Data Structures and Algorithms aren’t really my thing.
Hello, I am Richard. I am a developer, and you should know that I’m not a big fan of data structures and algorithms. If you can relate to this, don’t worry; after working on many projects (small and large), I discovered the six important algaorithms that every developer should know, and these six will almost always solve every problem in your development process.
What are those 6 significant algorithms?
1. Sorting Algorithm:
What exactly is sorting?- It is the algorithm that arranges the order of the items in a list.
Important Sorting Algorithms-
- Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.
- Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.
- Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.
- Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.
2. Searching Algorithm:
What exactly is searching?- It is the algorithm that finds an element in a data set.
Important Searching Algorithms-
- Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.
- Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.
- Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.
3. Dynamic Programming:
Dynamic Programming (DP) is an algorithmic technique for solving an optimization problem by breaking it down into simpler sub-problems and taking advantage of the fact that the optimal solution to the overall problem is dependent on the optimal solution to its sub-problems.
4. Recursion Algorithm:
Recursion is a problem-solving technique in which the solution is dependent on solutions to smaller instances of the same problem. Computing factorials is a classic example of recursive programming.
Every recursive program follows the same basic sequence of steps:
- Set up the algorithm. Recursive programs frequently require a seed value, to begin with. This is accomplished by either using a parameter passed to the function or by providing a non-recursive gateway function that sets up the seed values for the recursive calculation.
- Check to see if the current value(s) being processed correspond to the base case. If so, process the value and return it.
- Rephrase the solution in terms of a smaller or simpler sub-problem or sub-problems.
- Apply the algorithm to the sub-problem.
- In order to formulate an answer, combine the results.
- Return the results.
5. Divide and Conquer:
A divide-and-conquer algorithm recursively divides a problem into two or more sub-problems of the same or related type, until they are simple enough to be solved directly.
The Divide and Conquer algorithm consists of a dispute using the three steps listed below.
- Divide the original problem into sub-problems.
- Conquer: Solve each sub-problem one at a time, recursively.
- Combine: Put the solutions to the sub-problems together to get the solution to the whole problem.
Hashing is a technique or process that uses a hash function to map keys and values into a hash table. It is done to allow for quicker access to elements. The efficiency of mapping is determined by the hash function’s efficiency.
With so many algorithms of varying complexity available, it is difficult to determine which ones are actually important to understand. It often comes down to personal preference and perspective, but some algorithms that developers must be aware of have been highlighted in this article.
Source : Medium