/docs/notes/development/algo/ Recent content in Algorithms on Piotr Kosmowski Hugo -- gohugo.io pl-pl /docs/notes/development/algo/algorithms/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/notes/development/algo/algorithms/ /docs/notes/development/algo/data_structures/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/notes/development/algo/data_structures/ Using Java Framework Queue # Set # HashMap (or Map Interface) # Description: A collection that stores key-value pairs, providing constant-time complexity for insertions, deletions, and lookups. Java Implementation: Example: HashMap<String, Integer> map = new HashMap<>(); Use Cases: Frequently used for scenarios requiring fast lookups by a key, like counting frequencies of elements or caching. 5. HashSet (or Set Interface) # Description: A collection that stores unique elements with no duplicate values and provides constant-time operations. /docs/notes/development/algo/kata/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/notes/development/algo/kata/ Tips # If input array is sorted then Binary search Two pointers If asked for all permutations/subsets then Backtracking If given a tree then DFS BFS If given a graph then DFS BFS If given a linked list then Two pointers If recursion is banned then Stack If must solve in-place then Swap corresponding values Store one or more different values in the same pointer If asked for maximum/minimum subarray/subset/options then /docs/notes/development/algo/o-notation/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/notes/development/algo/o-notation/ In Big O Notation (or O-notation), we analyze the efficiency of algorithms, typically focusing on time complexity and space complexity. The goal is to understand how an algorithm’s performance scales as the input size grows. Big O notation expresses this growth in terms of the worst-case scenario. Examples: Operation Type Typical Big O Complexity Constant-time operations (e.g., reading/writing a variable) O(1) Loop over n elements O(n) Nested loops O(n^2) Binary search O(log n) Merge sort, Quick sort O(n log n) Insertion in balanced BST, Heap O(log n) BFS/DFS (graph traversal) O(V + E) Basic Operations # These are simple operations that are treated as having a constant time complexity, i.