4 algorithms every developer should learn

Ask any developer, and they will tell you they learned many algorithms as their career progressed. Some algorithms are more important than others. If you are getting started as a developer, you should focus on learning different algorithms to become a proficient programmer. However, with so many algorithms to learn, it can be challenging to pick up the essential developer algorithms.

To help you with the choice, we have created a list of the essential algorithms every developer should learn.

Let’s get started.

1.Dijkstra’s algorithm

Named after one of the most influential computer scientists of all time, Edsger Dijkstra, Dijkstra’s algorithm is used to find the shortest path between two nodes in a graph. It is also known as Dijkstra’s shortest path algorithm.

How does the algorithm work?

Dijkstra’s algorithm is used in computer applications to find the single shortest path in the graph from a source to all graph vertices. It is an iterative algorithm in which a vertex is added to the array with the minimum distance from the source. The iteration is repeated until all the vertices are covered. This is a greedy approach used by the algorithm.

In practical applications, Dijkstra’s algorithm is implemented using a set. It is very efficient when implemented with a Min Heap data structure. It also has a much lower running time at just O(V+ElogV) (V is the total number of vertices, and E is the number of edges in the graph).

Navigation applications like Google Maps use Dijkstra’s algorithm to find the shortest path to reach a destination.

One of the limitations of this algorithm is that it only works with directed and undirected graphs with positive weight edges.

2. Merge sort

Sorting algorithms are essential for developers. There are several sorting algorithms, but merge sort is one of the crucial developer algorithms to understand.

How does the algorithm work?

The merge sort algorithm is based on the divide and conquers programming technique that is much more efficient. In a worst-case scenario, this algorithm can sort n number of elements in just O(nlogn) time. For comparison, it is often more efficient than primitive sorting algorithms like the Bubble Sort, which takes O(n^2) time to sort the same number of elements.

The approach here is simple. This algorithm repeatedly breaks down an array into subarrays until each subarray contains only a single element. Then the merge sort recursively merges the subarrays to create a sorted array.

You can learn more about the theoretical aspect of the merge sort algorithm here.

In practical engineering, the merge sort algorithm is used by e-commerce websites to recommend “you may also like” options to the users.

3. Depth First Search

Depth First Search or DFS is one of the first developer algorithms taught to computer science students. It is an efficient algorithm to traverse or search a graph and other data structures. DFS can be easily modified to traverse trees.

How does the algorithm work?

The Depth First Search traversal can start from any arbitrary node of the graph, and gradually, it dives into each adjacent vertex until there are no unvisited vertices. The algorithm stops when it encounters a dead-end. It is one of the simplest developer algorithms that you should learn to traverse a data structure easily. The algorithm is also exceptionally efficient, taking only (V+E) time for complete traversal.

In practical applications, the DFS algorithm is used for topological sorting, detecting cycles in the graph, and finding strongly connected components in DNA analysis.

4. Minimum spanning tree algorithms

In computer science, a spanning tree is an important concept. A spanning tree is a subset of a graph that has all the vertices covered with the minimum number of edges.

How does this algorithm work?

The minimum spanning tree algorithm (MST) is used to find the minimum cost among a graph’s possible spanning tree. The cost of a graph spanning trees depends on the weight of the edges. There are several MST algorithms, but the two most widely used ones are Kruskal’s and Prim’s.

Kruskal’s algorithm creates a minimum spanning tree for a graph by adding the edge with the minimum cost to a growing set. Here the algorithm first sorts the edges by their weight and then starts adding the edges to the minimum spanning tree. This algorithm is preferred for sorting sparse graphs.

On the contrary, Prim’s Algorithm uses a greedy approach which is ideal for dense graphs. This algorithm creates two sets of structures, one containing the growing minimum spanning tree and the other containing all unused vertices. Each iteration adds the edge with the minimum weight to the graph.

In practical application, MST algorithms are used for broadcast networks, taxonomy, and cluster analysis.

Conclusion

Algorithms are the core essentials of computer programming; learning them improves your skill. It is the reason why interviews at tech giants like Google, Amazon, Facebook, and Netflix is focused on evaluating a candidate’s knowledge of algorithms and data structures. Learning algorithms improve your problem-solving skills and as technologies are evolving, more efficient algorithms will be needed to make the most use of these advancements. 

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