Subtyping Constrained Types
Valery Trifonov? ?? and Scott Smith?
Department of Computer Science, Johns Hopkins University
Baltimore, MD 21218, USA
http://www.cs.jhu.edu/hog/

Abstract. A constrained type is a type that comes with a set of subtyping constraints on variables occurring in the type. Constrained type inference systems
are a natural generalization of Hindley/Milner type inference to languages with
subtyping. This paper develops several subtyping relations on polymorphic constrained types of a general form that allows recursive constraints and multiple
bounds on type variables. Subtyping constrained types has applications to signature matching and to constrained type simplification.

1

Introduction

A constrained type intuitively is a simple type together with a set of subtyping constraints on its type variables. An example is t → int \ {t ≤ int}, a type of functions
whose argument type t is constrained to be a subtype ofint. It is possible to perform letpolymorphic type inference for constrained types, producing polymorphic types (“typeschemes”) of the form ∀t1 , . . . , tn . τ \ C, which generalize the type schemes producedby the Hindley/Milner unification algorithm; constrained type inference is strictly moregeneral than unification-based type inference. The idea of including subtyping constraints as part of typing judgements was first developed by Mitchell [17, 18]. Hisconstraint sets were restricted to be atomic, allowing coercions between type variables
only; numerous other systems with restricted forms of constraint inference have been
introduced since, including [16, 12, 14, 4].
A type inference algorithm for polymorphic constrained types of the form studied
here was first discovered by Curtis [8], and later independently discovered in somewhat
different form, and first proven sound, by Aiken and Wimmers [1]. These constraint
systems are less restrictive than the previously cited formulations: they allow recursive
constraints such as t ≤ t →int, and thus subsume recursive types. Additionally, both
upper- and lower-bound constraints on variables are legal, and multiple bounds may beplaced on a single variable (multiple bounds such as {t ≤ τ1 , t ≤ τ2 } are expressed
equivalently as t ≤ τ1 ∩ τ2 , where ∩ is the type intersection operator of [1]). This extra
flexibility allowed in the constraint sets gives a more powerful, but computationallymore complex, inference algorithm.
Constrained types are particularly appropriate for object-oriented programming languages [10, 9]: these types incorporate subtyping and recursive dependence, both critical?
??

Partially supported by NSF grant CCR-9301340
Partially supported by AFOSR grant F49620-93-1-0169

in an object-oriented setting, and their greater flexibility gives a reasonable solution to
the binary methods problem [5].
The objective of this paper is to address the problem of subtyping between polymorphic constrained types:∀t1 , . . . , tn .τ \ C ≤∀ ∀t1 , . . . , tn .τ 0 \ C 0
Considering the generality of these types, the relation ≤∀ should be expected to subsume
both the “more general than” relation between type schemes in the Hindley/Milner
system, and the subtyping relation on recursive types of Amadio/Cardelli [3].
This relation, which has not received the deserved attention in the literature, hasat least two important applications. The first is separate compilation via modules andfunctors, and the subsequent need for signature matching. To a first approximation, in amodule system based on Hindley/Milner style of type inference the program specifies
a polymorphic type (signature) S for the parameter of a functor F to allow uses ofthe parameter at different types (which is not possible for parameters of functions);
the polymorphic signature S 0 inferred for the actual argument M is then matchedagainst S at the point of application F (M ). In a constrained type setting S and S 0 arepolymorphic constrained types; “matching the signature” thus requires verifying that
the S 0 ≤∀ S. Another application is the justification of simplification operations on
constrained polymorphic types. In this case a proof is required that the simplified typeis equivalent to the original (i.e. both a subtype and a supertype).
In this paper we define several variants of ≤∀ . The “optimal” form ≤∀obs may becharacterized observationally by analogy with Morris/Plotkin contextual expression
equivalence, replacing expression contexts with proof contexts. We then define a semantic form ≤∀sem based on a regular tree interpretation, and prove it is a good modelby showing it is exactly ≤∀obs , a full type abstraction property. The ideal model [15]may also be used as a basis of a constrained type ordering ≤∀ideal [2], but it is not fully
abstract. The relation ≤∀sem is surprisingly complex: we leave open the question whether
it is decidable, and develop a powerful decidable approximation ≤∀dec .
In the process of defining these subtyping relations, other results of independentinterest are derived. First, an entailment relation C ` τ ≤ τ 0 over simple types τ
is axiomatized. The set C is a system of arbitrary type constraints, thus generalizing
the system of [3] which only allows one upper, non-recursive bound of each variable
in C. We define two reduced forms of constraint sets, constraint maps (kernels) andcanonical maps, which are also of use as more compact representations of constraint sets
in algorithms such as type inference. Next, the subtyping relations on constrained types
are defined, and their relationship is established. Finally, soundness of a system of typing
rules with constrained types is proved by a simple method of reduction to soundness ofa system without constrained types. A principal typing property is established for ourtype inference algorithm.
In this paper we work over a simple language with only function, top, and bottom
types to reduce clutter. However, previous work [10, 9] shows how state, records,
variants, classes, and objects all may be incorporated in a constrained type framework,
and we explicitly avoid semantic tools (such as the ideal model of types [15]) whichlack a strong potential to generalize to such constructs.

2 Simple and Constrained Types
We illustrate our ideas by studying an extension of the call-by-name λ-calculus withconstants and let-binding. The abstract syntax of the expressions in the language ise ::= x | λx. e | e e0 | X | let X = e in e0
To simplify the presentation we assume that the λ-bound and let-bound variables are indifferent syntactic classes, that λ-bound variables are not re-bound, and that constants
are a special case of let-variables, bound in the initial environment. We write λ_. e forλx. e where x is not free in e, and (e; e0 ) for (λ_. e0 ) e.
The simple types are
Typ 3 τ ::= t | ⊥ | > | τ →τ 0 | . . .
where t ranges over the set TyVar of type variables, ⊥ and > are “minimal” and“maximal” types. In addition to the function types there may be a set of basic types
which we leave unspecified. We call ⊥, >, →, etc. type constructors, and all simple
types in Typ − TyVar constructed.
A constrained type κ has the form ∀t. A ⇒ τ \ C, where the context A is a finite
map from variables to simple types, written hxi : τi i, the root type τ is a simple type,
and the constraint set C is a set of subtyping constraints on simple types, each of
the form τ 0 ≤ τ 00 .2 We use this new notion for constrained types in order to more
appropriately present the binding structure of type variables. The context A represents
the assumptions about the types of λ-bound variables free in the term, and C is theset of subtyping constraints (a.k.a. coercions) under which the term is typable; they areboth part of the type itself instead of the environment. Thus all constrained types in a
type sequent are closed, so we can compare constrained types with different sets of typevariables, and avoid giving meaning to constrained types with constrained but free typevariables.
Definition 1. A constraint set C is closed if it is closed under transitivity (i.e. {τ ≤
τ 0 , τ 0 ≤ τ 00 } ⊆ C entails τ ≤ τ 00 ∈ C) and decomposition (τ1 → τ10 ≤ τ2 → τ20 ∈ Centails {τ2 ≤ τ1 , τ10 ≤ τ20 } ⊆ C).
We denote by Cl(C) the least closed superset of C. Thus, for example, if C = {t →t ≤ (⊥ → t) → > → >}, then Cl(C) = C ∪ {⊥ → t ≤ t, t ≤ > → >, ⊥ → t ≤ > →>, > ≤ ⊥, t ≤ >, ⊥→t ≤ >}.
A constraint set is consistent if for each constraint τ ≤ τ 0 in it at least one of thefollowing is true: τ = ⊥, τ 0 = >, both sides have the same outermost type constructor,
or one of them is a type variable.
2

We only consider closed constrained types, for which {t} ⊇ FTV(A) ∪ FTV(τ ) ∪ FTV(C),
where FTV(τ ) as usual denotes the set of type variables free in τ . Constrained types are
considered identical under α-renaming of bound type variables.

3

Primitive Subtyping

In order to define notions of ≤∀ , a theory of primitive subtyping under a set of subtyping
constraints, C |= τ ≤ τ 0 , and its decidable axiomatization are developed. Due to space
limitations most of the proofs have been elided; currently they can be found on theWorld Wide Web at URL http://www.cs.jhu.edu/hog/subcon.ps.gz.
3.1

Regular Tree Semantics of Constraints

Sequents C |= τ ≤ τ 0 may be defined as valid if they hold for all instantiations of typevariables in C, τ , and τ 0 . There are many possibilities for the notion of “instance.” Thesimplest is to allow instances to range over the variable-free types constructed from >,⊥, and →. However, for our purposes this does not give enough points in the space ofinstances, e.g. when typing binary methods [5] we have to work with recursive constraint
sets such as {t → t ≤ t, t ≤ t → >}, which have no solutions in this space. This is an
example where differences arise when recursive constraint sets are allowed—if recursive
constraint sets were not allowed, the simple type basis would have been appropriate.
Another candidate is the ideal model [15] used in [2], but conversely it has too many
points, allowing polymorphic types such as ∀t.t→t to be substituted for type variables;
since our system is “shallow” these points are superfluous in our framework. It turns outthat the addition of the solutions of recursive type equations to the ground types gives
just enough points to define an appropriate semantics. In the next section a theorem
will be proven which rigorously demonstrates this fact. We use the convenient notionof regular trees [7, 3] to model solutions of recursive type equations.
We present the semantics of constraint sets in terms of regular trees over a ranked
alphabet. Let us review some definitions and results from [7, 3]. Given a ranked alphabet
L, a tree ϕ is a partial function from finite sequences of natural numbers N∗ (paths) to L
such that Dom(ϕ) is prefix-closed and for each π ∈ N∗ we have {k | πk ∈ Dom(ϕ)} ={0, . . . rankL (ϕ(π)) − 1}. The subtree at π ∈ Dom(ϕ) is the function λπ 0 .ϕ(ππ 0 ); |π|is the level of that subtree. A tree is regular if the set of its subtrees is finite.
Define T as the set of regular trees over the ranked alphabet Lτ of type constructors
in our language, the nullary ⊥ and > and the binary →; we reuse the syntax of types
as a notation for trees. A regular system in this context is a set of equations of theform ti = τi between type variables ti ∈ TyVar and simple types τi (i.e. finite trees
over Lτ ∪ TyVar where the type variables are nullary); a regular system is contractive
if it has no subset of the form {t0 = t1 , . . . , tn−1 = tn , tn = t0 }. An assignment ρon V ⊂ TyVar is a total map in V → T; it is homomorphically extended on simple
types τ with FTV(τ ) ⊆ V : ρ(⊥) = ⊥, ρ(>) = >, and ρ(τ →τ 0 ) = ρ(τ )→ρ(τ 0 ). An
assignment ρ on ti is a solution of the regular system ti = τi if ρti = ρτi .
Proposition 2. Each contractive regular system has a unique solution, and each regulartree is the image of some variable in a solution of a contractive regular system.
A level-k cut ϕ|k of ϕ ∈ T for k ∈ N is defined as the (finite) tree obtained byreplacing all subtrees at level k of ϕ (if any) by >.

A partial order over L, in which ⊥ is the minimal element and > is the maximal,
together with variance specifications for the arguments of non-nullary constructors (inthis case, contravariance in the domain and covariance in the range of →) induce apartial order ≤tree over T as follows: ⊥ ≤tree ϕ and ϕ ≤tree > for each finite tree ϕ,and ϕ1 → ϕ01 ≤tree ϕ2 → ϕ02 if ϕ2 ≤tree ϕ1 and ϕ01 ≤tree ϕ02 ; finally, ϕ ≤tree ϕ0 if
ϕ|k ≤tree ϕ0 |k for each k ∈ N.
Returning to our type system, within a set of constraints we model simple types byregular trees satisfying these constraints:
Definition 3. (i) An assignment ρ k-satisfies a constraint τ ≤ τ 0 , written ρ .k τ ≤ τ 0 ,if ρ(τ )|k ≤tree ρ(τ 0 )|k .
(ii) ρ satisfies τ ≤ τ 0 (ρ . τ ≤ τ 0 , also ρ is a solution of τ ≤ τ 0 ) if ρ .k τ ≤ τ 0 for eachk ∈ N.
(iii) The above properties are extended over a set of constraints C if they hold for eachconstraint in the set.
Regular trees may now be used to define the theory C |= τ ≤ τ 0 .
Definition 4. (i) C |= τ ≤ τ 0 if for every assignment ρ on FTV(C ∪ {τ ≤ τ 0 }), ifρ . C, then ρ . τ ≤ τ 0 .
(ii) C |= C 0 if FTV(C 0 ) ⊆ FTV(C) and for each solution ρ of C there is a solution ρ0
of C 0 which agrees with ρ on FTV(C 0 ).
(iii) C and C 0 are equivalent if C |= C 0 and C 0 |= C.
Lemma 5. C and Cl(C) are equivalent. Thus, if C is satisfiable, then Cl(C) is consistent.
We leave open the decidability of C |= τ ≤ τ 0 , and in the sequel we develop
decidable approximations to it.
3.2

Constraint Map Representation

We now define constraint maps as an equivalent form for representing consistent closed
constraint sets. Closed constraint sets may contain significant redundant information.
To a constraint set {τ1 → τ10 ≤ τ2 → τ20 } the closure adds {τ2 ≤ τ1 , τ10 ≤ τ20 }, and theoriginal constraint between functions is completely captured by the additions and can beremoved. Constraint maps in fact do not allow constraints between two constructed types,
since they can always be expressed by an equivalent family of constraints, provided theconstraint set was consistent to begin with. We reuse some of the notation previously
defined for constraint sets C on constraint maps K.
Definition 6. A constraint map is a finite map K ∈ TyVar → (2Typ )2 , assigning sets ofupper and lower bounds to each type variable in its domain; we use the more intuitive
notation t ≤ τ ∈ K and t ≥ τ 0 ∈ K for τ ∈ π1 (K(t)) and τ 0 ∈ π2 (K(t)), respectively,and (K, t ≤ τ ), (K, t ≥ τ ) for the maps extending the sets π1 (K(t)), respectively
π2 (K(t)), to contain τ . We write t ≥ τ instead of τ ≤ t since K is not required to beantisymmetric, i.e. t0 ∈ π1 (K(t)) does not imply t ∈ π2 (K(t0 )); we use t = τ ∈ K forthe pair t ≤ τ ∈ K, t ≥ τ ∈ K.

(⊥) K ` ⊥ ≤ τ(=) K ` t ≤ t,t ∈ Dom(K)(K, t ≤ τ, t ≤ τ 0 ) ` τ ≤ τ 0(K, t ≤ τ ) ` t ≤ τ 0
if (K, t ≤ τ, t ≤ τ 0 ) is contractive(↑)(>) K ` τ ≤ >(→)

K ` τ10 ≤ τ1
K ` τ2 ≤ τ20
K ` τ1 →τ2 ≤ τ10 →τ20

(K, t ≥ τ, t ≥ τ 0 ) ` τ 0 ≤ τ(K, t ≥ τ ) ` τ 0 ≤ t
if (K, t ≥ τ, t ≥ τ 0 ) is contractive(↓)

Fig. 1. Rules for primitive subtyping

Definition 7. The kernel Ker(C) of a constraint set C is the constraint map defined by
the set of constraints {τ ≤ τ 0 ∈ Cl(C) | {τ, τ 0 } ∩ TyVar 6= ∅}; a constraint of the formt ≤ t0 sets the appropriate bounds on both variables.
For example, since the closure of the constraint set C = {(>→t)→t ≤ t→t→⊥}is Cl(C) = {(> → t) → t ≤ t → t → ⊥, t ≤ > → t, t ≤ t → ⊥}, the kernel of C is
Ker(C) = (t ≤ >→t, t ≤ t→⊥).
Proposition 8. For consistent constraint set C, Ker(C) and C are equivalent.
The kernel form of a constraint set has significant advantages from an implementation
perspective: a type inference algorithm may maintain constraint sets in their equivalent
kernel form, which is considerably more compact than the closure.
3.3 Rules for Primitive Subtyping
We take advantage of the equivalent constraint map representation K of a constraint set
C, and with the rules in Fig. 1 define a decidable sound approximation K ` τ ≤ τ 0 of
the theory C |= τ ≤ τ 0 .
An implicit requirement for K ` τ ≤ τ 0 is FTV(τ ) ∪ FTV(τ 0 ) ⊆ Dom(K). As isusually the case in the presence of recursive types, a notion of contractiveness plays animportant role in detecting ill-defined constraint maps.
Definition 9. A constraint map K is contractive if π1 ◦K and π2 ◦K as relations on TyVar
have no cycles, i.e. if there do not exist variables {t1 , . . . , tn } ⊆ Dom(K) such thattn = t1 and ti ≤ ti+1 ∈ K (respectively ti ≥ ti+1 ∈ K) for each i ∈ {1, . . . , n − 1}.
For instance, neither (t ≤ t) nor (t ≥ t0 , t0 ≥ t) is contractive, while (t ≥ t0 , t ≤ t0 )is (recall a constraint map is not necessarily symmetric on type variables). However notethat contractiveness of constraint maps, as opposed to regular systems, does not entail
satisfiability, e.g. (t ≤ ⊥, t ≥ >) is a contractive map with no solutions. Contractivenessis required in order to ensure soundness by disallowing proofs in which e.g. a constraint
introduced to K by one rule (↑) is used in another (↑) with no intervening uses of (→).
Rules (⊥), (>), (→), and (=) for reflexivity of the relation on type variables are
standard. The novel rules (↑) and (↓) provide the only access to constraints in K; infact, were the constraint map in its premise identical to the one in its conclusion, rule(↑) would have been just the standard rule for proving an upper bound on a type variablein a system of rules with eliminated transitivity. With the extra assumption these are
induction rules, similar to the (FIX) rule of [11].
Some standard subtyping rules [3] are derivable from those given in Fig. 1 and thus
omited, for instance general reflexivity (K ` τ ≤ τ is always provable for FTV(τ ) ⊆Dom(K), and (K, t ≤ τ ) ` t ≤ τ is provable by (↑) and general reflexivity. In contrast,
transitivity only holds for consistent constraint maps.
Definition 10. A constraint map K is consistent if for each t, τ , and τ 0 , if t ≥ τ ∈ K
and t ≤ τ 0 ∈ K, then K ` τ ≤ τ 0 .
The following lemma shows that a kernel contains all of the information of aconsistent set C, and that computing the closure of C is equivalent to the constructionof a constraint map K such that K ` C.
Lemma 11. If Cl(C) is a consistent constraint set, then Ker(C) is a consistent constraint
map, and Ker(C) ` Cl(C).
3.4Soundness and Decidability of Primitive Subtyping

Next we establish soundness of the proof system ` with respect to the relation |=; themain idea is to show that all assignments which approximate solutions of a constraint
map K also approximate solutions of all subtyping constraints provable from K.
Lemma 12. If K is contractive, K ` τ ≤ τ 0 has a proof, and the assignment ρ
k-satisfies K, then(i) if the proof of K ` τ ≤ τ 0 has an instance of a rule other than (↑) or (↓) at its
root, then ρ .k+1 τ ≤ τ 0 ;(ii) if there is an instance of (↑) or (↓) at the root of the proof of K ` τ ≤ τ 0 , thenρ .k τ ≤ τ 0 .
Theorem 13 (Soundness). If K is contractive and K ` τ ≤ τ 0 , then K |= τ ≤ τ 0 .
The system ` is incomplete with respect to the relation |=; it is not even possible toprove that (t ≤ t→⊥, t ≤ >→t) ` t ≤ >→⊥ since the bound we need is stronger
than each of the two given. However ` is useful because of the following property.
Lemma 14. The relation K ` τ ≤ τ 0 is decidable.
3.5

Canonical Constraint Maps

We can obtain a stronger proof system if we place the constraints in an equivalentcanonical form that has pre-computed least upper and greatest lower bounds. In acanonical constraint map each variable has exactly one constructed upper and one
constructed lower bound. For instance, a canonical equivalent of (t ≤ t→⊥, t ≤ >→t)is K = (t ≥ ⊥, t ≤ > → ⊥). The upper bounds t → ⊥ and > → t on t have the lub> → ⊥ computed for them. For this set we can indeed prove K ` t ≤ > → ⊥. The
canonicalization process also has potential as an implementation technique.

Definition 15. A constraint map K is canonical if– K assigns exactly one upper and one lower constructed bound (canonical bounds)
to each type variable in its domain (with no restriction on the number of variablebounds);– if t ≤ t0 ∈ K and t0 ≤ t00 ∈ K, then t ≤ t00 ∈ K, and– for each (t ≤ t0 , t ≤ τ, t0 ≤ τ 0 ) ⊆ K, where {τ, τ 0 } ∩ TyVar = ∅, we haveK ` τ ≤ τ 0 , and similarly for the lower bounds.
Clearly if K and K 0 are equivalent on FTV(τ ) ∪ FTV(τ 0 ) then K |= τ ≤ τ 0 if andonly if K 0 |= τ ≤ τ 0 . This allows us to upgrade our system by converting each map K
to an equivalent canonical map Can(K) and then proving Can(K) ` τ ≤ τ 0 instead of
the original K ` τ ≤ τ 0 . Here we provide an algorithm for computing an equivalentcanonical form Can(K) of a map K.
Algorithm 16 Can(K) is computed as follows.
Start with K 0 = K, and for some t ∈ FTV(K 0 ) let V be the least set satisfying
V = {t} ∪ {t0 | ∃t00 ∈ V. t00 ≤ t0 ∈ K 0 }, i.e. the set of upper bounds on t in thereflexive transitive closure of K 0 on TyVar; the case of lower bounds is similar. Let
B = {τ ∈ Typ − TyVar | ∃t0 ∈ V. t0 ≤ τ ∈ K 0 }, the set of constructed upper bounds
on t. We compute the canonical upper bound τ of t as the greatest lower bound of theelements of B, as follows.
If B ⊆ {>}, then τ = >; if ⊥ ∈ B, then τ = ⊥. Otherwise let {τi →τi0 }∨be the set of all function types in B, and let τ = t∧
T → tT 0 , where T = {τi },∨T 0 = {τi0 }, and t∧
T and tT 0 are in general auxiliary type variables we associatewith the respective sets of type terms; in the cases when T is a singleton set∨0∧{t0 } we let t∧
T = tT = t to ensure termination. Add the bounds (tT ≥ τj ) (and∨0similarly for tT 0 ) to K .
Replace the constructed upper bounds of t by (t ≤ τ ); thus the new bounds on t, namely(t ≤ τ ) and (t ≤ t0 ) for each t0 ∈ V , are in canonical form. Continue until all variables∧∨∨
in K 0 are processed. (Adding also all bounds of the form (t∧S ≤ tT ) and (tT 0 ≥ tT ) if
T ⊂ S, when those auxiliary variables appear in the domain of the map, produces aneven stronger, with respect to `, yet equivalent form.) The resulting K 0 is the value ofCan(K).
The following lemma proves the correctness of this algorithm.
Lemma 17. For each constraint map K there exists a canonical equivalent Can(K).
For example computing the canonical equivalent of K = (t ≤ t → ⊥, t ≤ > → t)∧
0starts by introducing t1 = t∨{t, >} and t2 = t{⊥, t} , and transforming K into K = (t ≥⊥, t ≤ t1 →t2 , t1 ≥ >, t1 ≤ >, t1 ≥ t, t2 ≥ ⊥, t2 ≤ ⊥, t2 ≤ t). This map is alreadyin canonical form, and it is possible to prove K 0 ` t ≤ > → ⊥. However, even when
` is used on to canonical equivalents it still does not provide proofs for some valid
relations; for instance, (t ≥ ⊥, t ≤ t→>) |= t ≤ (>→⊥)→>.

In the general case, the algorithm implied by Lemma 14 may attempt comparing
τ against each upper bound on t currently in K in the process of searching for a
proof of K ` t ≤ τ . In this process, it may have to backtrack if it fails to find a
proof using a particular bound. However, in the case of canonical maps a more efficient
implementation is possible which has time complexity of O(n2 ), where n is the sizeof K ∪ {τ ≤ τ 0 }. This algorithm only compares new bounds on a variable against its
canonical bounds (which can be shown to suffice) and uses a form of memoisation to
detect looping; details are omited for lack of space.
A parallel can be drawn between our system of subtyping rules and the system`AC of Amadio and Cardelli [3], which is based on a relation of equivalence between
recursive types, and on the inductive rule(µ)

C, t ≤ t0 `AC τ ≤ τ 0
C `AC µt. τ ≤ µt0 . τ 0

Since recursive types can be encoded as type variables with identical upper and lower
bounds, the corresponding rule for simple types with constraints is
K, t = τ, t0 = τ 0 , t ≤ t0 ` τ ≤ τ 0
K, t = τ, t0 = τ 0 ` t ≤ t0
which is indeed derivable in ` in a stronger version by successive applications of (↑)
and (↓); furthermore, the steps of the proof of K, t = τ, t0 = τ 0 ` t ≤ t0 follow
closely the steps of the algorithm for computing C `AC µt. τ ≤ µt0 . τ 0 presented in[3], which also effectively constructs the type contexts necessary in order to establish
type equivalences. Amadio and Cardelli show that their system is complete with respect
to the regular tree model of recursive types under certain conditions on C, τ , and τ 0 ;in particular the constraints in C may not be recursive, and no variable may occur inboth τ and τ 0 . An attempt to directly apply the system to prove sequents violating these
conditions shows that it is incomplete in the more general setting considered in thispaper, e.g. t ≤ > → t 6`AC t ≤ µt0 . > → t0 . Our system, while still incomplete withrespect to the model we present, is capable of proving the corresponding forms of all
sequents provable in [3], in addition allowing multiple recursive upper and lower bounds
on type variables, e.g. t ≤ >→t, t0 = >→t0 ` t ≤ t0 .
3.6

Satisfiability of Canonical Constraint Maps

A constrained type only has meaning if its constraints describe a non-empty set of
instances, and hence the satisfiability of a constraint map is an important property. In
this section we provide a connection between consistency and satisfiability of canonical constraint maps. This connection also plays a role in establishing the relationshipbetween various notions of subtyping on constrained types in Sect. 4.
Definition 18. The canonical map K 0 is a submap of a canonical map K if K 0 ⊆ K.
Note that constraints on variables in Dom(K) − Dom(K 0 ) may involve variables inDom(K 0 ), but FTV(Codom(K 0 )) ⊆ Dom(K 0 ).

Lemma 19. If K 0 is a submap of K, and K is consistent and canonical, then every
solution of K 0 can be extended to a solution of K. Thus, considering the special case ofK 0 = ∅, every consistent canonical constraint map K is satisfiable.
Combining these results with canonicalization and soundness of ` with respect to|=, we can reason about canonical maps instead of their equivalent constraint sets.
Definition 20. C ` τ ≤ τ 0 if Can(Ker(C)) ` τ ≤ τ 0 .

4Subtyping Constrained Types

In this section we define three concrete ≤∀ relations of subtyping on constrained types:≤∀obs , ≤∀sem , and ≤∀dec , as promised in the introduction.

4.1

Operational Subtyping

For an initial definition of ≤∀ we rely on operational notions as a basis. The basic idea
is simple, but we could not find any precedent for it in the literature. Expressions ofconstrained type ∀t. A ⇒ τ \ C are also of type ∀t0 . A0 ⇒ τ 0 \ C 0 if for all possible
uses of expressions of the latter type that are consistent, use of the former type is also
consistent. Relation ≤∀obs is defined by this means. The difficult issue is how a “use”
of a type should be defined. Informally, each use is a typing proof context, in analogy
with Morris/Plotkin expression contexts. We give a particular version of typing proof
context which is one of many reasonable and equivalent notions: a “use” is a set of
constraints of the form that could be added by the inference rules. The constraints addedby the inference rules may only introduce upper bounds on the root types, and dually
only lower bounds on the types in the context. As a consequence one may obtain a valid
typing derivation after replacing a subterm by another term whose constrained typeyields a consistent system when those bounds are added. This leads us to the following
observational definition of a subtyping relation on constrained types. (We let A ≤ A0
abbreviate the set of constraints {A(x) ≤ A0 (x) | x ∈ Dom(A0 )}, defined only whenDom(A) ⊇ Dom(A0 ).3 )Definition 21. For closed constrained types, ∀t0 . A0 ⇒ τ 0 \ C 0 ≤∀obs ∀t00 . A00 ⇒ τ 00 \ C 00
if for each ∀t. A ⇒ τ \ C such that {t} is disjoint from {t0 } and {t00 }, if Cl(C∪C 00 ∪(A ≤A00 ) ∪ {τ 00 ≤ τ }) is consistent, then Cl(C ∪ C 0 ∪ (A ≤ A0 ) ∪ {τ 0 ≤ τ }) is consistent.
3This “subtyping rule” for contexts is similar to standard record subtyping [6]; the closure conversion [[x]] = λE. E.x, [[λx. e]] = λE. λx. [[e]]{ xi = E.xi |xi ∈FV(e)−{x} , x = x },and [[e e0 ]] = λE. [[e]] E ([[e0 ]] E) makes the environment explicit and maps terms of type∀t. hxi : τi i ⇒ τ \ C to closed terms of type ∀t. hi ⇒ { xi : τi }→τ \ C.

4.2

Semantic Subtyping

Next, a semantic notion ≤∀sem is defined, via the regular tree model: two polymorphic
constrained types are ordered if their sets of regular tree instances are ordered.
The context component A of a constrained type corresponds to a finite map Φ fromvariables to regular trees; the relation ≤tree can be extended on such maps as follows:Φ ≤tree Φ0 if Dom(Φ) ⊇ Dom(Φ0 ) and Φ(x) ≤tree Φ0 (x) for each x ∈ Dom(Φ0 ). An
instance of the constrained type κ = ∀t. A ⇒ τ \ C is a pair written Φ ⇒ ϕ whereΦ = ρ ◦ A and ϕ = ρτ for some assignment ρ on {t} that satisfies C. The set of
instances of κ is Inst(κ). As in the definition of ≤∀obs , the natural order on instances isΦ ⇒ ϕ ≤tree Φ0 ⇒ ϕ0 if Φ0 ≤tree Φ and ϕ ≤tree ϕ0 . We can now define a semantical
notion of subtyping on constrained types.
Definition 22. κ0 ≤∀sem κ00 if for each instance of κ00 there is a smaller instance of κ0 .
We may now prove the equivalence of ≤∀sem and ≤∀obs , demonstrating the appropriateness of the regular tree interpretation.
Theorem 23 (Full Type Abstraction). The relations ≤∀sem and ≤∀obs agree.
To contrast ≤∀sem with the ideal model ordering ≤∀ideal , consider the following
example, in which we omit contexts and quantifiers when empty. In the regular tree model
the only solution of C = {>→⊥ ≤ t, t ≤ >→>, t ≤ ⊥→⊥} is ρ = [t 7→ >→⊥],
which satisfies also t ≤ > → ⊥; hence (> → ⊥) → > → ⊥ \ ∅ ≤∀sem ∀t. t → t \ C. But
this fails for ≤∀ideal since in the ideal model e.g. [ t 7→ ∀t0 . t0 → t0 ] is a solution of C
which does not satisfy t ≤ >→⊥. As a consequence the ideal model ordering does notoffer full type abstraction with respect to the operational subtyping ≤∀obs .
4.3

Decidable Subtyping

The question of decidability of ≤∀sem is open; we show how it may be approximated bya powerful decidable relation. The material of the previous section is used to define thisdecidable relation.
The informal idea leading to the decidable relation is simple: observe that adding
constraints to a set may only shrink the set of its solutions. For constrained types κ0 =∀t0 . A0 ⇒ τ 0 \ C 0 and κ00 = ∀t00 . A00 ⇒ τ 00 \ C 00 , Definition 22 states that κ0 ≤∀sem κ00 if
a certain relation holds for each instance of κ00 (that is, for the unrestricted set of solutionsof C 00 ) and some corresponding instance of κ0 (that is, an element of a possibly restrictedsubset of solutions of C 0 ). Thus, assumming that {t0 } and {t00 } are disjoint, we would
have a proof of κ0 ≤ κ00 if we could show that the relations τ 0 ≤ τ 00 and (A00 ≤ A0 )hold under C 00 and C 0 together with some set C of constraints which do not “constrain
further” the type variables t00 (but possibly add constraints on t0 ).
Applying the machinery developed in Sect. 3, these ideas are formalized in thefollowing definition of a relation approximating ≤∀sem .
Definition 24. κ0 ≤∀dec κ00 if κ0 = ∀t0 . A0 ⇒ τ 0 \ C 0 and κ00 = ∀t00 . A00 ⇒ τ 00 \ C 00
for some {t0 } ∩ {t00 } = ∅, and there exists a consistent canonical map K such that
K ` C 0 ∪ (A00 ≤ A0 ) ∪ {τ 0 ≤ τ 00 } and Can(Ker(C 00 )) is a submap of K.

Here the map K represents the union of C 00 , C 0 , and C of our informal discussion:it has Can(Ker(C 00 )) as a submap (meaning {t0 } are not further constrained), it entails
C 0 , and its constraints on {t0 } may be stronger that those in C 0 in order to ensure thatthe relations between root types and contexts hold. The following theorem shows that≤∀dec is indeed an approximation to ≤∀sem .
Theorem 25. If κ0 ≤∀dec κ00 , then κ0 ≤∀sem κ00 .
Although the incompleteness of ` with respect to |= implies incompleteness of ≤∀decwith respect to ≤∀sem , the relation ≤∀dec is still quite powerful: it subsumes the relation of
instantiation between type schemes in the Hindley/Milner system, the subtyping relation
between recursive types in the Amadio/Cardelli system, and their union on recursive
polymorphic types in shallow (prenex) form. Consider Hindley/Milner subtyping in
more detail. The type scheme ∀t00 . τ 00 is an instance of ∀t0 . τ 0 if τ 00 = στ 0 , where σ
is a simple type substitution on {t0 } = FTV(τ 0 ), and {t00 } = FTV(τ 00 ); then we have∀t0 . hi ⇒ τ 0 \ ∅ ≤∀dec ∀t00 . hi ⇒ τ 00 \ ∅ by Definition 24, as evidenced by the canonicalconstraint map K = (t0 ≤ σ(t0 ), t0 ≥ σ(t0 )), which entails τ 0 ≤ σ(τ 0 ) and is obviously
consistent. Closed recursive types can also be represented as constrained types, withthe constraint set effectively encoding a regular system; when restricted to these types,≤∀dec is equivalent to the system of Amadio and Cardelli.
Furthermore, ≤∀dec is sufficiently strong to allow proving correctness of many usefulsimplifications of types inferred by the system. For example, ≤∀dec can be used to show
the soundness of the constraint set simplification “garbage collection” of [9], whichallows the removal of “unreachable” constraints.
Definition 26. Given a constrained type ∀t. A ⇒ τ \ K, where K is a canonical constraint map, a type variable t is positively reachable if t occurs positively in τ , ornegatively in A, or positively in the canonical lower bound in K of some positively
reachable t0 , or negatively in the constructed upper bound in K of some negatively
reachable t00 ; negative reachability is defined symmetrically. (Recall that an occurrenceof a variable in a simple type is positive (resp. negative) if it occurs inside an even (odd)number of type subterms in argument position of →.)A constraint t ≤ τ 0 ∈ K (resp. t ≥ τ 0 ∈ K) is reachable if t is negatively(positively) reachable; t ≤ t0 ∈ K is reachable if t is negatively and t0 is positively
reachable.
This notion of reachability is motivated by the type rules (Sect. 5, Fig. 2), which
only set upper bounds on types of subterms. Thus for instance a type variable t in thetype κ of a term e can only obtain new upper bounds (when e is used as a subterm)
if t is positively reachable in κ; in this case t’s lower bounds may be the source of aninconsistency (via transitivity). Conversely, however, if t is not reachable positively, its
lower bounds are not going to cause inconsistency in any use of e—hence they may
safely be ignored; for example,∀t. hi ⇒ t \ {>→t ≤ t, t ≤ t→>} ≤∀dec ∀t. hi ⇒ t \ {>→t ≤ t}.

Proposition 27. If GC(κ) is the constrained type obtained by removing the unreachable
constraints in κ, then GC(κ) ≤∀dec κ and κ ≤∀dec GC(κ).
Pottier [20] offers an alternative definition of reachability which ignores the polarityof the occurences of type variables. Our experience with applications of constrained typesystems to object-oriented languages [10, 9] shows that keeping track of polarity makes
a significant difference when simplifying types inferred for new objects (which are fixed
points of classes). Type variables associated with objects have upper bounds inheritedfrom the class definition (before taking the fixed point); they are often unreachable
by our definition but not by Pottier’s. Additionally, removing more constraints often
enables other simplifications, e.g. unifying a type variable with its bound.
We present an outline of an algorithm for computing ∀t0 . A0 ⇒ τ 0 \ C 0 ≤∀dec∀t00 . A00 ⇒ τ 00 \ C 00 . The algorithm either fails, if the subtyping does not hold, or it
produces a set of constraints C which only put bounds on the type variables in {t0 }; theconstraint map K required by Definition 24 can then be obtained by extending Can(C 00 )with Can(Ker(C 0 ∪ C)). The algorithm is very similar to the one for computing closureof a constraint set; in fact it is a generalization of the latter.
Algorithm 28 ∀t0 . A0 ⇒ τ 0 \ C 0 ≤∀dec ∀t00 . A00 ⇒ τ 00 \ C 00 is computed as follows.
We start by computing K 00 = Can(C 00 ) and with an initially empty set C0 of new
constraints on variables in {t00 } which are “pending proof,” and proceed as in computing
the closure of C 0 ∪ {τ 0 ≤ τ 00 } ∪ (A00 ≤ A0 ), namely, failing on inconsistent constraints,
and reducing consistent ones between constructed types to constraints on variables, ofthe form t ≤ τ (or τ ≤ t). When t ∈ {t0 }, if the constraint is already in C 0 ∪ C, thesearch succeeds; otherwise we add these constraints to C and continue as in the closure
computation by searching for a proof of τL ≤ τ (resp. τ ≤ τU ), where τL and τU
represent the lower and upper bound(s) on t in C 0 ∪ C so far. However, when t ∈ {t00 },we instead attempt to prove that this constraint is implied (by the rules for primitive
subtyping) by the constraints on t in K 00 . The proof search goes much as described in
Sect. 3.4: if τ is already an upper (lower) bound of t in K 00 ∪ C0 , it succeeds, otherwise
the new constraint is added to C0 , and we search for a proof of the constraint τU ≤ τ(resp. τ ≤ τL ), where τU and τL are the canonical upper and lower bounds on t in K 00 .
Thus, the algorithm treats variables in {t0 } and {t00 } differently, but symmetrically: it
compares new upper bounds on a t0 with its old lower bounds, but new upper bounds on
a t00 with its old (canonical) upper bound. (The reader may have noticed that convertingC 0 to a canonical constraint map is not necessary for this algorithm; however it may
improve its performance.)
Theorem 29. The relation ≤∀dec is decidable.

5Soundness of the Type System and Completeness of Inference

The typing rules shown in Fig. 2 infer sequents of the form Γ `T e : κ; the typeenvironment Γ only assigns constrained types to let-bound variables, while the types

(VAR) Γ `T x : ∀t. hxi : τi , x : τ i ⇒ τ \ C,
0

T

{t} ⊇

S

i

FTV(τi , τ, C)(A BS)Γ ` e : ∀t. hxi : τi , x : τ i ⇒ τ \ C
Γ `T λx. e : ∀t. hxi : τi i ⇒ τ →τ 0 \ C(A PP)

Γ `T e0 : ∀t. A ⇒ τ 00 →τ \ C
Γ `T e00 : ∀t. A ⇒ τ 00 \ CT0 00Γ ` e e : ∀t. A ⇒ τ \ C(L ET VAR) Γ `T X : Γ (X),X ∈ Dom(Γ )(L ET)Γ ` e : κΓ, X : κ `T e0 : κ0T
Γ ` let X = e in e0 : κ0

(S UB)

Γ `T e : κκ ≤ ∀ κ0T
Γ ` e : κ0T

Note: the closures of the constraint sets in all conclusions must be consistent.
Fig. 2. Typing rules.

of λ-bound variables are included in the context component of κ. Each rule has theimplicit side condition that the closure of the constraint set in the constrained type in
its conclusion is consistent. Rule (A PP) requires the types of the subterms to share thecontext A, constraint set C, and set of bound variables {t}. Rule (L ET) is sound withrespect to the call-by-name semantics;4 constraints on types of variables free in e neednot be reflected in κ0 unless X occurs free in e0 . Finally the subsumption rule (S UB)
replaces the constrained type of a term by a supertype; it is thus the only rule that may
allow the constraint set in the type of a term to be taken into account or modified. The
rules are parametric in the choice of ≤∀ , for which we considered a number of different
possibilities; the notation `T represents the rules with abstract ≤∀ , and `Tsem for
instance represents `T with ≤∀ defined as the concrete relation ≤∀sem .
Rules for type inference are presented5 in Fig. 3; there is no rule for subsumption,and the let-related rules are the same as in `T and hence omitted.
We may now establish soundness of the typing rules of Fig. 2. In our previous proofsof soundness of constrained typing systems [10], a direct subject reduction argument
was used. Recent observations concerning the close relation between constrained typesystems and simple type systems [19] allow us to establish soundness based on soundnessof a simple type system. We believe this direct approach to type soundness of constrained
type systems should be applicable to other constrained type languages.
Amadio and Cardelli [3] present a type system `µ with recursive types (modeledby regular trees) and a subtyping relation on them equivalent to ≤tree . This system canbe applied to the let-free fragment of our language to produce sequents of the formΦ `µ e : ϕ, where Φ is a finite map from variables to regular trees whose role in ourtype system `T is played by a context A.
A version sound with respect to call-by-value can be obtained by defining letv X = e in e0 as
let X = e in (X; e0 ) for type-checking purposes.
5
We write the inference rules with a top-down propagation of the contexts; a bottom-up presentation with synthesized context components is also possible.
4

(VARI ) Γ `I x : ∀ti , t. hxi : ti , x : ti ⇒ t \ ∅(A BSI )(A PPI )Γ `I e : ∀ti , t. hxi : ti , x : ti ⇒ τ \ C, {xi } = FV(λx. e)Γ `I λx. e : ∀ti , t. hxi : ti i ⇒ t→τ \ C
Γ `I e0 : ∀t0 . A ⇒ τ 0 \ C 0Γ `I e00 : ∀t00 . A ⇒ τ 00 \ C 00Γ `I e0 e00 : ∀t0 , t00 , t. A ⇒ t \ C 0 ∪ C 00 ∪ {τ 0 ≤ τ 00 →t}

where {t0 } − FTV(A), {t00 } − FTV(A), FTV(A), and {t} are all disjoint
Fig. 3. Typing rules modified for type inference.

We now establish that a typing derivation in `Tsem can be viewed as a family of
derivations in `µ .
Definition 30. The let-expansion LE(e) of a term e is defined as the homomorphic
extension of LE(let X = e0 in e00 ) = (LE(e0 ); LE(e00 )[LE(e0 )/X]), where the postfix [/]denotes capture-free substituton.
Theorem 31. If ∅ `Tsem e : κ, then ∅ `µ LE(e) : ϕ for each ϕ ∈ Inst(κ). If∅ `µ LE(e) : ϕ, then ∅ `Tsem e : κ for some κ such that ϕ ∈ Inst(κ).
Corollary 32. The type system `Tsem is sound.
Proof. Implied by the soundness of `µ [3]: the typability of a term e under `Tsem
implies the typability of LE(e) under `µ , which by soundness of `µ implies that theevaluation of LE(e) will not cause a run-time error. Since the let-expansion of e isobservationally equivalent to e, this implies that the evaluation of e is free of run-timeerrors.
Corollary 33. The type system `Tdec is sound.
Theorem 34. The inference system `I is complete with respect to `Tsem .

6

Related Work

Pottier [20] has independently derived results that are related to some results of this paper.
He defines a syntactic and a semantic notions of entailment on constraint sets, shows they
are equivalent, and presents a type system with subsumption based on this entailment.
He also provides an algorithm for an approximation to the entailment relation, whichappears equivalent to K ` τ ≤ τ 0 for canonical K; finally, the theory is used as a basis
for proving the soundness of a number of constrained type simplifications. However
the entailment relations do not take into account reachability of type variables, which
depends on the polarity of their occurrences and hence on the root type; in particular
his syntactic entailment C 0 ` C 00 requires C 00 ∪ C to be consistent whenever C 0 ∪ C is,for any constraint set C, including sets that put bounds on unreachable type variables,
which is not possible during type inference. As a consequence both the relation between

constrained types, implied by his subsumption rule, and its decidable approximation arestrictly less powerful than ours.
Jim [13] also defines a notion of ≤∀ that relates fewer types than ours but is still
powerful enough to prove some principal typing properties for constrained type systems.
Previous researchers [21, 4] have addressed the problem of subtyping constrained
types in the context of a system where recursive constraints are not allowed. The choiceof whether to allow or disallow recursive constraints greatly changes the theory.

7

Conclusions

This paper establishes a foundation for constrained type theory, in particular via a
powerful characterization of subtyping on constrained types. We introduce two natural notions of subtyping, observational ≤∀obs and semantic ≤∀sem , and prove that they
are equivalent; we further give a decidable approximation ≤∀dec to these relations. Bothresults represent improvements over recent work on subtyping of constrained types withrecursive constraints [9, 20, 13]. We also introduce a novel closed form of constraint
types with contexts, which eliminates the problems associated with free type variables.
Finally, we present a type system with principal constrained types, and establish its
soundness via reduction to the system of Amadio and Cardelli.
The most generous relations ≤∀sem and ≤∀obs may be undecidable, but we believe that≤∀dec is powerful enough to be useful in practice for signature matching and constraint
simplification. Our confidence in the system stems from the fact that ≤∀dec subsumes
the Amadio/Cardelli subtyping of recursive types, the type scheme instantiation in the
Hindley/Milner system, and the subtyping relation of [20]. Additionally, it turns out thatthe known simplifications of constraint sets do not test the limits of the system basedon ≤∀dec ; we have shown in this paper that ≤∀dec can be used to demonstrate the correctness of simplifications not included in other systems. Similarly, functor signatures may
generally be produced by starting with an inferred constrained type and transforming
it in regular ways, thus avoiding constrained types which ≤∀dec does not relate to theinferred type. We have yet to find a realistic subtyping example which is semanticallysound but is not derivable using ≤∀dec , but most convincing would be the performanceof a system that uses it for signature matching and simplifications in practice on real
code; we are in the process of constructing an implementation for this purpose.
Acknowledgements We wish to thank Simon Marlow, François Pottier, Didier Rémy,Philip Wadler, and the anonymous referees for many helpful comments and suggestions.

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