## 6 Algorithms Every Developer Should Know

- In this blog, I discovered the six important algorithms that every developer should know, and these six will almost always solve every problem in your development process.

- Sorting Algorithm
- Searching Algorithm
- Dynamic Programming
- Recursion Algorithm
- Divide and Conquer
- Hashing

### 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 conquers 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.

### 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.

### 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.

### 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.

### 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

- 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.

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