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Dsa

The document is a comprehensive guide on data structures and algorithms, covering key concepts such as sorting and searching in arrays, linked list operations, hash tables, tree traversals, and graph algorithms. It also discusses general algorithmic techniques including recursion, dynamic programming, greedy algorithms, and backtracking. Each section provides essential operations and applications relevant to the respective data structures and algorithms.

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82 views2 pages

Dsa

The document is a comprehensive guide on data structures and algorithms, covering key concepts such as sorting and searching in arrays, linked list operations, hash tables, tree traversals, and graph algorithms. It also discusses general algorithmic techniques including recursion, dynamic programming, greedy algorithms, and backtracking. Each section provides essential operations and applications relevant to the respective data structures and algorithms.

Uploaded by

2k22csbs11
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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THE ULTIMATE DSA GUIDE

Data Structure Specific Algorithms


1. Arrays
●​ Sorting:
○​ Quicksort: Efficient average case time complexity (O(n \log n)).
○​ Mergesort: O(n \log n) useful when order of elements matter.
○​ Heapsort: (O(n \log n)).
●​ Searching:
○​ Binary Search: Fast search in sorted arrays (O(\log n)).
●​ Two Pointers:
○​ In-place manipulation: sum for sorted array (removing duplicates).
●​ Sliding Window:
○​ Subarray problems: finding maximum/minimum within a window.
2. Linked Lists
●​ Traversal:
○​ Iterate through the list, understand the node structure.
●​ Insertion/Deletion:
○​ At beginning, end, or at a specific position.
●​ Reversal:
○​ In-place reversal, recursive and iterative approaches.
●​ Cycle Detection:
○​ Floyd's Tortoise and Hare algorithm.
3. Hash Tables (Hash Maps/Sets)
●​ Implementation not needed, just understand following:
○​ Understand how hash functions work.
○​ Average O(1) time lookup.
○​ Collision Handling.
4. Trees (Binary Trees, Binary Search Trees)
●​ Traversal:
○​ Inorder, Preorder, Postorder (recursive and iterative).
●​ Searching:
○​ Find a node with a given value (especially in BSTs).
5. Stacks
●​ Implementation not needed, just understand following:
○​ Push/Pop/Peek Operations.
6. Queues
●​ Implementation not needed, just understand following:
○​ Enqueue/Dequeue Operations.
7. Heaps (Priority Queues)
●​ Implementation not needed, just understand following:
○​ Insertion/Deletion (extract: min/max)
○​ Building a heap
○​ Applications:
■​ Using a heap to find k largest/smallest elements.
8. Graphs
●​ Traversal:
○​ Breadth-First Search (BFS)
○​ Depth-First Search (DFS)
●​ Shortest Path:
○​ Dijkstra's Algorithm.
●​ Cycle Detection:
○​ DFS.
9. Tries
●​ Implementation from scratch
●​ Insertion/searching:
○​ Prefix-based operations
○​ Autocompletion:
■​ Used for word suggestions
10. Union-Find (Disjoint Set)
●​ Implementation from scratch
●​ Union/Find operations
●​ Applications: un-directed graphs
●​ Cycle detection in un-directed graphs
General Algorithms/Techniques
1. Recursion
●​ Defining a problem in terms of itself, often used for tree/graph traversals, factorials,
Fibonacci.
●​ Common applications: tree traversals, depth-first search.
2. Dynamic Programming
●​ Breaking down a problem into overlapping subproblems and optimal substructure, using
memoization.
●​ Common problems: Fibonacci sequence, knapsack problem, longest common
subsequence.
3. Greedy Algorithms
●​ Making locally optimal choices at each step with the hope of finding a global optimum.
●​ Common problems: coin change, activity selection, Dijkstra's algorithm for minimum
spanning trees.
4. Backtracking
●​ Incrementally building solutions, exploring all possibilities, and undoing choices (pruces).
●​ Common problems: Sudoku solver, N-Queens problem, permutation, combination.

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