#### The problem

We want to use a very large array for some computations. When created, all the elements of this array have to be initialized to some value. We'll only use a few values from the array^{1}, so we don't want the runtime of the algorithm to be dominated by the initialization time.

In other words, we want to create and access an initialized array in constant time^{2}.

How can this be done ? (Hint: we may use extra memory)

#### A trivial non-solution

One wrong solution that often comes to mind is:

Keep another array, where index `i`

holds some known value if and only if our data array was initialized at index `i`

.

This won't work, because when we create an array, it is full of random data (garbage that was in the memory at the time of creation). This random data can contain any "special" value we might choose to mark initialized indices, and the idea breaks down.

#### Solution

For a n-element array (`vec`

), we'll use two additional n-element arrays and a pointer^{3}. The arrays are called `from`

and `to`

and the pointer is called `top`

. `init_val`

is the value we want to initialize the array with.

- When an element
`i`

from`vec`

is accessed, we'll know whether it has been initialized if`from[i] < top and to[from[i]] = i`

. If this condition is satisfied, we return`vec[i]`

. Otherwise, we return`init_val`

. - Initially,
`top`

is 0 - The array element
`i`

is first accessed by the code:

from[i] = top to[top] = i vec[i] = init_val top++

#### Understanding the algorithm

Let's assume we want an array with size 10. This is the initial state of the array and helpers (`X`

denotes a memory location with arbitrary contents):

vec: X X X X X X X X X X from: X X X X X X X X X X to: X X X X X X X X X X top: 0

Since `from`

and `to`

contain unsigned values, the condition `from[i] < top`

can never be true while `top = 0`

. So all read accesses at this point will result in `init_val`

.

Now suppose we write into the vector. Let's say `vec[4] <- 3`

. According to the algorithm, the next state will be:

vec: X X X X 3 X X X X X from: X X X X 0 X X X X X to: 4 X X X X X X X X X top: 1

Now accesses to `i = 4`

will satisfy the conditions `from[4] = 0 < 1`

and `to[from[4]] = 4`

, so the value of `vec[4]`

can be returned. Accesses to other indices will fail, because the only `i`

that will pass the `from[i] < top`

is an `i`

for which `from[i] = 0`

, but only `i = 4`

will also fulfill the second condition `to[from[i]] = i`

.

Let's work through another step. Let's say we write `vec[9] <- 6`

. The next state will be:

vec: X X X X 3 X X X X 6 from: X X X X 0 X X X X 1 to: 4 9 X X X X X X X X top: 2

Again, the accesses to initialized indices, `i = 4, 9`

will satisfy the conditions. Accesses to other indices will fail. Suppose for example an access to `i = 7`

. Is `from[7] < 2`

? Let's assume that by chance it is. It must be either 0 or 1 then. But for both, `to`

points to the only really initialized fields, which are either 4 or 9, so the second condition will not be satisfied.

I hope it's now clear how `to`

, `from`

and `top`

interact to ensure that only initialized values will be actually returned. They kind-of close a loop - `i`

points to `from`

, `from`

points to `to`

and `to`

points back to `i`

. `top`

makes sure that the location in `to`

was already initialized by the algorithm. Since the relevant locations in `from`

and `to`

are always in a controlled state, this algorithm can never be fooled by random values that were just lying around in the memory.

#### A C++ implementation

C++ is a convenient language to implement this algorith in, because:

- It can actually create un-initialized arrays in almost constant time with
`new`

, unlike the higher-level languages like Python, in which arrays (lists) are initialized. - It can encapsulate the whole data structure in a convenient class, and hide all the helper-data manipulation that goes on behind the scenes.

So here's the class:

#include#include using namespace std; class InitializedVector { public: InitializedVector(size_t len_, int init_val_ = 0) { vec = new int[len_]; from = new size_t[len_]; to = new size_t[len_]; top = 0; init_val = init_val_; len = len_; } int& operator[](size_t n) { if (from[n] < top && to[from[n]] == n) return vec[n]; else { from[n] = top; to[top] = n; vec[n] = init_val; top++; return vec[n]; } } ~InitializedVector() { delete[] vec; delete[] from; delete[] to; } private: int* vec; size_t* from; size_t* to; size_t top; int init_val; size_t len; };

And some test code to show that it works:

```
int main()
{
cout << "Hello there" << endl << endl;
InitializedVector iv(10, 2222);
cout << iv[4] << endl;
cout << iv[9] << endl;
iv[4] = 142;
cout << iv[4] << endl;
cout << iv[9] << endl;
return 0;
}
```

In principle, we could make read accesses a bit more efficient, by skipping the complex initialization and just returning `init_val`

. In this C++ implementation this isn't possible, however, because when you overload `operator []`

, you can't discriminate between read and write accesses to the element.

^{1} Such a data structure is usually called sparse.

^{2} Dynamic allocation routines (`malloc`

and its progeny) don't run in constant time in the worst case (when memory is fragmented and the requested block is large). In practice, however, we can assume it's very close to constant time for most real use-cases.

^{3} I've picked this algorithm in John Bentley's *Programming Pearls, 2nd Ed.*. He attributes it to Aho, Hopcroft and Ullman's *Design and Analysis of Computer Algorithms*. After some Googling I've also found references to it in James Storer's *Data structures and algorithms*.

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