Linear Structures and Hash Tables
A dynamic set S usually supports the following "dictionary operations":
There are two basic ways to represent relations in computer:
For a linear data structure, the order among elements is implicitly represented in an array, and explicitly in a linked list.
Between the two, an array is more efficient in random access of elements, while a linked list is more efficient in insertions and deletions.
A linked list can be implemented in two arrays, one of which keeps the elements themselves, and the other keeps the index of the successor for each element. Similarly, multiple arrays can be used to represent an array of objects with multiple fields.
To turn a linear structure into a circular structure, in array we can let the index go from A.length back to 1, and in linked list we can let the last element to point to the first element.
To move in both directions in a linked list, an additional link can be used.
Both stack and queue can be implemented by array and linked list. For stack, the implementation of the operations (called "push" and "pop") is relatively simple. For queue, one additional pointer is needed, because the insert and delete operations (sometimes called "enqueue" and "dequeue") work on different ends ("front" and "rear") of the structure. For both data structures, an array implementation is more efficient, but a linked list implementation is more flexible. [Why?]
Insert/Delete Operations on stack and queue have running time Θ(1).
Among the three operations, an insertion and deletion often require a search, therefore search is the major factor in efficiency analysis. Since search is basically a mapping from a key to an index in the table, the intuition behind hash table is to directly build such a mapping, without comparisons with the elements in the table.
The design of a hash table usually consists of two major tasks: (1) to define a hash function that calculates index from key, (2) to handle "collision", which is the situation where multiple keys are mapped into the same index.
Typically, the size of a hash table is proportional to the number of keys actually stored, while being much smaller than the range of possible keys.
A good hash function satisfies (approximately) the assumption that each key is equally likely to hash to any of the m index values, independently of the where any other key has hashed to. Often, a hash function works in two steps: (1) to convert a key into an integer, (2) to calculate the index from the integer. Therefore, hash function discussion often assumes that the function is a mapping from an integer (key) to an integer (index).
The most common hash function is to take the reminder of the key divided by the size of the hash table, that is, h(k) = k mod m. A more complicated version is h(k) = f(k) mod m, where f(k) does additional calculation to reduce collision, and the reminder operation makes the function value to cover the whole table. A similar approach is h(k) = floor(g(k) * m), where g(k) maps k into the range of [0, 1).
Common collision handling methods can be divided into two types, open addressing, where all elements are stored in the table itself, and separate chaining, where "overflow" elements are stored at the outside of the table.
Separate chaining requires additional space, though is conceptually simpler than open addressing.In open addressing, the hash function generates a probe sequence to tell the element where to go if the slot indicated by the hash function is already occupied. Such a sequence can depend on the key (such as double hashing) or follow a fixed pattern (such as linear and quadratic probing). In either way, element comparisons are necessary, as collision can happen in multiple places. After deletion, the released space still needs to be marked for the following search to pass through.
Under the assumption that each possible probing sequence is equally probable, and that the load factor of the hash table α (number of elements / table size) is less than 1, there are the following conclusions: