whether this problem is recursive enumerable. However, for a particular kind of rewrite systems, we show this regularity problem is R.E.-complete.

Theorem 4. The following problem is R.E.-complete.

Instance: a left-linear and convergent term rewriting system \(R\), a term \(w\).

Problem: Is \([w]_R\) regular?


As we show in Theorem 1, the regularity of \(R\) (and therefore \(R^{-1}\)) is undecidable. Because every string rewriting system is a linear term rewriting system \(R^{-1}\) is left-linear. Moreover, \(R^{-1}\) is convergent. Therefore, the regularity of left-linear, convergent term rewriting systems is undecidable. Additionally, we show the regularity problem is in R.E. by showing a semi-decision procedure for it.

Procedure \(\textit{equivClassOf}\)(\(R\), \(w\))

Input: a left-linear, convergent term rewriting system \(R\), a term \(w\).

Output: an E-graph that represents \([w]_R\) if exists.


  1. for each E-graph \(G\) such that \(w\in \mathcal{L}(G)\) do

  2. \(\quad\) if \(\textit{isFixPoint}(G, R\cup R^{-1})\) then

  3. \(\quad\) \(\quad\) if \(\mathcal{L}(G)\cap\textit{normalForms}(R)=\{w\}\) then

  4. \(\quad\) \(\quad\) \(\quad\) return \(G\);


In the above algorithm, isFixPoint checks if the E-graph \(G\) is “saturated” with respect to \(R\) and \(R^{-1}\). It does this by checking that, for each matched left-hand side \(l\sigma\) of the pattern, the right-hand side \(r\sigma\) exists in the E-graph and is equivalent to \(l\sigma\). Some care needs to be taken here: consider a rewrite rule \(f(x,y)\rightarrow g(x)\). The reverse form of this rule is \(g(x)\rightarrow f(x,y)\). The right-hand side pattern of this rewrite rule has a larger variable set than the left-hand side. To handle this rewrite rule, isFixPoint matches the left-hand side pattern \(g(x)\), and each match produces a substitution \(\{x\mapsto c_x\}\) at root E-class \(c\). isFixPoint checks regular set containment \(\{f(t_x, t_y)\mid t_x\in \mathcal{L}(c_x), t_b\in \ast\}\subseteq \mathcal{L}(c)\), where \(\ast\) is the universe of all terms.

We show the correctness of our algorithm in two steps.

Relaxing left-linearity

In the last theorem, the set of normal forms is intersected with the language represented by the e-graph, and checked for equivalence with the singleton set \(\{w\}\). This is why we require the term rewriting system to be left-linear: the set of normal forms of a left-linear term rewriting system is regular, and regular languages are closed under intersection and has decidable equivalence checking. Therefore, if there is a representation of normal forms for general term rewriting systems that is closed under intersection and has decidable equivalence checking, then we can lift the left linearity condition in the last theorem.

Indeed, such a representation exists. It is called tree automata with global constraints (TAGCs) [@TAGC]. Essentially, TAGCs extend tree automata with global equality and inequality constraints. TAGCs are closed under union and intersection, but not under complementation. Moreover, the emptiness and membership problem of TAGC is decidable. Therefore, if we can represent the set of normal forms of a term rewriting system as a TAGC, then line 3 of the algorithm used in the last theorem can be checked decidably for a general term rewriting system by considering its equivalent form: \[w\in L\land L\cap \overline{\{w\}}=\emptyset\] where \(L=\mathcal{L}(G)\cap\textit{normalForms}(R)\). I claim \(\textit{normalForms}(R)\) can be represented as an TAGC for an arbitrary term rewriting system, although I will omit the details of the construction here since it is a bit technical2. Putting everything together, we have the following generalization of Theorem 4:

Theorem 5. The following problem is R.E.-complete.

Instance: a convergent term rewriting system \(R\), a term \(w\).

Problem: Is \([w]_R\) regular?


  1. Note that this definition of \(\approx_G\) is different from the congruence relation defined in the Myhill-Nerode theorem for trees [@kozen1992myhill]. For example, consider an E-graph with transitions \(\{a()\rightarrow c_1, b()\rightarrow c_2, f(c_1)\rightarrow c_f, f(c_2)\rightarrow c_f\}\). In the Myhill-Nerode theorem, \(fa\) and \(fb\) would be equivalent while in our definition they are not because they are accepted by different states.↩︎

  2. In particular, we cannot use our strategy for left-linear term rewriting systems by first representing the set of rewritable terms and taking its complement, since TAGC is not closed under complementation. Luckily, for each rewrite rule, we can explicitly represent terms that does not match the left-hand side with inequality constraints, and then take the intersection of them.↩︎