Why use Hashing in Competitive Programming?

What is Hashing?

Hashing is a fundamental data structure and technique used in competitive programming to efficiently store and retrieve elements based on their “keys.”...

Why use Hashing in Competitive Programming?

Competitive programming often involves dealing with large amounts of data and solving problems under tight time constraints. Hashing comes in handy in several situations:...

Advantages of Hashing

Fast lookup and insertion: O(1) time complexity in the average case.Memory efficiency: Can be more efficient than other data structures for certain types of data.Simple to implement: Most languages provide built-in hash table libraries....

Disadvantages of Hashing

Collisions: Different elements can have the same hash code, requiring collision handling techniques (e.g., chaining, double hashing) which can impact performance.Not a good fit for all problems: Not suitable for problems requiring frequent updates or deletions.Choosing the right hash function: Important to select a good hash function to minimize collisions....

Common Hash Functions and Collision Handling Techniques

Several hash functions exist, each with its own strengths and weaknesses. Some popular choices include:...

Use Cases of Hashing in Competitive Programming

In competitive programming, hashing is a versatile technique for organizing and retrieving data. Because it performs lightning-quick lookups and data manipulation efficiently, it is a great tool for tackling algorithms. Here are some of its use cases/applications of hashing:...

Hashing in Competitive Programming for C++ Programmers:

Initially, C++ had set and map, which are tree data structures which offers the worst-case time complexity of O(logN) to find any item. With C++11, hash set and hash map as unordered_set and unordered_map was introduced which offer an average case of O(1) but worst case of O(N) to insert, delete or retrieve any item. The average case is O(1) because it is assumed that each item only runs into O(1) collisions on average. For random input, they offer O(1) but it is possible to design inputs to make large number of collisions causing the time complexity of every operation to be O(N). This is quite common on various Competitive Programming platforms where people hack solutions of others who have used unordered maps with standard hash function assuming that it takes O(1) to perform every operation....

Hashing in Competitive Programming for Java Programmers:

Java has HashMaps with a hashing function which is applied to the key to calculate the index of the bucket in order to store and retrieve any key-value pair. The Capacity of HashMap is the number of buckets in it. Initially, the capacity of HashMap is 16. As the number of elements in the HashMap increases, the capacity gets increased. This is handled with the help of Load Factor. The load factor is the measure that decides when to increase the capacity of the Map. The default load factor is 75% of the capacity....

Hashing in Competitive Programming for Python Programmers:

Python offers dictionaries to store (key, value) pairs. A hash table is a data structure that stores a set of elements in an array of large size, defining the position of the element as its hash taken modulo the size of the array. If hashes of several elements give the same cell in the hash table, the hash table tries to take another cell according to some rule, which depends on the particular implementation of the table. Usually, if the hash f(x) leads to a collision, then it checks the next hash f(f(x)), then f(f(f(x))) and so on, until an empty cell is found. The function f(x) is often a linear transformation (a*x+b)%size, where size is the size of the array, and a and b are relatively prime with it....

Practice Problems on Hashing for Competitive Programming:

Easy Level Problems on Hashing:...