20 docs tagged with "algorithms"

Master advanced backtracking techniques including word search, palindrome partitioning, constraint propagation, and branch-and-bound optimization.

Master advanced binary search techniques including searching in rotated arrays, finding peak elements, and solving optimization problems like capacity to ship packages.

Master advanced dynamic programming techniques including interval DP, bitmask DP, digit DP, and DP optimizations with Knuth-Yao and divide-and-conquer.

Master advanced graph algorithms including Tarjan SCC, bipartite checking, Euler paths, and network flow concepts.

Master advanced greedy techniques including greedy with sorting, priority queue applications, task scheduling, and when greedy fails.

Master algorithmic problem-solving with a comprehensive guide from fundamentals to advanced techniques. Perfect for LeetCode preparation and technical interviews.

Master the core concepts of backtracking algorithms including state space exploration, decision trees, pruning strategies, and constraint satisfaction problems.

Master fundamental backtracking problems including N-Queens, Sudoku solver, permutations, combinations, and subsets with complete implementations.

Master fundamental dynamic programming problems including 0/1 Knapsack, Longest Common Subsequence, Longest Increasing Subsequence, Coin Change, and Edit Distance.

Master the most important greedy algorithm problems: fractional knapsack, Huffman coding, job scheduling, and interval scheduling maximization

Understand overlapping subproblems, optimal substructure, memoization vs tabulation, and the core principles of dynamic programming.

Apply dynamic programming to tree and graph problems including tree DP, rerooting technique, DAG DP, and shortest paths as DP.

Learn essential graph algorithms including Dijkstra, Bellman-Ford, topological sort, and minimum spanning trees using Kruskal and Prim algorithms.

Learn graph representations (adjacency list and matrix), graph traversal algorithms (BFS and DFS), and connected components analysis.

Master greedy algorithm principles including greedy choice property, optimal substructure, proof techniques, and when greedy applies.

Master advanced coding techniques including sentinel values, dummy nodes, index mapping, coordinate compression, offline processing, and meet-in-the-middle optimization.

Master systematic problem decomposition, pattern recognition, constraint analysis, and data structure selection for algorithmic problems.

Various searching algorithms from linear search to advanced interpolation search

Comparison-based and non-comparison sorting algorithms with their properties and use cases

Master string processing techniques including parsing, validation, anagrams, pattern matching, and transformations essential for problem-solving.