The latest release is December 2012. The header file reducible_queue.h is the whole library. The source file reducible_queue.cc is a test program. Both are MIT-licensed.

`reduq`

provides a C++ template class `reduq::reducible_queue<Reducer>`

with the following operations. All of them run in worst-case constant time assuming that `Reducer`

and the memory allocator are well-behaved.

`reducible_queue()`

: constructs a`reducible_queue`

`bool empty()`

: returns true if and only if there are no elements in the queue`size_t size()`

: returns the number of elements in the queue`const Reducer::Type& front()`

: returns a`const`

reference to the element in the queue least recently inserted`const Reducer::Type& back()`

: returns a`const`

reference to the element in the queue most recently inserted`void push(const Reducer::Type& x)`

: inserts`x`

into the queue`void pop()`

: removes from the queue the element least recently inserted`Reducer::Type reduce()`

: returns the “product” by`Reducer::reduce`

of the elements in the queue from least to most recently inserted

The template parameter `Reducer`

must have three members.

`Type`

: the element type`Type reduce(const Type& x, const Type& y)`

: returns the “product” of`x`

and`y`

; must be associative`Type id()`

: returns a right identity for`reduce`

`reduq`

defines the following reducer templates, whose element types are all `T`

.

`reduq::BitwiseAnd<T>`

`reduq::BitwiseOr<T>`

`reduq::BitwiseXor<T>`

`reduq::IntegerMax<T>`

`reduq::IntegerMin<T>`

`reduq::Product<T>`

`reduq::RealMax<T>`

`reduq::RealMin<T>`

`reduq::Sum<T>`

Although the implementation uses linked lists, I describe the algorithm using two infinite arrays. At a high level, it interleaves continual back-to-front suffix scans with pushes and pops.

```
## public ##
f = empty():
f = begin >= end
push(x):
A[end] = x
end = end + 1
current_batch = reduce(current_batch, x)
step()
# precondition: not empty()
x = pop():
x = A[begin]
begin = begin + 1
step()
y = reduce():
if empty():
y = id
else if begin >= j:
y = reduce(B[begin], current_batch)
else:
y = reduce(B[begin], previous_batch, current_batch)
## private ##
step():
if j <= begin:
j = end - 1
B[j] = A[j]
{previous_k = current_k}
previous_batch = current_batch
{current_k = end}
current_batch = id
else:
j = j - 1
B[j] = reduce(A[j], B[j + 1])
begin = 0
end = 0
j = -1
A = [...]
B = [...]
{previous_k = 0}
{current_k = 0}
previous_batch = id
current_batch = id
```

Statements necessary only for the analysis are surrounded by {braces}. It is clear that `empty`

and `push`

and `pop`

are correct and that all operations run in constant time. The following invariant implies the correctness of `reduce`

.

```
(0) 0 <= begin <= end
(1) previous_k <= current_k <= end
(2) for all i in {begin, ..., min(previous_k, j) - 1},
B[i] == reduce(A[i ... previous_k - 1])
(3) for all i in { j, ..., current_k - 1},
B[i] == reduce(A[i ... current_k - 1])
(4) previous_batch == reduce(A[previous_k ... current_k - 1])
(5) current_batch == reduce(A[ current_k ... end - 1])
(6) (j - begin) + (end - current_k) < current_k - begin
(7) j - previous_k <= previous_k - begin
(8) min(begin, end - 1) <= j
```

The intuition for clauses `(6-7)`

is that there are two intervals, `previous_k ... j - 1`

and `current_k ... end - 1`

, that `begin`

must not reach if `reduce()`

is to be correct. Clause `(7)`

ensures that `begin`

does not reach the first, and clause `(6)`

ensures that `begin`

does not reach the second. Accordingly, `reduce`

is correct.

Now we establish the invariant. `empty`

and `reduce`

have no side effects and thus maintain the invariant. `push`

, just prior to calling `step`

, ensures clauses `(0-5)`

; `(6')`

, a weakening of `(6)`

; `(7)`

; `(8')`

, a weakening of `(8)`

given `(0)`

; and a new clause, `(9)`

.

```
(6') (j - begin) + (end - 1 - current_k) < current_k - begin
(8') begin - 1 <= j
(9) 0 <= end - 1
```

In turn, `step`

ensures `(0-8)`

by starting the next scan or subtracting one from `j`

.

`pop`

, just prior to calling `step`

, ensures clauses `(0-6)`

; `(7')`

, a weakening of `(7)`

; `(8')`

; and `(9)`

.

`(7') j - previous_k <= previous_k - (begin - 1)`

In turn, `step`

again ensures `(0-8)`

.

A classic data structures interview question asks for a queue with an efficient find-min operation. One commonly proposed solution achieves amortized time bounds via two stacks. `reduq`

achieves worst-case time bounds without resorting to the complexity of purely functional data structures.

© 2012–2013 David Eisenstat