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GoAlgo

Interactive Learning Platform

Master Algorithms Through Visualization

Explore our comprehensive collection of 11 algorithms with step-by-step visualizations, detailed complexity analysis, and production-ready code implementations in multiple programming languages.

11 Algorithms
Big-O Analysis
Step-by-Step Traces

Sorting Algorithms

Organize data efficiently

Sorting algorithms arrange elements in a specific order, typically ascending or descending. They are fundamental to Computer Science and are used everywhere—from organizing search results to optimizing database queries. Understanding different sorting techniques helps you choose the right algorithm for your specific use case, balancing factors like time complexity, space usage, and data characteristics.

Compare-based sorting (Bubble, Quick, Merge) vs. non-comparative (Counting, Radix)
In-place algorithms use O(1) extra space, while others require O(n) auxiliary space
Stable sorts preserve the relative order of equal elements

Bubble Sort

Sorting

Bubble Sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order.

Best
O(n)
Worst
O(n²)
Average
O(n²)
Space
O(1)
Visualize Algorithm

Quick Sort

Sorting

Quick Sort is an efficient, divide-and-conquer sorting algorithm. It works by selecting a 'pivot' element and partitioning the other elements into two sub-arrays.

Best
O(n log n)
Worst
O(n²)
Average
O(n log n)
Space
O(log n)
Visualize Algorithm

Merge Sort

Sorting

Merge Sort is an efficient, divide-and-conquer algorithm that works by recursively dividing the array into halves until each sub-array is sorted, then merging them back together.

Best
O(n log n)
Worst
O(n log n)
Average
O(n log n)
Space
O(n)
Visualize Algorithm

Selection Sort

Sorting

Selection Sort repeatedly selects the smallest remaining element and swaps it into its correct position, building the sorted portion of the array from left to right.

Best
O(n²)
Worst
O(n²)
Average
O(n²)
Space
O(1)
Visualize Algorithm

Insertion Sort

Sorting

Insertion Sort builds the final sorted array one element at a time by inserting each new element into its correct position in the already sorted part.

Best
O(n)
Worst
O(n²)
Average
O(n²)
Space
O(1)
Visualize Algorithm

Counting Sort

Sorting

Counting Sort counts occurrences of each distinct value and rebuilds the array in order, making it efficient for integers in a limited range.

Best
O(n + k)
Worst
O(n + k)
Average
O(n + k)
Space
O(n + k)
Visualize Algorithm

Radix Sort (LSD)

Sorting

Radix Sort processes numbers digit by digit using a stable counting sort per digit, making it efficient for integers with limited digit length.

Best
O(d · (n + k))
Worst
O(d · (n + k))
Average
O(d · (n + k))
Space
O(n + k)
Visualize Algorithm

Heap Sort

Sorting

Heap Sort builds a max-heap and repeatedly extracts the maximum element to produce a sorted array in-place.

Best
O(n log n)
Worst
O(n log n)
Average
O(n log n)
Space
O(1)
Visualize Algorithm

Searching Algorithms

Find elements quickly

Searching algorithms locate specific elements within a data structure. The efficiency of your search can dramatically impact application performance, the difference between O(n) and O(log n) becomes significant with millions of records. Choosing the right search algorithm depends on whether your data is sorted, the data structure being used, and how frequently searches are performed.

Linear Search works on any array but is slow for large datasets
Binary Search requires sorted data but offers O(log n) performance
Tree-based searches combine fast lookup with dynamic insertion

Linear Search

Searching

Linear Search is a simple searching algorithm that steps through an array one by one until it finds the target value or reaches the end.

Best
O(1)
Worst
O(n)
Average
O(n)
Space
O(1)
Visualize Algorithm

Binary Search

Searching

Binary Search efficiently finds a target value in a sorted array by repeatedly dividing the search interval in half.

Best
O(1)
Worst
O(log n)
Average
O(log n)
Space
O(1)
Visualize Algorithm

BST Search

Searching

Searches for a value in a Binary Search Tree (BST) by repeatedly comparing the target with the current node and moving left or right.

Best
O(1)
Worst
O(n)
Average
O(log n)
Space
O(1)
Visualize Algorithm
Learn the Fundamentals

Understanding Algorithms

Build a strong foundation in computational thinking

Future-Proof your skillset

Frameworks and languages fade, but algorithmic logic is timeless. Mastering these patterns gives you a transferable skill set that allows you to adapt to any new tech stack without starting from scratch.

Slash Infrastructure Costs

Efficient algorithms don't just run faster; they consume less memory and processing power. Learning to optimize code is the key to lowering cloud server bills and extending battery life in mobile applications.

Demystify the "Black Box"

Move beyond "magic boxes." Knowing how functions like .sort() work under the hood is key to debugging obscure errors and selecting the perfect data structure for performance-critical tasks.

Common Questions

Frequently Asked Questions

Get answers to the most common questions about algorithms, complexity analysis, and when to use each approach.

Ready to start learning?

Pick any algorithm above and watch it come to life with our interactive visualizations.