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A collection of coding challenges from LeetCode, HackerRank and CtCI

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CodingChallenges

A collection of coding challenges and their solutions from:

  • LeetCode
    • https://leetcode.com/
  • HackerRank
    • https://www.hackerrank.com/
  • Cracking the Coding Interview
    • https://www.amazon.co.uk/Cracking-Coding-Interview-6th-Programming/dp/0984782850/

Big O

Complexity Name
O(1) Constant
O(log n) Logarithmic
O(n) Linear
O(n log n) Linear-logarithmic
O(n ^ 2) Quadratic
O(n ^ c) Polynomial
O(2 ^ n) Exponential
O(n!) Factorial

Hints for how to solve:

Keywords Techniques
"Shortest path", "Minimum", "closest" BFS, Dijkstra
"Level-by-level", "Least number of moves", "Batch" BFS
"All paths", "Choices", "Branching" BFS, DFS
"Path exists" BFS, DFS, Disjont sets
"Path may not exist" isolated vertices, cycles
"Generate all", "All permutations", "All combinations", "All possible", "Choices", "Branching" Backtracking (DFS)
"Next Permutation" pivot & sorting
"Sorted", "Maximum", "Minimum" Binary Search, Two pointers
"Right-most", "Left-most" Binary Search
"Iterating array comparing elements" Stack
"Next greater element", "Next lesser element" Monotonic stack
"Longest subsequence", "Smallest subsequence", "Maximum", "Minimum", "Neighbors" Sliding window
"Subsequence in a graph" Memoization, Backtracking (DFS)
"In-place" Swap
"Loop/cycle in a linked list" Slow and Fast pointers i.e. Hare and Tortoise
"Loop/cycle in a graph" Disjoint sets, Topological sort
"Minimum cost" MST, Kruskal, Prims
"Compare neighbors in a string", "Comparing right to left elements" Stack
"Largest value", "Smallest value" Heap
"kth smallest", "kth largest", "kth frequent", "Top k", "k closet" QuickSelect
"Matrix diagonal" r1 - c1 == r2 - c2
"Consecutive" sort
"Merge", "Intervals", "Neighbors" sort
"Rotate by k" (i + k) % array.count

Need to... Tips

Need to... Technique Example
Optimise graph traversal Memoization LongestIncreasingSubsequence
Generate all premutations of an array Offset nested for loops with the inner starting at i+1 AdditiveNumber
Find "x" from an infinite array Treat array as a graph and perform a DFS, at each level include all elements in the array CoinChanges
Explore possible replacement values for a given element in an array/string DFS, Memoization ValidParenthesisString
Can't use additional memeory when working with an array Negative Marking FindAllNumbersDisappearedInAnArray
Combine numbers together to form one number e.g. [5, 10] to 510 Convert the numbers to strings LargestNumber
Simulate time passing or different rounds Batch up changes either using a Queue or caching state between rounds PushDominoes
Count possible substrings count * (count + 1) / 2 Substring
Count possible subarrays count * (count + 1) / 2 CountTheNumberOfIncremovableSubarraysI
Count possible subsequences (2 ^ count) - 1 Subsequence
Repeatedly find the min and max value in subarrays Sort the overall array and take the first and last element in the subarray MinimumDifferenceBetweenHighestAndLowestOfKScore
Find the end of duplicates in an array Nest a while loop that only increments one of the pointers RemoveDuplicatesFromSortedListII
Find the minimum distance from one element to multiple elements in a graph Multi-source BFS AsFarFromLandAsPossible
Traverse up a tree Turn the tree into a directed graph with child -> parent and parent -> child ClosestLeafInABinaryTree
Sort two "linked" arrays together Merge the two array into one tuple array MaximumCoinsHeroesCanCollect
Work out the cost/total of all previous elements in sorted data PrefixSum MaximumCoinsHeroesCanCollect
Flip neighbors to find the maximum/minimum Sliding window MaximizeTheConfusionOfAnExam

A lot of problems can be treated as graph problems.

Techniques

Technique What is it? Example
BFS Breadth-first search involves searching a graph level by level - ensuring all vertices on a level have been searched before moving onto the next level. A level is all immediate vertices connected to a vertice from the previous level. BFS
DFS Depth-first search involves searching a graph by fully exploring one path from root before searching any other other vertices from root. DFS
Binary search Binary search works by selecting the middle index in the sorted array values and comparing its value against target. If values[mid] matches target then we have found our target and can return; if values[mid] does not match target then we half the search space by moving either left to the right (if values[mid] was smaller than target) or moving right to the left (if values[mid] was larger than target). We can reduce the search space like this because values sorted so we know that if values[mid] was smaller than target then any index less than mid will contain an even smaller value than values[mid] so searching those other indexes would be pointless (the opposite is true for reducing the search space to the right of mid). BinarySearch
Dynamic Programming Dynamic programming is a technique for solving problems of recursive nature where we re-use work to speed up the process of solving the problem. It can look like dynamic programming solutions are brute force in nature.

Dynamic programming can be implemented bottom-up or top-down:

- Bottom-up is when we solve the easiest sub-problem first and use that solution directly to solve a "higher" problem.

- Top-down is when we cache the solutions to sub-problems in the course of solving a "higher" problem. We can then access those cached solutions when attempting to solve other similar "higher" problems (not the original "higher problem").		 HouseRobberIII

Data Structure Characteristics

Binary Tree

  1. A tree has one root node
  2. Each node has only one parent node (apart from the root node which has none)
  3. Each node has a maximum of two child nodes - left and right
  4. A node without any child nodes is a leaf node
  5. Any subtree is also a valid independent binary Tree
  6. The depth of a node is the number of edges from the node to the tree's root node - a root node will have a depth of 0
  7. The height of a node is the number of edges on the longest path from the node to a leaf - a leaf node will have a height of 0
  8. Traversal:
    1. Preorder traversal involves traversing from the root to the left subtree then to the right subtree
    2. Inorder traversal involves traversing from the left subtree to the root then to the right subtree
    3. Postorder traversal involves traversing from the left subtree to the right subtree then to the root
  9. Searching:
    1. Breadth First Search (BFS) - queue based, level by level traversal
    2. Depth First Search (DFS) - stack based, go as deep as possible then backtrack
  10. A balanced binary tree is a tree in which the height of the left and right subtrees differ by no more than 1
  11. A complete binary tree is a tree in which every level, except possibly the last, is completely filled, and all nodes in the last level are filled left to right
  12. A full/proper binary tree is a tree in which every node has either 0 or 2 children.
  13. A tree is special type of graph
  14. Doesn't contain cycles

Binary Search Tree

Inherits the characteristics of a Binary Tree

  1. Nodes to the left of the root are less than or equal than that of the root
  2. Nodes to the right of the root are greater than that of the root
  3. Inorder traversal results in a sorted list of the nodes in a BST
  4. Searching takes O(log n) time as at each node it is possible to discard half of the remaining nodes (as being either too small of too large)

Graph

  1. Non-linear data structure consisting of nodes/vertices and edges
  2. Can be weighted or unweighted
    1. Weighted - each edge has a cost
    2. Unweighted - each edge is the same i.e. no cost
  3. Can be directed or undirected
    1. Directed - only allow travel down an edge in a certain direction,
    2. Undirected - edge can be used for travel both ways
  4. Each node has an indegree - this is the number of node connecting into that node
  5. Each node has n outdegree - this is the number of nodes, this node is connecting to
  6. A node can have any number of edges to other nodes
  7. Can contain unconnected nodes
  8. Adjacency list can be used to store the outdegree of each node in a graph
  9. A matrix can be used to show edges between Nodes
    1. Wasteful spacewise
  10. A path is sequence of nodes that it takes to get from A -> Z
  11. Directed Acyclic Graph (DAG) is a directed graph with no directed cycles
    1. Topological sort only works on DAGs
  12. Searching:
    1. Breadth First Search (BFS) - queue based, level-by-level traversal
    2. Depth First Search (DFS) - stack (recursive) based, go as deep as possible then backtrack
  13. Can contain cycles

Matrix

  1. Just a disguised graph
  2. Relative indexing allows for moving/search a matrix without going out of bounds by mapping the possible movements and determining if that movement is valid
  3. Visited arrays ensure that when search we don't get caught in an infinite loop
  4. Searching:
    1. Breadth First Search (BFS) - queue based, level by level traversal
    2. Depth First Search (DFS) - stack based, go as deep as possible then backtrack
  5. Backtracking involves exhaustively searching down one path before reversing back up that path to search down alternative paths at each branching point

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A collection of coding challenges from LeetCode, HackerRank and CtCI

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