A comprehensive collection of algorithmic templates, data structures, and problem solutions for competitive programming and technical interviews.

• AdvancedTechniques.py - Mo's Algorithm, advanced optimization techniques
• AdvancedDSA.py - Segment Trees with Lazy Propagation, advanced data structures
• Dp.py - Dynamic Programming patterns including TSP, Digit DP, and Bitmask DP
• Graph.py - Graph algorithms (Dijkstra, Floyd-Warshall, 0-1 BFS, etc.)
• String.py - String algorithms including KMP, pattern matching
• SlidingWindow.py - Sliding window techniques and patterns

• mySolutions/ - Directory containing solutions to specific LeetCode problems
  • Find Edges in Shortest Paths
  • Maximize Subarray Sum After Removing All Occurrences of One Element
  • Number of Distinct Roll Sequences
  • Paint House III
  • Swim in Rising Water

• Lazy Propagation Segment Tree - Efficient range updates and queries
• Mo's Algorithm - Square root decomposition for offline queries
• Graph Algorithms - Complete implementation of shortest path algorithms

• Bitmask DP - Traveling Salesman Problem (TSP) variations
• Digit DP - Count numbers with specific properties
• Set Cover Problems - Minimum actions to cover all positions

• KMP Algorithm - Efficient pattern matching
• Advanced String Processing - Multiple string manipulation techniques

• Dijkstra's Algorithm - Single-source shortest path
• Floyd-Warshall - All-pairs shortest path
• 0-1 BFS - Specialized BFS for binary weights

Each Python file contains well-documented classes and functions that can be directly imported and used:

from AdvancedDSA import LazySegTree
from Graph import Solution as GraphSolution
from Dp import Solution as DPSolution

# Example: Using Lazy Segment Tree
arr = [1, 2, 3, 4, 5]
seg_tree = LazySegTree(arr)
seg_tree.range_add(0, 2, 10)  # Add 10 to range [0, 2]
result = seg_tree.range_sum(1, 3)  # Query sum in range [1, 3]

# Example: Using Dijkstra's algorithm
graph_solver = GraphSolution()
edges = [(0, 1, 2), (1, 2, 3), (0, 2, 6)]
distances = graph_solver.dijkstra(3, edges, 0)

• Competitive Programmers preparing for contests like Codeforces, AtCoder
• Software Engineers preparing for technical interviews at FAANG companies
• Students learning advanced algorithms and data structures
• Anyone looking to improve their problem-solving skills

• Shortest Path Algorithms
• Graph Traversal Techniques
• Specialized BFS/DFS variants

• Classical DP patterns
• Optimization techniques
• State space reduction methods

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• travelling salesmen problem

• Range Query Data Structures
• Efficient Update Mechanisms
• Square Root Decomposition

• Pattern Matching
• String Hashing
• Advanced String Algorithms

• Python 3.7+
• Basic understanding of algorithms and data structures
• Familiarity with competitive programming concepts

Feel free to contribute by:

1. Adding new algorithm templates
2. Optimizing existing implementations
3. Adding more problem solutions
4. Improving documentation

This project is open source and available under the MIT License.

• LeetCode for providing an excellent platform for practicing algorithms
• The competitive programming community for sharing knowledge and techniques