Q1: What are the Radix Sort algorithm and a recursive algorithm?

Ans: By comparing the numbers, the radix sort places the element in order. It is an integral linear sorting method.

Recursive algorithms are a way to resolve a complex problem by dividing a problem into smaller sub-problems until you can solve the problem easily. It usually involves a calling function.

Q2: What is Divide and Conquer algorithms? Describe how they work. Can you offer any frequent instances of the sorts of problems your method could address?

Ans: Divide and Conquer algorithms are a model of many key phases in the resolution of issues. First of all, we separate the problem into smaller parts and concentrate on each solution. After all the components have been addressed, we take all the resultant smaller solutions and blend them into a complete, one integrated solution.

This may be done recursively; i.e. every "sub-problem" can, when required, be broken into even smaller sections. The problem is divided repetitively until every single problem is small enough to be comparatively easy to solve.

Some typical examples of issues that are good for this technique include binary search, algorithms for sorting (e.g., Sort merge, Quicksort), computer-complex math operations optimization (Exponentiation, FFT, Street algorithm), and more.

Q3: What is the Complexity of Algorithm?

Ans: The algorithm complexity can be classified as effective an algorithm as alternative algorithms. It focuses on how the time required to run the data set rises. The algorithm is crucial for computing complexity.

It is highly suited for the classification of algorithms based on the relative time or space required and the development of time/space requirement is specified following the input size.

Time complexity

Time complexity is a program running time, depending on the input size.

Space complexity

The complexities of space evaluate algorithms based on the amount of space an algorithm uses. In the early days of computers, space complexity analysis was essential (when storage space on the computer was limited). The problem of space nowadays is unusual because of the large amount of space on the computer.

We do the following sorts of complexity analysis

Worst-case: f(n)

The maximum number of steps done in any case of size n is specified.

Best-case: f(n)

The minimal number of steps done in each instance of size n is specified.

Average-case: f(n)

The average number of steps done for any case of size n is determined.

Q4: Explain how encryption algorithm works?

Ans: Encryption is the process by which plaintext is converted into the format of a secret code known as "chip text." The method employs for the conversion of the text a string of bits called "keys." The bigger the key, the higher the number of possible ciphertext patterns. Most of the encryption algorithms employ fixed input block codes that are around 64 to 128 bits long and some are streaming.

Q5: What is a Hash Table? How can we locate all anagrams in a dictionary using this structure?

Ans: A hash table is an arbitrary key storage data structure. The hash table consists of a Hash function index in an array. Elements are stored with indexes. By employing a hash function, we assign every potential item to a bucket. You may give multiple keys to the same bucket so all key pairs and value pairs are kept in their buckets in lists. The correct hacking function has a huge performance effect.

We must group all the words with the same set of letters in them, to locate all anagrams in a dictionary. If we thus map words into strings that reflect their sorted letters, we could arrange them into lists using their sorted letters.

The hash table has lists of strings. For each word, we add it to the appropriate key list or build and add a new list.