Top 10 Algorithms for Coding Interview

Link of the original article : http://www.programcreek.com/2012/11/top-10-algorithms-for-coding-interview/

 

Top 10 Algorithms for Coding Interview

Lastest Upate: 5/19/2015 (PDF Version)

The following are the common subjects in coding interviews. As understanding those concepts requires much more effort, this tutorial only serves as an introduction. The subjects that are covered include: 1) String/Array/Matrix, 2) Linked List, 3) Tree, 4) Heap, 5) Graph, 6) Sorting, 7) Dynamic Programming, 8) Bit Manipulation, 9) Combinations and Permutations, and 10) Math Problems. I highly recommend you to read "Simple Java" first, if you need a brief review of Java basics. If you want to see code examples that show how to use a popular API, you can useJavaSED.com. (Note: Similar problems are placed together even with the same number.)

1. String/Array

String is a class in Java. It contains a field of a character array and other fields and methods. Without auto-completion of any IDE, the following methods should be remembered.

toCharArray() //get char array of a String
charAt(int x) //get a char at the specific index
length() //string length
length //array size 
substring(int beginIndex) 
substring(int beginIndex, int endIndex)
Integer.valueOf()//string to integer
String.valueOf()/integer to string
Arrays.sort()  //sort an array
Arrays.toString(char[] a) //convert to string
Arrays.copyOf(T[] original, int newLength)
System.arraycopy(Object src, int srcPos, Object dest, int destPos, int length)

Strings/arrays are easy to understand, but the interview problems often require advanced algorithm to solve, such as dynamic programming, recursion, etc.

Classic problems:
1) Rotate Array
2) Evaluate Reverse Polish Notation (Stack)
3) Isomorphic Strings
4) Word Ladder (BFS)
4) Word Ladder II (BFS)
5) Median of Two Sorted Arrays
5) Kth Largest Element in an Array
6) Wildcard Matching
6) Regular Expression Matching
7) Merge Intervals
8) Insert Interval
9) Two Sum
9) Two Sum II – Input array is sorted
9) Two Sum III - Data structure design
9) 3Sum
9) 4Sum
10) 3Sum Closest
11) String to Integer
12) Merge Sorted Array
13) Valid Parentheses
13) Longest Valid Parentheses
14) Implement strStr()
15) Minimum Size Subarray Sum
16) Search Insert Position
17) Longest Consecutive Sequence
18) Valid Palindrome
19) ZigZag Conversion
20) Add Binary
21) Length of Last Word
22) Triangle
24) Contains Duplicate
24) Contains Duplicate II
24) Contains Duplicate III
25) Remove Duplicates from Sorted Array
26) Remove Duplicates from Sorted Array II
27) Longest Substring Without Repeating Characters
28) Longest Substring that contains 2 unique characters [Google]
28) Substring with Concatenation of All Words
29) Minimum Window Substring
30) Reverse Words in a String
31) Find Minimum in Rotated Sorted Array
31) Find Minimum in Rotated Sorted Array II
31) Search in Rotated Sorted Array
31) Search in Rotated Sorted Array II
32) Find Peak Element
33) Min Stack
34) Majority Element
34) Majority Element II
35) Remove Element
36) Largest Rectangle in Histogram
37) Longest Common Prefix [Google]
38) Largest Number
39) Simplify Path
40) Compare Version Numbers
41) Gas Station
44) Pascal's Triangle
44) Pascal’s Triangle II
45) Container With Most Water
45) Candy [Google]
45) Trapping Rain Water
46) Count and Say
47) Search for a Range
48) Basic Calculator
49) Anagrams
50) Shortest Palindrome
51) Rectangle Area
52) Summary Ranges

2. Matrix

Common methods to solve matrix related problem include DFS, BFS, dynamic programming, etc.

Classic Problems:
1) Set Matrix Zeroes
2) Spiral Matrix
2) Spiral Matrix II
3) Search a 2D Matrix
4) Rotate Image [Palantir]
5) Valid Sudoku
6) Minimum Path Sum (DP) [Google]
7) Unique Paths (DP) [Google]
7) Unique Paths II (DP)
8) Number of Islands (DFS/BFS)
9) Surrounded Regions (BFS)
10) Maximal Rectangle
10) Maximal Square
11) Word Search (DFS)
11) Word Search II

3. Linked List

The implementation of a linked list is pretty simple in Java. Each node has a value and a link to next node.

class Node {
	int val;
	Node next;   Node(int x) {
		val = x;
		next = null;
	}
}

Two popular applications of linked list are stack and queue.

Stack

class Stack{
	Node top;   public Node peek(){
		if(top != null){
			return top;
		}   return null;
	}   public Node pop(){
		if(top == null){
			return null;
		}else{
			Node temp = new Node(top.val);
			top = top.next;
			return temp;	
		}
	}   public void push(Node n){
		if(n != null){
			n.next = top;
			top = n;
		}
	}
}

Queue

class Queue{
	Node first, last;   public void enqueue(Node n){
		if(first == null){
			first = n;
			last = first;
		}else{
			last.next = n;
			last = n;
		}
	}   public Node dequeue(){
		if(first == null){
			return null;
		}else{
			Node temp = new Node(first.val);
			first = first.next;
			return temp;
		}	
	}
}

The Java standard library contains a class called "Stack". Another class from Java SDK isLinkedList, which can be used as a Queue (add() and remove()). (LinkedList implements the Queue interface.) If a stack or queue is required to solve problems during your interview, they are ready to be used.

Classic Problems:
1) Add Two Numbers
2) Reorder List
3) Linked List Cycle
4) Copy List with Random Pointer
5) Merge Two Sorted Lists
6) Merge k Sorted Lists *
7) Remove Duplicates from Sorted List
7) Remove Duplicates from Sorted List II
8) Partition List
9) LRU Cache
10) Intersection of Two Linked Lists
11) Remove Linked List Elements
12) Swap Nodes in Pairs
13) Reverse Linked List
13) Reverse Linked List II
14) Remove Nth Node From End of List (Fast-Slow Pointers)
15) Implement Stack using Queues
15) Implement Queue using Stacks
16) Palindrome Linked List
17) Implement a Queue using an Array
18) Delete Node in a Linked List

4. Tree & Heap

A tree normally refers to a binary tree. Each node contains a left node and right node like the following:

class TreeNode{
	int value;
	TreeNode left;
	TreeNode right;
}

Here are some concepts related with trees:

1. Binary Search Tree: for all nodes, left children <= current node <= right children
   
2. Balanced vs. Unbalanced: In a balanced tree, the depth of the left and right subtrees of every node differ by 1 or less.
   
3. Full Binary Tree: every node other than the leaves has two children.
   
4. Perfect Binary Tree: a full binary tree in which all leaves are at the same depth or same level, and in which every parent has two children.
   
5. Complete Binary Tree: a binary tree in which every level, except possibly the last, is completely filled, and all nodes are as far left as possible

Heap is a specialized tree-based data structure that satisfies the heap property. The time complexity of its operations are important (e.g., find-min, delete-min, insert, etc). In Java,PriorityQueue is important to know.

Classic problems:
1) Binary Tree Preorder Traversal
2) Binary Tree Inorder Traversal [Palantir]
3) Binary Tree Postorder Traversal
4) Binary Tree Level Order Traversal
4) Binary Tree Level Order Traversal II
5) Validate Binary Search Tree
6) Flatten Binary Tree to Linked List
7) Path Sum (DFS or BFS)
7) Path Sum II (DFS)
8) Construct Binary Tree from Inorder and Postorder Traversal
8) Construct Binary Tree from Preorder and Inorder Traversal
9) Convert Sorted Array to Binary Search Tree [Google]
10) Convert Sorted List to Binary Search Tree [Google]
11) Minimum Depth of Binary Tree
12) Binary Tree Maximum Path Sum *
13) Balanced Binary Tree
14) Symmetric Tree
15) Binary Search Tree Iterator
16) Binary Tree Right Side View
17) Implement Trie (Prefix Tree)
18) Add and Search Word - Data structure design (DFS)
19) Merge k sorted arrays [Google]
20) Populating Next Right Pointers in Each Node
21) Populating Next Right Pointers in Each Node II
21) Unique Binary Search Trees (DP)
21) Unique Binary Search Trees II (DFS)
22) Sum Root to Leaf Numbers (DFS)
23) Count Complete Tree Nodes
24) Invert Binary Tree
25) Kth Smallest Element in a BST
26) Lowest Common Ancestor of a Binary Search Tree
26) Lowest Common Ancestor of a Binary Tree

5. Graph

Graph related questions mainly focus on depth first search and breath first search. Depth first search is straightforward, you can just loop through neighbors starting from the root node.

Below is a simple implementation of a graph and breath first search. The key is using a queue to store nodes.

1) Define a GraphNode

class GraphNode{ 
	int val;
	GraphNode next;
	GraphNode[] neighbors;
	boolean visited;   GraphNode(int x) {
		val = x;
	}   GraphNode(int x, GraphNode[] n){
		val = x;
		neighbors = n;
	}   public String toString(){
		return "value: "+ this.val; 
	}
}

2) Define a Queue

class Queue{
	GraphNode first, last;   public void enqueue(GraphNode n){
		if(first == null){
			first = n;
			last = first;
		}else{
			last.next = n;
			last = n;
		}
	}   public GraphNode dequeue(){
		if(first == null){
			return null;
		}else{
			GraphNode temp = new GraphNode(first.val, first.neighbors);
			first = first.next;
			return temp;
		}	
	}
}

3) Breath First Search uses a Queue

public class GraphTest {   public static void main(String[] args) {
		GraphNode n1 = new GraphNode(1); 
		GraphNode n2 = new GraphNode(2); 
		GraphNode n3 = new GraphNode(3); 
		GraphNode n4 = new GraphNode(4); 
		GraphNode n5 = new GraphNode(5);   n1.neighbors = new GraphNode[]{n2,n3,n5};
		n2.neighbors = new GraphNode[]{n1,n4};
		n3.neighbors = new GraphNode[]{n1,n4,n5};
		n4.neighbors = new GraphNode[]{n2,n3,n5};
		n5.neighbors = new GraphNode[]{n1,n3,n4};   breathFirstSearch(n1, 5);
	}   public static void breathFirstSearch(GraphNode root, int x){
		if(root.val == x)
			System.out.println("find in root");   Queue queue = new Queue();
		root.visited = true;
		queue.enqueue(root);   while(queue.first != null){
			GraphNode c = (GraphNode) queue.dequeue();
			for(GraphNode n: c.neighbors){   if(!n.visited){
					System.out.print(n + " ");
					n.visited = true;
					if(n.val == x)
						System.out.println("Find "+n);
					queue.enqueue(n);
				}
			}
		}
	}
}

Output:

value: 2 value: 3 value: 5 Find value: 5
value: 4

Classic Problems:
1) Clone Graph
2) Course Schedule (BFS/DFS)
2) Course Schedule II (BFS)

6. Sorting

Time complexity of different sorting algorithms. You can go to wiki to see basic idea of them.

Algorithm
Average Time
Worst Time
Space

Bubble sort
n^2
n^2
1

Selection sort
n^2
n^2
1

Insertion sort
n^2
n^2

Quick sort
n log(n)
n^2

Merge sort
n log(n)
n log(n)
depends

* BinSort, Radix Sort and CountSort use different set of assumptions than the rest, and so they are not "general" sorting methods. (Thanks to Fidel for pointing this out)

Here are some implementations/demos, and in addition, you may want to check out how Java developers sort in practice.
1) Mergesort
2) Quicksort
3) InsertionSort.
4) Maximum Gap (Bucket Sort)
5) First Missing Positive (Bucket Sort)
6) Sort Colors (Counting Sort)

7. Dynamic Programming

Dynamic programming is a technique for solving problems with the following properties:

1. An instance is solved using the solutions for smaller instances.
   
2. The solution for a smaller instance might be needed multiple times.
   
3. The solutions to smaller instances are stored in a table, so that each smaller instance is solved only once.
   
4. Additional space is used to save time.

The problem of climbing steps perfectly fit those 4 properties. Therefore, it can be solve by using dynamic programming.

public static int[] A = new int[100];   public static int f3(int n) {
	if (n <= 2)
		A[n]= n;   if(A[n] > 0)
		return A[n];
	else
		A[n] = f3(n-1) + f3(n-2);//store results so only calculate once!
	return A[n];
}

Classic problems:
1) Edit Distance
1) Distinct Subsequences Total
2) Longest Palindromic Substring
3) Word Break
3) Word Break II
4) Maximum Subarray
4) Maximum Product Subarray
5) Palindrome Partitioning
5) Palindrome Partitioning II
6) House Robber [Google]
6) House Robber II
7) Jump Game
7) Jump Game II
8) Best Time to Buy and Sell Stock
8) Best Time to Buy and Sell Stock II
8) Best Time to Buy and Sell Stock III
8) Best Time to Buy and Sell Stock IV
9) Dungeon Game
10) Minimum Path Sum
11) Unique Paths
12) Decode Ways

8. Bit Manipulation

Bit operators:

OR (|)
AND (&)
XOR (^)
Left Shift (<<)
Right Shift (>>)
Not (~)

1|0=1
1&0=0
1^0=1
0010<<2=1000
1100>>2=0011
~1=0

Get bit i for a give number n. (i count from 0 and starts from right)

public static boolean getBit(int num, int i){
	int result = num & (1<<i);   if(result == 0){
		return false;
	}else{
		return true;
	}
}

For example, get second bit of number 10.

i=1, n=10
1<<1= 10 1010&10=10 10 is not 0, so return true;

Classic Problems:
1) Single Number
1) Single Number II
2) Maximum Binary Gap
3) Number of 1 Bits
4) Reverse Bits
5) Repeated DNA Sequences
6) Bitwise AND of Numbers Range
7) Power of Two

9. Combinations and Permutations

The difference between combination and permutation is whether order matters.

Example 1:

Given 5 numbers - 1, 2, 3, 4 and 5, print out different sequence of the 5 numbers. 4 can not be the third one, 3 and 5 can not be adjacent. How many different combinations?

Example 2:

Given 5 banaba, 4 pear, and 3 apple, assuming one kind of fruit are the same, how many different combinations?

Class Problems:
1) Permutations
2) Permutations II
3) Permutation Sequence
4) Generate Parentheses
5) Combination Sum (DFS)
5) Combination Sum II (DFS)
5) Combination Sum III (DFS)
6) Combinations (DFS)
7) Letter Combinations of a Phone Number (DFS)
8) Restore IP Addresses

10. Math

Solving math problems usually require us to get some observations and form rules:

1) Reverse Integer
2) Palindrome Number
3) Pow(x,n)
4) Subsets
5) Subsets II
6) Fraction to Recurring Decimal [Google]
7) Excel Sheet Column Number
8) Excel Sheet Column Title
9) Factorial Trailing Zeroes
10) Happy Number
11) Count Primes
12) Plus One
13) Divide Two Integers
14) Multiply Strings
15) Max Points on a Line
16) Product of Array Except Self