# TrieNumber Representations and Efficient Lookup Tables

# LogN Penalties in Functional Programming

*indexed array update*in constant time, while functional programs cannot.

collisions=0; for (i=0; i<2N; i++) a[i]=0; for (j=0; j<N; j++) { i = input[j]; if (a[i] != 0) collisions++; a[i]=1; } return collisions;

*Verified Functional Algorithms*we prove program correct by relying on the

*tractable proof theory*of purely functional programs; if we use nonfunctional features of ML, then this style of proof will not work. We'd have to use something like Hoare logic instead (see Hoare.v in volume 2 of

*Software Foundations*), and that is not

*nearly*as nice.

*only*run the programs in Ocaml, not inside Coq.

# A Simple Program That's Waaaaay Too Slow.

From Coq Require Import Strings.String. (* for manual grading *)

From Coq Require Import ZArith.

From Coq Require Import PArith.

From VFA Require Import Perm.

From VFA Require Import Maps.

Import FunctionalExtensionality.

Module VerySlow.

Fixpoint loop (input: list nat) (c: nat) (table: total_map bool) : nat :=

match input with

| nil ⇒ c

| a::al ⇒ if table a

then loop al (c+1) table

else loop al c (t_update table a true)

end.

Definition collisions (input: list nat) : nat :=

loop input 0 (t_empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6] = 1.

Proof. reflexivity. Qed.

From Coq Require Import ZArith.

From Coq Require Import PArith.

From VFA Require Import Perm.

From VFA Require Import Maps.

Import FunctionalExtensionality.

Module VerySlow.

Fixpoint loop (input: list nat) (c: nat) (table: total_map bool) : nat :=

match input with

| nil ⇒ c

| a::al ⇒ if table a

then loop al (c+1) table

else loop al c (t_update table a true)

end.

Definition collisions (input: list nat) : nat :=

loop input 0 (t_empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6] = 1.

Proof. reflexivity. Qed.

This program takes cubic time, O(N^3). Let's assume that there are
few duplicates, or none at all. There are N iterations
of loop, each iteration does a table lookup, most iterations do
a t_update as well, and those operations each do N comparisons.
The average length of the table (the number of elements) averages
only N/2, and (if there are few duplicates) the lookup will have to
traverse the entire list, so really in each iteration there will be only
N/2 comparisons instead of N, but in asymptotic analysis we ignore
the constant factors.
So far it seems like this is a quadratic-time algorithm, O(N^2). But
to compare Coq natural numbers for equality takes O(N) time as well:

Print eqb.

(* fix eqb (n m : nat) {struct n} : bool :=

match n with

| 0 => match m with 0 => true | S _ => false end

| S n

end *)

(* fix eqb (n m : nat) {struct n} : bool :=

match n with

| 0 => match m with 0 => true | S _ => false end

| S n

_{1}=> match m with 0 => false | S m_{1}=> eqb n_{1}m_{1}endend *)

Remember, nat is a unary representation, with a number of S constructors
proportional to the number being represented!

# Efficient Positive Numbers

We can do better; we*must*do better. In fact, Coq's integer type, called Z, is a binary representation (not unary), so that operations such as plus and leq take time linear in the number of bits, that is, logarithmic in the value of the numbers. Here we will explore how Z is built.

We start with positive numbers.

A positive number is either

- 1, that is, xH
- 0+2n, that is, xO n
- 1+2n, that is, xI n.

To interpret a positive number as a nat,

Fixpoint positive2nat (p: positive) : nat :=

match p with

| xI q ⇒ 1 + 2 × positive2nat q

| xO q ⇒ 0 + 2 × positive2nat q

| xH ⇒ 1

end.

Eval compute in positive2nat ten. (* = 10 : nat *)

match p with

| xI q ⇒ 1 + 2 × positive2nat q

| xO q ⇒ 0 + 2 × positive2nat q

| xH ⇒ 1

end.

Eval compute in positive2nat ten. (* = 10 : nat *)

We can read the binary representation of a positive number
as the

*backwards*sequence of xO (meaning 0) and xI/xH (1). Thus, ten is 1010 in binary.
Fixpoint print_in_binary (p: positive) : list nat :=

match p with

| xI q ⇒ print_in_binary q ++ [1]

| xO q ⇒print_in_binary q ++ [0]

| xH ⇒ [1]

end.

Eval compute in print_in_binary ten. (* = 1; 0; 1; 0 *)

match p with

| xI q ⇒ print_in_binary q ++ [1]

| xO q ⇒print_in_binary q ++ [0]

| xH ⇒ [1]

end.

Eval compute in print_in_binary ten. (* = 1; 0; 1; 0 *)

Another way to see the "binary representation" is to make up
postfix notation for xI and xO, as follows

Notation "p ~ 1" := (xI p)

(at level 7, left associativity, format "p '~' '1'").

Notation "p ~ 0" := (xO p)

(at level 7, left associativity, format "p '~' '0'").

Print ten. (* = xH~0~1~0 : positive *)

(at level 7, left associativity, format "p '~' '1'").

Notation "p ~ 0" := (xO p)

(at level 7, left associativity, format "p '~' '0'").

Print ten. (* = xH~0~1~0 : positive *)

Why are we using positive numbers anyway? Since the zero was
invented 2300 years ago by the Babylonians, it's sort of old-fashioned
to use number systems that start at 1.
The answer is that it's highly inconvenient to have number systems
with several different representations of the same number.
For one thing, we don't want to worry about 00110=110.
Then, when we extend this to the integers, with a "minus sign",
we don't have to worry about -0 = +0.
To find the successor of a binary number--that is to increment--
we work from low-order to high-order, until we hit a zero bit.

To add binary numbers, we work from low-order to high-order,
keeping track of the carry.

Fixpoint addc (carry: bool) (x y: positive) {struct x} : positive :=

match carry, x, y with

| false, p~1, q~1 ⇒ (addc true p q)~0

| false, p~1, q~0 ⇒ (addc false p q)~1

| false, p~1, xH ⇒ (succ p)~0

| false, p~0, q~1 ⇒ (addc false p q)~1

| false, p~0, q~0 ⇒ (addc false p q)~0

| false, p~0, xH ⇒ p~1

| false, xH, q~1 ⇒ (succ q)~0

| false, xH, q~0 ⇒ q~1

| false, xH, xH ⇒ xH~0

| true, p~1, q~1 ⇒ (addc true p q)~1

| true, p~1, q~0 ⇒ (addc true p q)~0

| true, p~1, xH ⇒ (succ p)~1

| true, p~0, q~1 ⇒ (addc true p q)~0

| true, p~0, q~0 ⇒ (addc false p q)~1

| true, p~0, xH ⇒ (succ p)~0

| true, xH, q~1 ⇒ (succ q)~1

| true, xH, q~0 ⇒ (succ q)~0

| true, xH, xH ⇒ xH~1

end.

Definition add (x y: positive) : positive := addc false x y.

match carry, x, y with

| false, p~1, q~1 ⇒ (addc true p q)~0

| false, p~1, q~0 ⇒ (addc false p q)~1

| false, p~1, xH ⇒ (succ p)~0

| false, p~0, q~1 ⇒ (addc false p q)~1

| false, p~0, q~0 ⇒ (addc false p q)~0

| false, p~0, xH ⇒ p~1

| false, xH, q~1 ⇒ (succ q)~0

| false, xH, q~0 ⇒ q~1

| false, xH, xH ⇒ xH~0

| true, p~1, q~1 ⇒ (addc true p q)~1

| true, p~1, q~0 ⇒ (addc true p q)~0

| true, p~1, xH ⇒ (succ p)~1

| true, p~0, q~1 ⇒ (addc true p q)~0

| true, p~0, q~0 ⇒ (addc false p q)~1

| true, p~0, xH ⇒ (succ p)~0

| true, xH, q~1 ⇒ (succ q)~1

| true, xH, q~0 ⇒ (succ q)~0

| true, xH, xH ⇒ xH~1

end.

Definition add (x y: positive) : positive := addc false x y.

Lemma succ_correct: ∀ p,

positive2nat (succ p) = S (positive2nat p).

Proof.

(* FILL IN HERE *) Admitted.

☐

positive2nat (succ p) = S (positive2nat p).

Proof.

(* FILL IN HERE *) Admitted.

☐

#### Exercise: 3 stars, standard (addc_correct)

You may use lia in this proof if you want, along with induction of course. But really, using lia is an anachronism in a sense: Coq's lia uses theorems about Z that are proved from theorems about Coq's standard-library positive that, in turn, rely on a theorem much like this one. So the authors of the Coq standard library had to do the associative-commutative rearrangement proofs "by hand." But really, here you can use lia without penalty.
Lemma addc_correct: ∀ (c: bool) (p q: positive),

positive2nat (addc c p q) =

(if c then 1 else 0) + positive2nat p + positive2nat q.

Proof.

(* FILL IN HERE *) Admitted.

Theorem add_correct: ∀ (p q: positive),

positive2nat (add p q) = positive2nat p + positive2nat q.

Proof.

intros.

unfold add.

apply addc_correct.

Qed.

☐

positive2nat (addc c p q) =

(if c then 1 else 0) + positive2nat p + positive2nat q.

Proof.

(* FILL IN HERE *) Admitted.

Theorem add_correct: ∀ (p q: positive),

positive2nat (add p q) = positive2nat p + positive2nat q.

Proof.

intros.

unfold add.

apply addc_correct.

Qed.

☐

Fixpoint compare x y {struct x}:=

match x, y with

| p~1, q~1 ⇒ compare p q

| p~1, q~0 ⇒ match compare p q with Lt ⇒ Lt | _ ⇒ Gt end

| p~1, xH ⇒ Gt

(* DELETE THIS CASE! Replace it with cases that actually work. *)

| _, _ ⇒ Lt

end.

Lemma positive2nat_pos:

∀ p, positive2nat p > 0.

Proof.

intros.

induction p; simpl; lia.

Qed.

Theorem compare_correct:

∀ x y,

match compare x y with

| Lt ⇒ positive2nat x < positive2nat y

| Eq ⇒ positive2nat x = positive2nat y

| Gt ⇒ positive2nat x > positive2nat y

end.

Proof.

induction x; destruct y; simpl.

(* FILL IN HERE *) Admitted.

☐

match x, y with

| p~1, q~1 ⇒ compare p q

| p~1, q~0 ⇒ match compare p q with Lt ⇒ Lt | _ ⇒ Gt end

| p~1, xH ⇒ Gt

(* DELETE THIS CASE! Replace it with cases that actually work. *)

| _, _ ⇒ Lt

end.

Lemma positive2nat_pos:

∀ p, positive2nat p > 0.

Proof.

intros.

induction p; simpl; lia.

Qed.

Theorem compare_correct:

∀ x y,

match compare x y with

| Lt ⇒ positive2nat x < positive2nat y

| Eq ⇒ positive2nat x = positive2nat y

| Gt ⇒ positive2nat x > positive2nat y

end.

Proof.

induction x; destruct y; simpl.

(* FILL IN HERE *) Admitted.

☐

## Coq's Integer Type, Z

Coq's integer type is constructed from positive numbers:
We can construct efficient (logN time) algorithms for operations
on Z: add, subtract, compare, and so on. These algorithms
call upon the efficient algorithms for positives.
We won't show these here, because in this chapter we now turn
to efficient maps over positive numbers.

These types, positive and Z, are part of the Coq standard library.
We can access them here, because we imported PArith and ZArith
at the top of the file.

Print positive. (* from the Coq standard library:

Inductive positive : Set :=

| xI : positive -> positive

| xO : positive -> positive

| xH : positive *)

Check Pos.compare. (* : positive -> positive -> comparison *)

Check Pos.add. (* : positive -> positive -> positive *)

Check Z.add. (* : Z -> Z -> Z *)

Inductive positive : Set :=

| xI : positive -> positive

| xO : positive -> positive

| xH : positive *)

Check Pos.compare. (* : positive -> positive -> comparison *)

Check Pos.add. (* : positive -> positive -> positive *)

Check Z.add. (* : Z -> Z -> Z *)

## From N×N×N to N×N×logN

Module RatherSlow.

Definition total_mapz (A: Type) := Z → A.

Definition empty {A:Type} (default: A) : total_mapz A := fun _ ⇒ default.

Definition update {A:Type} (m : total_mapz A)

(x : Z) (v : A) :=

fun x' ⇒ if Z.eqb x x' then v else m x'.

Fixpoint loop (input: list Z) (c: Z) (table: total_mapz bool) : Z :=

match input with

| nil ⇒ c

| a::al ⇒ if table a

then loop al (c+1) table

else loop al c (update table a true)

end.

Definition collisions (input: list Z) := loop input 0 (empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6]%Z = 1%Z.

Proof. reflexivity. Qed.

End RatherSlow.

Definition total_mapz (A: Type) := Z → A.

Definition empty {A:Type} (default: A) : total_mapz A := fun _ ⇒ default.

Definition update {A:Type} (m : total_mapz A)

(x : Z) (v : A) :=

fun x' ⇒ if Z.eqb x x' then v else m x'.

Fixpoint loop (input: list Z) (c: Z) (table: total_mapz bool) : Z :=

match input with

| nil ⇒ c

| a::al ⇒ if table a

then loop al (c+1) table

else loop al c (update table a true)

end.

Definition collisions (input: list Z) := loop input 0 (empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6]%Z = 1%Z.

Proof. reflexivity. Qed.

End RatherSlow.

## From N×N×logN to N×logN×logN

# Tries: Efficient Lookup Tables on Positive Binary Numbers

*any*totally ordered type to any other type. But when the type of keys is known specifically to be (small-to-medium size) integers, then we can use a more specialized representation.

*trie*is a tree in which the edges are labeled with letters from an alphabet, and you look up a word by following edges labeled by successive letters of the word. In fact, a trie is a special case of a Deterministic Finite Automaton (DFA) that happens to be a tree rather than a more general graph.

*binary trie*is a trie in which the alphabet is just {0,1}. The "word" is a sequence of bits, that is, a binary number. To look up the "word" 10001, use 0 as a signal to "go left", and 1 as a signal to "go right."

Print positive.

(* Inductive positive : Set :=

xI : positive -> positive

| xO : positive -> positive

| xH : positive *)

Goal 10%positive = xO (xI (xO xH)).

Proof. reflexivity. Qed.

(* Inductive positive : Set :=

xI : positive -> positive

| xO : positive -> positive

| xH : positive *)

Goal 10%positive = xO (xI (xO xH)).

Proof. reflexivity. Qed.

Given a positive number such as ten, we will go left to right in
the xO/xI constructors (which is from the low-order bit to the high-order bit),
using xO as a signal to go left, xI as a signal to go right,
and xH as a signal to stop.

Inductive trie (A : Type) :=

| Leaf : trie A

| Node : trie A → A → trie A → trie A.

Arguments Leaf {A}.

Arguments Node {A} _ _ _.

Definition trie_table (A: Type) : Type := (A × trie A)%type.

Definition empty {A: Type} (default: A) : trie_table A :=

(default, Leaf).

Fixpoint look {A: Type} (default: A) (i: positive) (m: trie A): A :=

match m with

| Leaf ⇒ default

| Node l x r ⇒

match i with

| xH ⇒ x

| xO i' ⇒ look default i' l

| xI i' ⇒ look default i' r

end

end.

Definition lookup {A: Type} (i: positive) (t: trie_table A) : A :=

look (fst t) i (snd t).

Fixpoint ins {A: Type} default (i: positive) (a: A) (m: trie A): trie A :=

match m with

| Leaf ⇒

match i with

| xH ⇒ Node Leaf a Leaf

| xO i' ⇒ Node (ins default i' a Leaf) default Leaf

| xI i' ⇒ Node Leaf default (ins default i' a Leaf)

end

| Node l o r ⇒

match i with

| xH ⇒ Node l a r

| xO i' ⇒ Node (ins default i' a l) o r

| xI i' ⇒ Node l o (ins default i' a r)

end

end.

Definition insert {A: Type} (i: positive) (a: A) (t: trie_table A)

: trie_table A :=

(fst t, ins (fst t) i a (snd t)).

Definition three_ten : trie_table bool :=

insert 3 true (insert 10 true (empty false)).

Eval compute in three_ten.

(* = (false,

Node (Node Leaf false (Node (Node Leaf true Leaf) false Leaf))

false

(Node Leaf true Leaf))

: trie_table bool *)

Eval compute in

map (fun i ⇒ lookup i three_ten) [3;1;4;1;5]%positive.

(* = true; false; false; false; false : list bool *)

| Leaf : trie A

| Node : trie A → A → trie A → trie A.

Arguments Leaf {A}.

Arguments Node {A} _ _ _.

Definition trie_table (A: Type) : Type := (A × trie A)%type.

Definition empty {A: Type} (default: A) : trie_table A :=

(default, Leaf).

Fixpoint look {A: Type} (default: A) (i: positive) (m: trie A): A :=

match m with

| Leaf ⇒ default

| Node l x r ⇒

match i with

| xH ⇒ x

| xO i' ⇒ look default i' l

| xI i' ⇒ look default i' r

end

end.

Definition lookup {A: Type} (i: positive) (t: trie_table A) : A :=

look (fst t) i (snd t).

Fixpoint ins {A: Type} default (i: positive) (a: A) (m: trie A): trie A :=

match m with

| Leaf ⇒

match i with

| xH ⇒ Node Leaf a Leaf

| xO i' ⇒ Node (ins default i' a Leaf) default Leaf

| xI i' ⇒ Node Leaf default (ins default i' a Leaf)

end

| Node l o r ⇒

match i with

| xH ⇒ Node l a r

| xO i' ⇒ Node (ins default i' a l) o r

| xI i' ⇒ Node l o (ins default i' a r)

end

end.

Definition insert {A: Type} (i: positive) (a: A) (t: trie_table A)

: trie_table A :=

(fst t, ins (fst t) i a (snd t)).

Definition three_ten : trie_table bool :=

insert 3 true (insert 10 true (empty false)).

Eval compute in three_ten.

(* = (false,

Node (Node Leaf false (Node (Node Leaf true Leaf) false Leaf))

false

(Node Leaf true Leaf))

: trie_table bool *)

Eval compute in

map (fun i ⇒ lookup i three_ten) [3;1;4;1;5]%positive.

(* = true; false; false; false; false : list bool *)

Module FastEnough.

Fixpoint loop (input: list positive) (c: nat) (table: trie_table bool) : nat :=

match input with

| nil ⇒ c

| a::al ⇒ if lookup a table

then loop al (1+c) table

else loop al c (insert a true table)

end.

Definition collisions (input: list positive) := loop input 0 (empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6]%positive = 1.

Proof. reflexivity. Qed.

End FastEnough.

Fixpoint loop (input: list positive) (c: nat) (table: trie_table bool) : nat :=

match input with

| nil ⇒ c

| a::al ⇒ if lookup a table

then loop al (1+c) table

else loop al c (insert a true table)

end.

Definition collisions (input: list positive) := loop input 0 (empty false).

Example collisions_pi: collisions [3;1;4;1;5;9;2;6]%positive = 1.

Proof. reflexivity. Qed.

End FastEnough.

This program takes O(N log N) time: the loop executes N
iterations, the lookup takes log N time, the insert takes log N time.
One might worry about 1+c computed in the natural numbers (unary
representation), but this evaluates in one step to S c, which
takes constant time, no matter how long c is. In "real life", one
might be advised to use Z instead of nat for the c variables,
in which case, 1+c takes worst-case log N, and average-case
constant time.

#### Exercise: 2 stars, standard (successor_of_Z_constant_time)

Explain why the average-case time for successor of a binary integer, with carry, is constant time. Assume that the input integer is random (uniform distribution from 1 to N), or assume that we are iterating successor starting at 1, so that each number from 1 to N is touched exactly once -- whichever way you like.
(* explain here

*)

(* Do not modify the following line: *)

Definition manual_grade_for_successor_of_Z_constant_time : option (nat×string) := None.

☐

*)

(* Do not modify the following line: *)

Definition manual_grade_for_successor_of_Z_constant_time : option (nat×string) := None.

☐

# Proving the Correctness of Trie Tables

## Lemmas About the Relation Between lookup and insert

#### Exercise: 1 star, standard (look_leaf)

Lemma look_ins_other: ∀ {A} a j k (v:A) t,

j ≠ k → look a j (ins a k v t) = look a j t.

(* FILL IN HERE *) Admitted.

☐

j ≠ k → look a j (ins a k v t) = look a j t.

(* FILL IN HERE *) Admitted.

☐

## Bijection Between positive and nat.

Definition nat2pos (n: nat) : positive := Pos.of_succ_nat n.

Definition pos2nat (n: positive) : nat := pred (Pos.to_nat n).

Lemma pos2nat2pos: ∀ p, nat2pos (pos2nat p) = p.

Proof. (* You don't need to read this proof! *)

intro. unfold nat2pos, pos2nat.

rewrite <- (Pos2Nat.id p) at 2.

destruct (Pos.to_nat p) eqn:?.

pose proof (Pos2Nat.is_pos p). lia.

rewrite <- Pos.of_nat_succ.

reflexivity.

Qed.

Lemma nat2pos2nat: ∀ i, pos2nat (nat2pos i) = i.

Proof. (* You don't need to read this proof! *)

intro. unfold nat2pos, pos2nat.

rewrite SuccNat2Pos.id_succ.

reflexivity.

Qed.

Definition pos2nat (n: positive) : nat := pred (Pos.to_nat n).

Lemma pos2nat2pos: ∀ p, nat2pos (pos2nat p) = p.

Proof. (* You don't need to read this proof! *)

intro. unfold nat2pos, pos2nat.

rewrite <- (Pos2Nat.id p) at 2.

destruct (Pos.to_nat p) eqn:?.

pose proof (Pos2Nat.is_pos p). lia.

rewrite <- Pos.of_nat_succ.

reflexivity.

Qed.

Lemma nat2pos2nat: ∀ i, pos2nat (nat2pos i) = i.

Proof. (* You don't need to read this proof! *)

intro. unfold nat2pos, pos2nat.

rewrite SuccNat2Pos.id_succ.

reflexivity.

Qed.

Now, use those two lemmas to prove that it's really a bijection!

#### Exercise: 2 stars, standard (pos2nat_bijective)

Lemma pos2nat_injective: ∀ p q, pos2nat p = pos2nat q → p=q.

(* FILL IN HERE *) Admitted.

Lemma nat2pos_injective: ∀ i j, nat2pos i = nat2pos j → i=j.

(* FILL IN HERE *) Admitted.

☐

(* FILL IN HERE *) Admitted.

Lemma nat2pos_injective: ∀ i j, nat2pos i = nat2pos j → i=j.

(* FILL IN HERE *) Admitted.

☐

## Proving That Tries are a "Table" ADT.

Definition is_trie {A: Type} (t: trie_table A) : Prop

(* REPLACE THIS LINE WITH ":= _your_definition_ ." *). Admitted.

(* REPLACE THIS LINE WITH ":= _your_definition_ ." *). Admitted.

Abstraction relation. This is what we mean by, "what you get is
what you get." That is, the abstraction of a trie_table is the
total function, from naturals to A values, that you get by
running the lookup function. Based on this abstraction
relation, it'll be trivial to prove lookup_relate. But
insert_relate will NOT be trivial.

Definition abstract {A: Type} (t: trie_table A) (n: nat) : A :=

lookup (nat2pos n) t.

Definition Abs {A: Type} (t: trie_table A) (m: total_map A) :=

abstract t = m.

lookup (nat2pos n) t.

Definition Abs {A: Type} (t: trie_table A) (m: total_map A) :=

abstract t = m.

#### Exercise: 2 stars, standard (is_trie)

If you picked a*really simple*representation invariant, these should be easy. Later, if you need to change the representation invariant in order to get the _relate proofs to work, then you'll need to fix these proofs.

Theorem empty_is_trie: ∀ {A} (default: A), is_trie (empty default).

(* FILL IN HERE *) Admitted.

Theorem insert_is_trie: ∀ {A} i x (t: trie_table A),

is_trie t → is_trie (insert i x t).

(* FILL IN HERE *) Admitted.

☐

(* FILL IN HERE *) Admitted.

Theorem insert_is_trie: ∀ {A} i x (t: trie_table A),

is_trie t → is_trie (insert i x t).

(* FILL IN HERE *) Admitted.

☐

#### Exercise: 2 stars, standard (empty_relate)

Just unfold a bunch of definitions, use extensionality, and use one of the lemmas you proved above, in the section "Lemmas about the relation between lookup and insert."
Theorem empty_relate: ∀ {A} (default: A),

Abs (empty default) (t_empty default).

Proof.

(* FILL IN HERE *) Admitted.

☐

Abs (empty default) (t_empty default).

Proof.

(* FILL IN HERE *) Admitted.

☐

#### Exercise: 2 stars, standard (lookup_relate)

Given the abstraction relation we've chosen, this one should be really simple.
Theorem lookup_relate: ∀ {A} i (t: trie_table A) m,

is_trie t → Abs t m → lookup i t = m (pos2nat i).

(* FILL IN HERE *) Admitted.

☐

is_trie t → Abs t m → lookup i t = m (pos2nat i).

(* FILL IN HERE *) Admitted.

☐

#### Exercise: 3 stars, standard (insert_relate)

Given the abstraction relation we've chosen, this one should NOT be simple. However, you've already done the heavy lifting, with the lemmas look_ins_same and look_ins_other. You will not need induction here. Instead, unfold a bunch of things, use extensionality, and get to a case analysis on whether pos2nat k =? pos2nat j. To handle that case analysis, use bdestruct. You may also need pos2nat_injective.
Theorem insert_relate: ∀ {A} k (v: A) t cts,

is_trie t →

Abs t cts →

Abs (insert k v t) (t_update cts (pos2nat k) v).

(* FILL IN HERE *) Admitted.

☐

is_trie t →

Abs t cts →

Abs (insert k v t) (t_update cts (pos2nat k) v).

(* FILL IN HERE *) Admitted.

☐

Example Abs_three_ten:

Abs

(insert 3 true (insert 10 true (empty false)))

(t_update (t_update (t_empty false) (pos2nat 10) true) (pos2nat 3) true).

Proof.

try (apply insert_relate; [hnf; auto | ]).

try (apply insert_relate; [hnf; auto | ]).

try (apply empty_relate).

(* Change this to Qed once you have is_trie defined and working. *)

(* FILL IN HERE *) Admitted.

Abs

(insert 3 true (insert 10 true (empty false)))

(t_update (t_update (t_empty false) (pos2nat 10) true) (pos2nat 3) true).

Proof.

try (apply insert_relate; [hnf; auto | ]).

try (apply insert_relate; [hnf; auto | ]).

try (apply empty_relate).

(* Change this to Qed once you have is_trie defined and working. *)

(* FILL IN HERE *) Admitted.

# Conclusion

Efficient functional maps with (positive) integer keys are one of the most important data structures in functional programming. They are used for symbol tables in compilers and static analyzers; to represent directed graphs (the mapping from node-ID to edge-list); and (in general) anywhere that an imperative algorithm uses an array or*requires*a mutable pointer.

*tries*on positive numbers are very important in Coq programming. They were introduced by Xavier Leroy and Sandrine Blazy in the CompCert compiler (2006), and are now available in the Coq standard library as the PositiveMap module, which implements the FMaps interface. The core implementation of PositiveMap is just as shown in this chapter, but FMaps uses different names for the functions insert and lookup, and also provides several other operations on maps.

(* 2021-08-11 15:15 *)