Different Types Data Structure Algorithms Explained
Learn different types of data structure algorithms with its implementations and applications in real world programming. We have listed and explained different types of algorithms used in data structures.
What is a Data Structure?
A Data Structure can be viewed as a systematic way to organise data so that it can be used efficiently. The choice of proper data structure can greatly affect the efficiency of our program. It is a logical way of storing data on your computer which also defines the retrieval process.
What is an Algorithm?
An Algorithm is a procedure having well-defined steps for solving a particular problem. The data stored in data structures is manipulated by using different algorithms, so the study of data structures includes the study of algorithms.
Must Read: Get More Data on Algorithms Here
Let us now have a look at different types of data structure algorithms that are extensively used to solve different computational problems.
A Greedy Algorithm works by taking a decision that appears the best at the moment, without thinking about the future. This algorithm follows the problem-solving heuristic which means that a local optimum is chosen at every step in the hope of getting a global optimum. The decision once taken in never reconsidered. It is not necessary that a greedy algorithm will always produce an optimal solution.
Finding a global optimum using Greedy Algorithm may require numerous steps. Greedy algorithms are used to solve combinatorial problems in mathematical optimisation. These algorithms produce good solutions for some mathematical problems, but not for every problem.
There are different variations to a Greedy Algorithms such as:
- Pure Greedy Algorithms
- Relaxed Greedy Algorithms
- Orthogonal Greedy Algorithms
The different types of Greedy Algorithm Implementations are as follows:
- Djikstra’s Algorithm for Single Source Shortest Path
- Kruskal’s Algorithm for Minimum Spanning Tree
- Prim’s Algorithm for Minimum Spanning Tree
- Huffman’s Algorithm
- Network Routing
Backtracking Algorithms are used in constraint satisfaction problems which are normally computational problems.This algorithm is a trial and error process. In some problems, we are given several options. One of these options might lead to the optimum solution.
We take any one of these several options and try to see if we get the optimum solution. If the optimum solution is not obtained, the action is undone and another option is selected. This option is again tried and tested. The above steps are repeated or retraced if we do not reach the solution.
The Backtracking Algorithm is, therefore, a meta – heuristic. It does not guarantee to find solutions to a given problem in a bounded amount of time.
The different types of Backtracking Algorithm Implementations are as follows:
- N-Queens Problem
- Sudoku Game
- Knapsack Problem
- Peg Solitaire
Divide and Conquer
A Divide and Conquer algorithm is a type of Multi – Branched Recursion. This algorithm solves a problem by dividing the original problem into smaller chunks which are similar subproblems. The solutions of these smaller subproblems are later combined to get the solution of the given original problem.
The algorithm works by breaking down the original problem recursively. The divide and conquer algorithms, therefore, are based on Recursion methods. This algorithm has led to the discovery of many algorithms such as Merge Sorting, Quick Sorting, Karatsuba Algorithm and much more.
The different types of Divide and Conquer Algorithm Implementations are as follows:
In randomised algorithms, random numbers are used to make some decisions. These randomised algorithms are approximated using a pseudo – random number generator. The running time of these algorithms depends on the input as well as the random numbers that are generated.
The algorithm stops its execution if the generated random number is an optimum solution. The running time may vary for the same input data. The two major algorithms are Monte Carlo Algorithm and Las Vegas Algorithm.
The different types of Randomized Algorithm Implementations are as follows:
- Quick sort
- Closest Pair Problems
Brute Force Algorithm
Brute Force Algorithm is also known as Exhaustive Search and Generate and Test Technique. Brute force algorithm is a general problem solving and one of the easiest data structure algorithms. It is the simplest way of finding the solution to a given problem. Brute – Force basically means that you will have to go through all the possible solutions one after another until you find the optimum solution. The brute force and backtracking algorithm are completely different.
The Brute – Force Algorithm is one of the simplest algorithms but the time that is taken by the algorithm to get an optimum solution may be comparatively higher. So, if time is not a constraint, brute force technique is a good option. Therefore, this algorithm is used only when the problem size is small because if the problem set is large then the speed of execution will be really slow.
The different types of Brute – Force Algorithm Implementations are as follows:
- Linear Search
Simple Recursive Algorithms
Recursion is a problem-solving process in which a problem is defined in terms of itself. The problem is solved by repeatedly breaking it into smaller problems, which are similar in nature to the original problems. The smaller problems are solved and their results are applied to get the final solution to the original problem.
So, in Recursive Algorithm, the original problem is divided into smaller and similar problem. Recursion, therefore, proceeds by breaking a problem into smaller versions of the same problem. The smallest version is actually the base case and it can be solved without recursion. After the base case solution is obtained, the function will stop calling itself and starts returning results. Recursion is one of the most confusing data structure algorithms. However, if the base case is understood properly, it then becomes easier.
The different types of Recursive Algorithm Implementations are as follows:
- Find Fibonacci Series using Recursion
- Raise Number To Positive Power using Recursion
- Factorial of a Number using Recursion
Branch and Bound
Branch and Bound Algorithms are for general real value problems as well as discrete and combinatorial optimisation problems. This algorithm consists of a systematic enumeration of candidate solutions by means of State Space Search. The algorithm depends on the efficient estimation of the upper and lower bounds of a branch or a region of the search space.
The primary task of a branch and bound algorithm is to maintain the lowest cost path to a goal found so far, and its cost. Once a solution has been found by the Branch and bound algorithm, the method generates a sequence of ever – improving solutions.
The first solution is remembered by the algorithm and set as a benchmark. It returns the original or the first solution if no solution is found after the algorithm completes the whole search space.
The different types of Branch and Bound Algorithm Implementations are as follows:
- Travelling Salesman Problem
- False Noise Analysis
- Nearest Neighbour Search
If you have any doubts or suggestion regarding Data Structure Algorithms, let us know about it in the comment section below.