I'm trying to translate the following piece of code:
(* elim: vname * term * (term * term) list * subst -> subst *)
and elim(x,t,S,s) =
if occurs x t then raise UNIFY
else let val xt = lift [(x,t)]
in solve(map (fn (t1,t2) => (xt t1, xt t2)) S,
(x,t) :: (map (fn (y,u) => (y, xt u)) s))
end;
coming form this repository into another language. My problem comes because I don't know exactly what is the semantics of (xt t1, xt t2)
in ML. In principle xt,t1,t2 are terms so i imagine that xt t1 should be some sort of concatenation (althougth there is no concatenation defined).
What does this code mean in ML?
For completeness here is the rest of the relevant definitions:
type vname = string * int;
type subst = (vname * term) list;
datatype term = V of vname | T of string * term list;
(* lift: subst -> term -> term *)
(* indom: vname -> subst -> bool *)
(* solve: (term * term)list * subst -> subst *)
Juxtaposition is function application.
(a,b) is a pair.
As you can see from the type of lift, lift [(x,t)] – that is, xt– is a function term -> term.
Thus, (xt t1, xt t2) is a pair term * term, whose first element is the result of applying xtto t1, and whose second element is xt applied to t2.
Related
Context: I have been trying to implement the unification algorithm (the algorithm to find the most general unifier of two abstract syntax trees). Since a unifier is a substitution, algorithm requires defining composition of substitutions.
To be specific, given a type treeSigma dependent on another type X, a substitution is a function of type:
X -> treeSigma X
and the function substitute takes a substitution as an input and has type
substitute: (X-> (treeSigma X))-> (treeSigma X) -> (treeSigma X)
I need to define a function to compose two substitutions:
compose_kleisli (rho1 rho2: X->(treeSigma X)) : (treeSigma X) := ...
such that,
forall tr: treeSigma X,
substitute (compose_kleisli rho1 rho2) tr = substitute rho1 (substitute rho2 tr).
I am fairly new to coq and have been stuck with defining this composition.
How can I define this composition?
I tried to define it using Record like this:
Record compose {X s} (rho1 rho2: X-> treeSigma X):= mkCompose{
RHO: X-> treeSigma X;
CONDITION: forall t, substitute RHO t = substitute rho2 (substitute rho1 t)
}.
but along with this, I would need to prove the result that the composition can be defined for any two substitutions. Something like:
Theorem composeTotal: forall {X s} (rho1 rho2: X-> treeSigma s X), exists rho3,
forall t, substitute rho3 t = substitute rho2 (substitute rho1 t).
Proving this would require a construction of rho3 which circles back to the same problem of defining compose.
treeSigma is defined as:
(* Signature *)
Record sigma: Type := mkSigma {
symbol : Type;
arity : symbol -> nat
}.
Record sigmaLeaf (s:sigma): Type := mkLeaf {
cLeaf: symbol s;
condLeaf: arity s cLeaf = 0
}.
Record sigmaNode (s:sigma): Type := mkNode {
fNode: symbol s;
condNode: arity s fNode <> 0
}.
(* Sigma Algebra *)
Record sigAlg (s:sigma) (X:Type) := mkAlg {
Carrier: Type;
meaning: forall f:(sigmaNode s), (Vector.t Carrier (arity s (fNode s f))) -> Carrier;
meanLeaf: forall f:(sigmaLeaf s), Vector.t Carrier 0 -> Carrier
}.
(* Abstract tree on arbitrary signature. *)
Inductive treeSigma (s:sigma) (X:Type):=
| VAR (x:X)
| LEAF (c: sigmaLeaf s)
| NODE (f: sigmaNode s) (sub: Vector.t (treeSigma s X) (arity s (fNode s f)) ).
(* Defining abstract syntax as a sigma algebra. *)
Definition meanTreeNode {s X} (f:sigmaNode s) (sub: Vector.t (treeSigma s X) (s.(arity)
(fNode s f))): treeSigma s X:= NODE s X f sub.
Definition meanTreeLeaf {s X} (c:sigmaLeaf s) (sub: Vector.t (treeSigma s X) 0) := LEAF s X c.
Definition treeSigAlg {s X} := mkAlg s X (treeSigma s X) meanTreeNode meanTreeLeaf.
The substitution function is defined as:
Fixpoint homoSigma1 {X:Type} {s} (A: sigAlg s X) (rho: X-> (Carrier s X A))
(wft: (treeSigma s X)) {struct wft}: (Carrier s X A) :=
match wft with
| VAR _ _ x => rho x
| LEAF _ _ c => meanLeaf s X A c []
| NODE _ _ f l2 => meanNode s X A f (
(fix homoSigVec k (l2:Vector.t _ k):= match l2 with
| [] => []
| t::l2s => (homoSigma1 A rho t):: (homoSigVec (vlen _ l2s) l2s)
end)
(arity s (fNode s f)) l2)
end.
Definition substitute {X s} (rho: X-> treeSigma s X) (t: treeSigma s X) := #homoSigma1 X s treeSigAlg rho t.
To be particular, a substitution is the homomorphic extension of rho (which is a variable valuation).
Definitions like this are challenging to work with because the tree type occurs recursively inside of another inductive type. Coq has trouble generating induction principles for these types on its own, so you need to help it a little bit. Here is a possible solution, for a slightly simplified set up:
Require Import Coq.Vectors.Vector.
Import VectorNotations.
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Section Dev.
Variable symbol : Type.
Variable arity : symbol -> nat.
Record alg := Alg {
alg_sort :> Type;
alg_op : forall f : symbol, Vector.t alg_sort (arity f) -> alg_sort;
}.
Arguments alg_op {_} f _.
(* Turn off the automatic generation of induction principles.
This tree type does not distinguish between leaves and nodes,
since they only differ in their arity. *)
Unset Elimination Schemes.
Inductive treeSigma (X:Type) :=
| VAR (x:X)
| NODE (f: symbol) (args : Vector.t (treeSigma X) (arity f)).
Arguments NODE {X} _ _.
Set Elimination Schemes.
(* Manual definition of a custom induction principle for treeSigma.
HNODE is the inductive case for the NODE constructor; the vs argument is
saying that the induction hypothesis holds for each tree in the vector of
arguments. *)
Definition treeSigma_rect (X : Type) (T : treeSigma X -> Type)
(HVAR : forall x, T (VAR x))
(HNODE : forall f (ts : Vector.t (treeSigma X) (arity f))
(vs : Vector.fold_right (fun t V => T t * V)%type ts unit),
T (NODE f ts)) :
forall t, T t :=
fix loopTree (t : treeSigma X) : T t :=
match t with
| VAR x => HVAR x
| NODE f ts =>
let fix loopVector n (ts : Vector.t (treeSigma X) n) :
Vector.fold_right (fun t V => T t * V)%type ts unit :=
match ts with
| [] => tt
| t :: ts => (loopTree t, loopVector _ ts)
end in
HNODE f ts (loopVector (arity f) ts)
end.
Definition treeSigma_ind (X : Type) (T : treeSigma X -> Prop) :=
#treeSigma_rect X T.
Definition treeSigma_alg (X:Type) : alg := {|
alg_sort := treeSigma X;
alg_op := #NODE X;
|}.
Fixpoint homoSigma {X : Type} {Y : alg} (ρ : X -> Y) (t : treeSigma X) : Y :=
match t with
| VAR x => ρ x
| NODE f xs => alg_op f (Vector.map (homoSigma ρ) xs)
end.
Definition substitute X (ρ : X -> treeSigma X) (t : treeSigma X) : treeSigma X :=
#homoSigma X (treeSigma_alg X) ρ t.
(* You can define composition simply by applying using substitution. *)
Definition compose X (ρ1 ρ2 : X -> treeSigma X) : X -> treeSigma X :=
fun x => substitute ρ1 (ρ2 x).
(* The property you are looking for follows by induction on the tree. Note
that this requires a nested induction on the vector of arguments. *)
Theorem composeP X (ρ1 ρ2 : X -> treeSigma X) t :
substitute (compose ρ1 ρ2) t = substitute ρ1 (substitute ρ2 t).
Proof.
unfold compose, substitute.
induction t as [x|f ts IH]; trivial.
simpl; f_equal.
induction ts as [|n t ts IH']; trivial.
simpl.
destruct IH as [e IH].
rewrite e.
f_equal.
now apply IH'.
Qed.
End Dev.
In order to do this you need to use the operations of the monad, typically:
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Section MonadKleisli.
(* Set Universe Polymorphism. // Needed for real use cases *)
Variable (M : Type -> Type).
Variable (Ma : forall A B, (A -> B) -> M A -> M B).
Variable (η : forall A, A -> M A).
Variable (μ : forall A, M (M A) -> M A).
(* Compose: o^* *)
Definition oStar A B C (f : A -> M B) (g: B -> M C) : A -> M C :=
fun x => μ (Ma g (f x)).
(* Bind *)
Definition bind A B (x : M A) (f : A -> M B) : M B := oStar (fun _ => x) f tt.
End MonadKleisli.
Depending on how you organize your definitions, proving your desired properties will likely require functional extensionality, not a big deal usually but something to keep in ind.
I’d like to define the following function using Program Fixpoint or Function in Coq:
Require Import Coq.Lists.List.
Import ListNotations.
Require Import Coq.Program.Wf.
Require Import Recdef.
Inductive Tree := Node : nat -> list Tree -> Tree.
Fixpoint height (t : Tree) : nat :=
match t with
| Node x ts => S (fold_right Nat.max 0 (map height ts))
end.
Program Fixpoint mapTree (f : nat -> nat) (t : Tree) {measure (height t)} : Tree :=
match t with
Node x ts => Node (f x) (map (fun t => mapTree f t) ts)
end.
Next Obligation.
Unfortunately, at this point I have a proof obligation height t < height (Node x ts) without knowing that t is a member of ts.
Similarly with Function instead of Program Fixpoint, only that Function detects the problem and aborts the definition:
Error:
the term fun t : Tree => mapTree f t can not contain a recursive call to mapTree
I would expect to get a proof obligation of In t ts → height t < height (Node x ts).
Is there a way of getting that that does not involve restructuring the function definition? (I know work-arounds that require inlining the definition of map here, for example – I’d like to avoid these.)
Isabelle
To justify that expectation, let me show what happens when I do the same in Isabelle, using the function command, which is (AFAIK) related to Coq’s Function command:
theory Tree imports Main begin
datatype Tree = Node nat "Tree list"
fun height where
"height (Node _ ts) = Suc (foldr max (map height ts) 0)"
function mapTree where
"mapTree f (Node x ts) = Node (f x) (map (λ t. mapTree f t) ts)"
by pat_completeness auto
termination
proof (relation "measure (λ(f,t). height t)")
show "wf (measure (λ(f, t). height t))" by auto
next
fix f :: "nat ⇒ nat" and x :: nat and ts :: "Tree list" and t
assume "t ∈ set ts"
thus "((f, t), (f, Node x ts)) ∈ measure (λ(f, t). height t)"
by (induction ts) auto
qed
In the termination proof, I get the assumption t ∈ set ts.
Note that Isabelle does not require a manual termination proof here, and the following definition works just fine:
fun mapTree where
"mapTree f (Node x ts) = Node (f x) (map (λ t. mapTree f t) ts)"
This works because the map function has a “congruence lemma” of the form
xs = ys ⟹ (⋀x. x ∈ set ys ⟹ f x = g x) ⟹ map f xs = map g ys
that the function command uses to find out that the termination proof only needs to consider t ∈ set ts..
If such a lemma is not available, e.g. because I define
definition "map' = map"
and use that in mapTree, I get the same unprovable proof obligation as in Coq. I can make it work again by declaring a congruence lemma for map', e.g. using
declare map_cong[folded map'_def,fundef_cong]
In this case, you actually do not need well-founded recursion in its full generality:
Require Import Coq.Lists.List.
Set Implicit Arguments.
Inductive tree := Node : nat -> list tree -> tree.
Fixpoint map_tree (f : nat -> nat) (t : tree) : tree :=
match t with
| Node x ts => Node (f x) (map (fun t => map_tree f t) ts)
end.
Coq is able to figure out by itself that recursive calls to map_tree are performed on strict subterms. However, proving anything about this function is difficult, as the induction principle generated for tree is not useful:
tree_ind :
forall P : tree -> Prop,
(forall (n : nat) (l : list tree), P (Node n l)) ->
forall t : tree, P t
This is essentially the same problem you described earlier. Luckily, we can fix the issue by proving our own induction principle with a proof term.
Require Import Coq.Lists.List.
Import ListNotations.
Unset Elimination Schemes.
Inductive tree := Node : nat -> list tree -> tree.
Set Elimination Schemes.
Fixpoint tree_ind
(P : tree -> Prop)
(IH : forall (n : nat) (ts : list tree),
fold_right (fun t => and (P t)) True ts ->
P (Node n ts))
(t : tree) : P t :=
match t with
| Node n ts =>
let fix loop ts :=
match ts return fold_right (fun t' => and (P t')) True ts with
| [] => I
| t' :: ts' => conj (tree_ind P IH t') (loop ts')
end in
IH n ts (loop ts)
end.
Fixpoint map_tree (f : nat -> nat) (t : tree) : tree :=
match t with
| Node x ts => Node (f x) (map (fun t => map_tree f t) ts)
end.
The Unset Elimination Schemes command prevents Coq from generating its default (and not useful) induction principle for tree. The occurrence of fold_right on the induction hypothesis simply expresses that the predicate P holds of every tree t' appearing in ts.
Here is a statement that you can prove using this induction principle:
Lemma map_tree_comp f g t :
map_tree f (map_tree g t) = map_tree (fun n => f (g n)) t.
Proof.
induction t as [n ts IH]; simpl; f_equal.
induction ts as [|t' ts' IHts]; try easy.
simpl in *.
destruct IH as [IHt' IHts'].
specialize (IHts IHts').
now rewrite IHt', <- IHts.
Qed.
You can now do this with Equations and get the right elimination principle automatically, using either structural nested recursion or well-founded recursion
In general, it might be advisable to avoid this problem. But if one really wants to obtain the proof obligation that Isabelle gives you, here is a way:
In Isabelle, we can give an external lemma that stats that map applies its arguments only to members of the given list. In Coq, we cannot do this in an external lemma, but we can do it in the type. So instead of the normal type of map
forall A B, (A -> B) -> list A -> list B
we want the type to say “f is only ever applied to elements of the list:
forall A B (xs : list A), (forall x : A, In x xs -> B) -> list B
(It requires reordering the argument so that the type of f can mention xs).
Writing this function is not trivial, and I found it easier to use a proof script:
Definition map {A B} (xs : list A) (f : forall (x:A), In x xs -> B) : list B.
Proof.
induction xs.
* exact [].
* refine (f a _ :: IHxs _).
- left. reflexivity.
- intros. eapply f. right. eassumption.
Defined.
But you can also write it “by hand”:
Fixpoint map {A B} (xs : list A) : forall (f : forall (x:A), In x xs -> B), list B :=
match xs with
| [] => fun _ => []
| x :: xs => fun f => f x (or_introl eq_refl) :: map xs (fun y h => f y (or_intror h))
end.
In either case, the result is nice: I can use this function in mapTree, i.e.
Program Fixpoint mapTree (f : nat -> nat) (t : Tree) {measure (height t)} : Tree :=
match t with
Node x ts => Node (f x) (map ts (fun t _ => mapTree f t))
end.
Next Obligation.
and I don’t have to do anything with the new argument to f, but it shows up in the the termination proof obligation, as In t ts → height t < height (Node x ts) as desired. So I can prove that and define mapTree:
simpl.
apply Lt.le_lt_n_Sm.
induction ts; inversion_clear H.
- subst. apply PeanoNat.Nat.le_max_l.
- rewrite IHts by assumption.
apply PeanoNat.Nat.le_max_r.
Qed.
It only works with Program Fixpoint, not with Function, unfortunately.
My attempt to create a custom linear order for a custom data type failed, Below is my code:
theory Scratch
imports Main
begin
datatype st = Str "string"
fun solf_str_int:: "string ⇒ int" where
"solf_str_int str = (if (size str) > 0
then int(nat_of_char (hd str) + 1) + 100 * (solf_str_int (tl str))
else 0)"
fun soflord:: "st ⇒ st ⇒ bool" where
"soflord s1 s2 = (case s1 of Str ss1 ⇒ (case s2 of Str ss2 ⇒
(solf_str_int ss1) ≤ (solf_str_int ss2)))"
instantiation st :: linorder
begin
definition nleq: "less_eq n1 n2 == soflord n1 n2"
definition neq: "eq n1 n2 == (n1 ≤ n2) ∧ (n2 ≤ n1)"
definition nle: "less n1 n2 == (n1 ≤ n2) ∧ (¬(n1 = n2))" (* ++ *)
instance proof
fix n1 n2 x y :: st
show "n1 ≤ n1" by (simp add:nleq split:st.split)
show "(n1 ≤ n2) ∨ (n2 ≤ n1)" by (simp add:nleq split:st.split) (*why is 'by ()' highlited?*)
(*this fail if I comment line ++ out*)
show "(x < y) = (x ≤ y ∧ (¬ (y ≤ x)))" by (simp add:nleq neq split:node.split)
qed
end
end
The definition marked with (* ++ *) is not right and if delete it the last show give problems.
How do I correct the prove?
Why is the second last show partially highlighted?
When you define the operations of a type class (less_eq and less in the case of linorder), the name of the overloaded operation can only be used if the inferred type of the operation matches exactly the overloaded instance that is being defined. In particular, the type is not specialised if it turns out to be too general.
The definition for less_eq works because soflord restricts the types of n1 and n2 to st, so less_eq is used with type st => st => bool, which is precisely what is needed here. For less, type inference computes the most general type 'b :: ord => 'b => bool. As this is not of the expected type st => st => bool, Isabelle does not recognize the definition as a definition of an overloaded operation and consequently complains that you want to redefine an existing operation in its full generality. If you restrict the types as necessary, then the definition works as expected.
definition nle: "less n1 (n2 :: st) == (n1 ≤ n2) ∧ (¬(n1 = n2))"
However, your definitions do not define a linear order on st. The problem is that antisymmetry is violated. For example, the two strings Str ''d'' and Str [Char Nibble0 Nibble0, Char Nibble0 Nibble0] (i.e., the string consisting of two characters at codepoint 0) are "equivalent" in your order, although they are different values. You attempt to define equality on st, too, but in higher-order logic, equality cannot be defined. It is determined by the way you constructed your type. If you really want to identify strings that are equivalent according to your order, you have to construct a quotient first, e.g., using the quotient package.
The purple highlighting of by(simp ...) indicates that the proof method simp is still running. In your case, it will not terminate, because simp will keep unfolding the defining equation for solf_str_int: its right-hand side contains an instance of the left-hand side. I recommend that you define your functions by pattern-matching on the left-hand side of =. Then, the equations are only used when they can consume a pattern. Thus, you have to trigger case distinctions yourself (e.g. using cases), but you also get more control over the automated tactics.
I need to make an algorithm to solve this problem using a BFS :
given an oriented weighted graph, a start node, a stop node, and a integer K, say if exist a path between start and stop with wight at least k.
So, first I declared my weighted oriented graph type, a list of triples:
type 'a graph = Gr of ('a * 'a * 'a) list;;
let grafo1 = Gr [(1,3,2);(1,1,5);(2,2,3);(5,5,3);(5,4,6);(3,1,6);(3,7,4);(6,2,7);(4,4,6)];;
In (x,y,z), x is the starting node, y the edge weight and z the arrive node.
Then I made a succ function:
let succ (Gr arcs) n=
let rec aux = function
[] -> []
| (x,y,z):: rest ->
if n = x then z::(aux rest)
else aux rest
in aux arcs;;
This function give me the successors of a node as oputput, so:
succ grafo1 1
gives me
int list = [2; 5]
as output.
In the end, I made this bf_path function, it's a modified BFS that can find a path between 2 nodes (otherwise it raises an exception) and it takes 3 inputs: a graph, a predicate and a starting node
let bf_path g p start =
let rec aux visited = function
[] -> raise Not_found
| x::rest -> if List.mem x visited then aux visited rest
else if p x then [x]
else try aux (x::visited) rest
with Not_found ->
x:: aux (x::visited) (succ g x)
in aux [] [start];;
The predicate specifies the condition, so the call:
bf_path grafo1 ((=)7)1
gives me int list = [1; 5; 6; 7] as output, the path between nodes 1 and 7.
Now, I can find a path but I need to find a path with at least weight K, so I made a little function that takes a list of triples as input and it sum the weight value:
let rec tot = function
[] -> 0
|(v,c,p)::t -> c + (tot t);;
So, call and output:
tot [(2,2,3);(4,5,6);(8,9,0)]
- : int = 16
I thought all I needed was to add the condition inside the function so I made this function where I add an int K as input and a condition: (tot path >= k)
let bf_path_final g p start k =
let rec aux visited = function
[] -> raise Not_found
| x::rest -> if List.mem x visited then aux visited rest
else if p x then
if (tot [x]) >= k then [x]
else aux visited rest
else try aux (x::visited) rest
with Not_found ->
x:: aux (x::visited) (succ g x)
in aux [] [start];;
The function compiles without problem:
val bf_path_final : ('a * int * 'b) graph ->
('a * int * 'b -> bool) -> 'a * int * 'b -> int -> ('a * int * 'b) list = <fun>
However, I get an error when I try to call it:
bf_path_final grafo1 ((=)4)1,13;;
^^^^^^
Error: This expression has type int graph_w
but an expression was expected of type ('a * int * 'b) graph_w
So, is the function wrong or do I have to call it on another way?
Another solution was to give the function bf_path output (the path) as input to my tot function, but my output is a list of int, not a list of triples, so i tried to convert my first function to give an output of triplees:
(ex: instead of [1;5;6;7] it should give [(1,1,5);(5,4,6);(6,2,7)])
let bf_path_tr g p start =
let rec aux visited = function
[] -> raise Not_found
| (x,y,z)::rest -> if List.mem x visited then aux visited rest
else if p x then [(x,y,z)]
else try aux (x::visited) rest
with Not_found ->
(x,y,z):: aux (x::visited) (succ_w g (x,y,z))
in aux [] [start];;
Same results, the function is compiled
val bf_path_tr :
('a * 'b * 'c) graph ->
('a -> bool) -> 'a * 'b * 'c -> ('a * 'b * 'c) list = <fun>
but I get the same error:
bf_path_tr grafo1 ((=)7)2
Characters 11-18:
bf_path_tr grafo1 ((=)7)2;;
^^^^^^
Error: This expression has type int graph
but an expression was expected of type ('a * 'b * 'c) graph
Any ideas to solve at least one of these two problems?
The best way to debug this sort of thing is to start adding explicit type annotations everywhere, till you find the place where your expectations don't match the inferred type.
let bf_path_final (g : int graph) (p : int -> bool) (start : int) (k : int) =
let rec aux (visited : int list) = function
[] -> raise Not_found
| x::rest -> if List.mem x visited then aux visited rest
else if p x then (
if (tot [x]) >= k then [x]
else aux visited rest )
else try aux (x::visited) rest
with Not_found ->
x:: aux (x::visited) (succ g x)
in aux [] [start];;
raises the error
File "test.ml", line 32, characters 17-18:
Error: This expression has type int but an expression was expected of type
'a * int * 'b
which points to if (tot [x]) where indeed tot expects a list of triples, but you have passed it an int list.
Also did you really mean to call tot [x]? It's not totaling anything; x is just a single node there.
In pure functional languages like Haskell, is there an algorithm to get the inverse of a function, (edit) when it is bijective? And is there a specific way to program your function so it is?
In some cases, yes! There's a beautiful paper called Bidirectionalization for Free! which discusses a few cases -- when your function is sufficiently polymorphic -- where it is possible, completely automatically to derive an inverse function. (It also discusses what makes the problem hard when the functions are not polymorphic.)
What you get out in the case your function is invertible is the inverse (with a spurious input); in other cases, you get a function which tries to "merge" an old input value and a new output value.
No, it's not possible in general.
Proof: consider bijective functions of type
type F = [Bit] -> [Bit]
with
data Bit = B0 | B1
Assume we have an inverter inv :: F -> F such that inv f . f ≡ id. Say we have tested it for the function f = id, by confirming that
inv f (repeat B0) -> (B0 : ls)
Since this first B0 in the output must have come after some finite time, we have an upper bound n on both the depth to which inv had actually evaluated our test input to obtain this result, as well as the number of times it can have called f. Define now a family of functions
g j (B1 : B0 : ... (n+j times) ... B0 : ls)
= B0 : ... (n+j times) ... B0 : B1 : ls
g j (B0 : ... (n+j times) ... B0 : B1 : ls)
= B1 : B0 : ... (n+j times) ... B0 : ls
g j l = l
Clearly, for all 0<j≤n, g j is a bijection, in fact self-inverse. So we should be able to confirm
inv (g j) (replicate (n+j) B0 ++ B1 : repeat B0) -> (B1 : ls)
but to fulfill this, inv (g j) would have needed to either
evaluate g j (B1 : repeat B0) to a depth of n+j > n
evaluate head $ g j l for at least n different lists matching replicate (n+j) B0 ++ B1 : ls
Up to that point, at least one of the g j is indistinguishable from f, and since inv f hadn't done either of these evaluations, inv could not possibly have told it apart – short of doing some runtime-measurements on its own, which is only possible in the IO Monad.
⬜
You can look it up on wikipedia, it's called Reversible Computing.
In general you can't do it though and none of the functional languages have that option. For example:
f :: a -> Int
f _ = 1
This function does not have an inverse.
Not in most functional languages, but in logic programming or relational programming, most functions you define are in fact not functions but "relations", and these can be used in both directions. See for example prolog or kanren.
Tasks like this are almost always undecidable. You can have a solution for some specific functions, but not in general.
Here, you cannot even recognize which functions have an inverse. Quoting Barendregt, H. P. The Lambda Calculus: Its Syntax and Semantics. North Holland, Amsterdam (1984):
A set of lambda-terms is nontrivial if it is neither the empty nor the full set. If A and B are two nontrivial, disjoint sets of lambda-terms closed under (beta) equality, then A and B are recursively inseparable.
Let's take A to be the set of lambda terms that represent invertible functions and B the rest. Both are non-empty and closed under beta equality. So it's not possible to decide whether a function is invertible or not.
(This applies to the untyped lambda calculus. TBH I don't know if the argument can be directly adapted to a typed lambda calculus when we know the type of a function that we want to invert. But I'm pretty sure it will be similar.)
If you can enumerate the domain of the function and can compare elements of the range for equality, you can - in a rather straightforward way. By enumerate I mean having a list of all the elements available. I'll stick to Haskell, since I don't know Ocaml (or even how to capitalise it properly ;-)
What you want to do is run through the elements of the domain and see if they're equal to the element of the range you're trying to invert, and take the first one that works:
inv :: Eq b => [a] -> (a -> b) -> (b -> a)
inv domain f b = head [ a | a <- domain, f a == b ]
Since you've stated that f is a bijection, there's bound to be one and only one such element. The trick, of course, is to ensure that your enumeration of the domain actually reaches all the elements in a finite time. If you're trying to invert a bijection from Integer to Integer, using [0,1 ..] ++ [-1,-2 ..] won't work as you'll never get to the negative numbers. Concretely, inv ([0,1 ..] ++ [-1,-2 ..]) (+1) (-3) will never yield a value.
However, 0 : concatMap (\x -> [x,-x]) [1..] will work, as this runs through the integers in the following order [0,1,-1,2,-2,3,-3, and so on]. Indeed inv (0 : concatMap (\x -> [x,-x]) [1..]) (+1) (-3) promptly returns -4!
The Control.Monad.Omega package can help you run through lists of tuples etcetera in a good way; I'm sure there's more packages like that - but I don't know them.
Of course, this approach is rather low-brow and brute-force, not to mention ugly and inefficient! So I'll end with a few remarks on the last part of your question, on how to 'write' bijections. The type system of Haskell isn't up to proving that a function is a bijection - you really want something like Agda for that - but it is willing to trust you.
(Warning: untested code follows)
So can you define a datatype of Bijection s between types a and b:
data Bi a b = Bi {
apply :: a -> b,
invert :: b -> a
}
along with as many constants (where you can say 'I know they're bijections!') as you like, such as:
notBi :: Bi Bool Bool
notBi = Bi not not
add1Bi :: Bi Integer Integer
add1Bi = Bi (+1) (subtract 1)
and a couple of smart combinators, such as:
idBi :: Bi a a
idBi = Bi id id
invertBi :: Bi a b -> Bi b a
invertBi (Bi a i) = (Bi i a)
composeBi :: Bi a b -> Bi b c -> Bi a c
composeBi (Bi a1 i1) (Bi a2 i2) = Bi (a2 . a1) (i1 . i2)
mapBi :: Bi a b -> Bi [a] [b]
mapBi (Bi a i) = Bi (map a) (map i)
bruteForceBi :: Eq b => [a] -> (a -> b) -> Bi a b
bruteForceBi domain f = Bi f (inv domain f)
I think you could then do invert (mapBi add1Bi) [1,5,6] and get [0,4,5]. If you pick your combinators in a smart way, I think the number of times you'll have to write a Bi constant by hand could be quite limited.
After all, if you know a function is a bijection, you'll hopefully have a proof-sketch of that fact in your head, which the Curry-Howard isomorphism should be able to turn into a program :-)
I've recently been dealing with issues like this, and no, I'd say that (a) it's not difficult in many case, but (b) it's not efficient at all.
Basically, suppose you have f :: a -> b, and that f is indeed a bjiection. You can compute the inverse f' :: b -> a in a really dumb way:
import Data.List
-- | Class for types whose values are recursively enumerable.
class Enumerable a where
-- | Produce the list of all values of type #a#.
enumerate :: [a]
-- | Note, this is only guaranteed to terminate if #f# is a bijection!
invert :: (Enumerable a, Eq b) => (a -> b) -> b -> Maybe a
invert f b = find (\a -> f a == b) enumerate
If f is a bijection and enumerate truly produces all values of a, then you will eventually hit an a such that f a == b.
Types that have a Bounded and an Enum instance can be trivially made RecursivelyEnumerable. Pairs of Enumerable types can also be made Enumerable:
instance (Enumerable a, Enumerable b) => Enumerable (a, b) where
enumerate = crossWith (,) enumerate enumerate
crossWith :: (a -> b -> c) -> [a] -> [b] -> [c]
crossWith f _ [] = []
crossWith f [] _ = []
crossWith f (x0:xs) (y0:ys) =
f x0 y0 : interleave (map (f x0) ys)
(interleave (map (flip f y0) xs)
(crossWith f xs ys))
interleave :: [a] -> [a] -> [a]
interleave xs [] = xs
interleave [] ys = []
interleave (x:xs) ys = x : interleave ys xs
Same goes for disjunctions of Enumerable types:
instance (Enumerable a, Enumerable b) => Enumerable (Either a b) where
enumerate = enumerateEither enumerate enumerate
enumerateEither :: [a] -> [b] -> [Either a b]
enumerateEither [] ys = map Right ys
enumerateEither xs [] = map Left xs
enumerateEither (x:xs) (y:ys) = Left x : Right y : enumerateEither xs ys
The fact that we can do this both for (,) and Either probably means that we can do it for any algebraic data type.
Not every function has an inverse. If you limit the discussion to one-to-one functions, the ability to invert an arbitrary function grants the ability to crack any cryptosystem. We kind of have to hope this isn't feasible, even in theory!
In some cases, it is possible to find the inverse of a bijective function by converting it into a symbolic representation. Based on this example, I wrote this Haskell program to find inverses of some simple polynomial functions:
bijective_function x = x*2+1
main = do
print $ bijective_function 3
print $ inverse_function bijective_function (bijective_function 3)
data Expr = X | Const Double |
Plus Expr Expr | Subtract Expr Expr | Mult Expr Expr | Div Expr Expr |
Negate Expr | Inverse Expr |
Exp Expr | Log Expr | Sin Expr | Atanh Expr | Sinh Expr | Acosh Expr | Cosh Expr | Tan Expr | Cos Expr |Asinh Expr|Atan Expr|Acos Expr|Asin Expr|Abs Expr|Signum Expr|Integer
deriving (Show, Eq)
instance Num Expr where
(+) = Plus
(-) = Subtract
(*) = Mult
abs = Abs
signum = Signum
negate = Negate
fromInteger a = Const $ fromIntegral a
instance Fractional Expr where
recip = Inverse
fromRational a = Const $ realToFrac a
(/) = Div
instance Floating Expr where
pi = Const pi
exp = Exp
log = Log
sin = Sin
atanh = Atanh
sinh = Sinh
cosh = Cosh
acosh = Acosh
cos = Cos
tan = Tan
asin = Asin
acos = Acos
atan = Atan
asinh = Asinh
fromFunction f = f X
toFunction :: Expr -> (Double -> Double)
toFunction X = \x -> x
toFunction (Negate a) = \a -> (negate a)
toFunction (Const a) = const a
toFunction (Plus a b) = \x -> (toFunction a x) + (toFunction b x)
toFunction (Subtract a b) = \x -> (toFunction a x) - (toFunction b x)
toFunction (Mult a b) = \x -> (toFunction a x) * (toFunction b x)
toFunction (Div a b) = \x -> (toFunction a x) / (toFunction b x)
with_function func x = toFunction $ func $ fromFunction x
simplify X = X
simplify (Div (Const a) (Const b)) = Const (a/b)
simplify (Mult (Const a) (Const b)) | a == 0 || b == 0 = 0 | otherwise = Const (a*b)
simplify (Negate (Negate a)) = simplify a
simplify (Subtract a b) = simplify ( Plus (simplify a) (Negate (simplify b)) )
simplify (Div a b) | a == b = Const 1.0 | otherwise = simplify (Div (simplify a) (simplify b))
simplify (Mult a b) = simplify (Mult (simplify a) (simplify b))
simplify (Const a) = Const a
simplify (Plus (Const a) (Const b)) = Const (a+b)
simplify (Plus a (Const b)) = simplify (Plus (Const b) (simplify a))
simplify (Plus (Mult (Const a) X) (Mult (Const b) X)) = (simplify (Mult (Const (a+b)) X))
simplify (Plus (Const a) b) = simplify (Plus (simplify b) (Const a))
simplify (Plus X a) = simplify (Plus (Mult 1 X) (simplify a))
simplify (Plus a X) = simplify (Plus (Mult 1 X) (simplify a))
simplify (Plus a b) = (simplify (Plus (simplify a) (simplify b)))
simplify a = a
inverse X = X
inverse (Const a) = simplify (Const a)
inverse (Mult (Const a) (Const b)) = Const (a * b)
inverse (Mult (Const a) X) = (Div X (Const a))
inverse (Plus X (Const a)) = (Subtract X (Const a))
inverse (Negate x) = Negate (inverse x)
inverse a = inverse (simplify a)
inverse_function x = with_function inverse x
This example only works with arithmetic expressions, but it could probably be generalized to work with lists as well. There are also several implementations of computer algebra systems in Haskell that may be used to find the inverse of a bijective function.
No, not all functions even have inverses. For instance, what would the inverse of this function be?
f x = 1