Tree Algorithms

Learn to work with hierarchical data structures that power databases, file systems, and search operations. Understand binary search trees, explore self-balancing trees like AVL and Red-Black trees that guarantee O(log n) operations, and master tree traversal techniques (inorder, preorder, postorder) essential for data processing and expression evaluation.

3 algorithms

Trie (Prefix Tree)

Intermediate

Tree-based data structure that stores strings efficiently by sharing common prefixes. Each path from root to leaf represents a word or key. Excels at autocomplete, spell checking, IP routing tables, and dictionary implementations with O(m) operations where m is key length.

O(m)

O(ALPHABET_SIZE × N × M)

treestring-algorithms

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Lowest Common Ancestor (LCA)

Intermediate

Finds the deepest node that is an ancestor of two given nodes in a tree. Fundamental operation in tree queries with applications in computational biology (species evolution), network routing, and version control systems. Various algorithms offer different time-space tradeoffs.

AVL Tree

Advanced

First self-balancing binary search tree invented in 1962 by Adelson-Velsky and Landis. Maintains height balance through rotations, guaranteeing O(log n) operations. Stricter balancing than red-black trees makes it ideal for lookup-heavy applications like databases and file systems.

💡 Learning Tip

Start with the beginner-level algorithms to build your foundation, then progress to intermediate and advanced topics. Each algorithm includes interactive visualizations, complexity analysis, and code examples in multiple languages.