Code Test

Code test on tons of simple algorithm questions.

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Difficulty	No.	Challenge Name	Tag	Description
	0	single number	bitwise	find num appears only once.
	1	single number ii	bitwise	find num appears once while others 3s.
	2	3 sum	sort, hash	pick 3 nums : x + y + z = target.
	3	3 sum closest	sort, hash	pick 3 sums to min abs(target-(x+y+z)).
	4	4 sum	sort, hash	pick 4 nums: x + y + z + d = target.
	5	tree preorder	tree	preorder traversal of bianry tree.
	6	LRU cache	hash	implement a LRU cache.
	7	add binary	bitwise	add two binaries.
	8	add 2 numbers	string	add two numbers.
	9	anagrams	hash	group anagrams.
	10	atoi	string	string to integer.
	11	combination sum	backtrack	find sum of several nums = target
	12	jump game	dynamic	determine if can reach the end.
	13	spiral matrix	recursion	convert matrix to spiral order.
	14	maximum subarray	dynamic	maximum sum of contiguous subarray.
	15	combination sum ii	backtrack, hash	similar to 11, avoid duplicates.
	16	multiply strings	string	return multiplication of the numbers as a string.
	17	pow(x,n)	divide & conquer
	18	permutations	backtrack	return all permutations
	19	rotate image	array	rotate matrix by 90 degrees.(clockwise, inplace)
	20	2 sum	sort, hash	find x + y = target, return index
	21	permutations ii	backtrack & visited	avoid duplicates
	22	balanced binary tree	tree	balanced?
	23	best time stock	dynamic	track the minimum price and find the largest difference
	24	best time stock ii	dynamic	key:: stock[i+n] - stock[i] = stock[i+n] - stock[i+n-k] + stock[i+n-k] - stock[i]
	25	betting result
	26	binary search	divide&conquer
✮	27	BST iterator	tree
	28	tree inorder traversal	tree
✮	29	wildcard matching	string	pattern matching of '?' and *
	30	n queens	backtrack	find all valid sol in NxN board.
	31	trapping rain water	dynamic	find left highest and right highest for each vol.
	32	first missing positive	partition	O(n) time, O(1) space,
	33	n queens ii	backtrack	count valids
	34	merge intervals	dynamic
	35	insert intervals	dynamic
	36	sudoku solver	backtrack	disgusting problem, use solution of valid sudoku
✮✮	37	valid number	string	many cases to consider
✮✮	38	edit distance	dynamic	insert, del, replace a char each distance, related to No.40
	39	search in rotated sorted array	divide & conquer	careful with cases
	40	minimum window substring	dynamic	two pointers, s[slow:fast] always have all the chars needed.
	41	longest valid parentheses	dynamic
	42	candy	dynamic adjustment	adjust the candies whenever there is a trough
	43	dungeon game	dynamic	table filling
	44	word break	backtrack & optimization	cut off unnecessary leafs
	45	valid parentheses	stack & string
	46	merge sorted array	linear time	tail -> head merge
	47	excel sheet column number
	48	intersection of two linked lists	O(n) time O(1) space
	49	zigzag conversion	O(n) time math solution	dont touch this question!
	50	min stack	stack	retrive by maintaining another stack for mins
	51	reverse integer	careful with overflow
	52	string to integer	careful with white spaces, -/+ , illegal chars and overflow
	53	palindrome number	O(1) space -> do not use recursion (use stack space, which is not constant)
	54	roman to integer	(／‵Д′)／~ ╧╧
	55	longest common prefix	string
	56	remove nth node from end of the list	linkedlist	fast and slow pointer
	57	pascal triangle ii	pure math problem c30, c31, c32, c33, be aware of the ultimate optimization.
	58	pascal triangle	read 57.
	59	factorial trailing zeroes	find all those 5s
	60	path sum	tree, recursion
	61	minimum depth tree	tree, recursion
	62	binary tree level order traversal ii	queue
	63	maximum depth of binary tree	recursion, make it concise
	64	binary tree level order traversal	queue
	65	symmetric tree	recursion	recursion
	66	same tree	recursion	isSameTree(p.left, q.left)&&isSameTree(p.right, q.right)
	67	remove duplicates from sorted array	two pointer
	68	merge sorted array	from end to start, use empty space
	69	remove element	two pointers
	70	implement strStr()	brute O(m*n), optimized KMP solution O(m+n)
	71	remove duplicates from sorted list	O(n)
	72	climb stairs	dynamic
	73	plus one
	74	valid soduku	check every row, column and 3x3 boxes.
	75	add binary	string
	76	merge two sorted lists	linked list, basic op of merge sort, either merge two into one and splice ont to another
	77	length of last word	return the length of last word of a string.	make it as neat as possible.
	78	gray code	find the patterns lie within the sequence
	79	find peak element	binary search	O(logn)
	80	gas station	dynamic	maintain a sum var, if it becomes negative, which means current start is not valid, start a new route in the next station.
✮	81	clone graph	queue, graph, breath-first traversal	use nodeMap to mark the connectivity between graph node and cloned node
	82	palindrome partitioning	backtrack	cutoff unnecessary branches.
	83	surrounded regions	mark 0s on the edge, then find those 0s connected to them.
	84	sum root to leaf	recursion	path = path*10 + root.val
	85	3sum closest	similar to 3sum
	86	4sum	preprocess the sum of all pais and store in dict for lookup can reduce time cost
✮	87	word ladder	graph	do it either dfs or iteratively bfs
	88	letter combinations of a phone number	backtrack	make a dict of num -> letters
	89	longest substring without repeating characters	dynamic	map or a lookup table, make it neat
	90	triangle	dynamic	similar to finding num of path in grid
	91	longest palindrome substring	common O(n*n) with optimization	Manacher's method, linear solution
	92	binary search tree iterator	recursion	make it neat
	93	repeated DNA sequence	ez, use map
	94	largest number	compare : int(str(s2) + str(s1)) - int(str(s1) + str(s2))
	95	populating next right pointers in each node	recursion, use the transitivity
	96	generate parenthesis	backtrack, track left_num and right_num
	97	flatten binary tree to linkedlist	bottom-up
	98	path sum ii	recursion, top-down
	99	convert sorted array to binary search tree	recursion	inorder, topdown
	100	convert sorted list to binary search tree	recursion	similar to 99
	101	construct binary tree from inorder and postorder traversal	recursion
	102	construct binary tree from preorder and inorder traversal	recursion
	103	find minimum in rotated sorted array	ans = min(ans, num[mid])
	104	binary tree zigzag order traversal	queue, track the level order
	105	maximum product subarray	dynamic	track the minimum value in case of negative
	106	reverse words in a string
	107	evaluate reverse polish notation	stack	make it neat.
	108	swap nodes in pairs	linked list	do it in space
	109	validate binary search tree	track the min and max of each path, top-down
	110	fraction to recurring decimal	a little bit tricky, need to mark the remain.
	111	unique binary search trees (count)	dynamic	select 0<=i<=n as root: arr[0:i] as the left tree, arr[i+1:n] as right tree.
	112	unique binary search trees ii	tree traversal	a little tricky to use recursion
	113	binary tree inorder traversal	iteratively
	114	binary tree preorder traversal	iteratively
	115	restore ip addresses	backtrack
	116	reverse linked list ii
	117	subsets ii	backtrack
	118	decode ways	dynamic	tricky, use three rotation vars to make the ways of s[:i-1], s[:i] and current ways, check s[i-1:i+1] (two digits) if it is valid.
✮	119	sort list	TRICKY	Note: use constant space, which means cannot use stack / recursion.
✮	120	insertion sort list	o(n^2)	optimization
✮	121	partition list	do it in space, do not create new nodes.	use two pointers.
✮	122	divide two integers
	123	remove duplicates from sorted list ii
	124	reverse linked list	both iteratively and recursively
	125	container with most water	o(n), two pointers
	126	search in rotated sorted array ii	with dupicates
	127	remove duplicates from sorted array ii	each num is allowed for existing twice.
	128	merge sorted array
	129	remove element
	130	implement strstr()
	131	remove duplicates from sorted list
	132	word search	dfs & backtrack, mark visited
	133	subsets	backtrack
	134	combinations	backtrack
✮	135	next permutation
	136	sort colors	the "holland flag" problem
	137	search a 2d matrix
	138	set matrix zeroes	do it in space, use the first column and row to save the state
	139	search for a range	binary search
	140	simplify path
	141	binary tree preorder traversal	iteratively
	142	sqrt(x)
	143	search insertion position in sorted array	binary search	find the target or position i where A[i-1]<target<A[i]
	144	reorder list	find the mid -> reverse the right section -> merge
	145	linked list cycle	check if exist
	146	linked list cycle ii	find the start position
	147	integer to roman
	148	minimum path sum	similar to unique path
	149	unique paths
	150	unique paths ii
	151	rotate list	possibility of rotating longer than length k = n - k%n
	152	permutation sequence
	153	spiral matrix ii
✮	154	word break	dynamic	use an array to mark
✮	155	recover binary search tree	recursion	O(logn) space to create a in-sys stack.
	156	best time to buy and sell stack iii
✮	157	best time to buy and sell stack iv
	158	longest valid parentheses	dynamic
	159	minimum windows substring
✮	160	merge intervals	o(nlgn)
✮	161	insert intervals	o(n)
	162	text justification
✮✮	163	largest rectangle in histogram	stack, dynamic
✮	164	maximal rectangle
✮✮	165	scramble string
✮	166	interleaving string
	167	jump game ii
	168	compare version number
	169	count and say
	170	zigzag conversion
	171	one edit distance
	172	missing range
	173	longest substring with at most two distinct chars
	174	read N characters given read 4
	175	read N characters given read 4 - call multiple times
	177	merge k sorted linked lists	use merge two sorted linked lists as operation, O(nklogk). Alternative solution: heap
	178	copy list with random pointer	try not to break the list
	179	bianry tree maximum path sum	the start and end node can be anywhere in the tree, node value can be negative.
	180	bianry tree upside down
	181	revaluate reverse polish notation
	182	coins in a line
	183	find minimum in rotated sorted array	array with duplicates
	184	reverse bits	= reverse integers
	185	rotate array	= rotate string
	186	contains duplicates ii	sorting/hashing
	187	two_sum_ii	sorting/two-pointer traversal

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Reference

For these questions, you can find their original posts in the following websites:

Feature ideas

There are two ways of doing subproblems.

Top-Down : Start solving the given problem by breaking it down. If you see that the problem has been solved already, then just return the saved answer. If it has not been solved, solve it and save the answer. This is usually easy to think of and very intuitive. This is referred to as Memoization.
Bottom-Up : Analyze the problem and see the order in which the sub-problems are solved and start solving from the trivial subproblem, up towards the given problem. In this process, it is guaranteed that the subproblems are solved before solving the problem. This is referred to as Dynamic Programming.

How to solve it

UNDERSTAND THE PROBLEM

First. You have to understand the problem.
What is the unknown? What are the data? What is the condition?
Is it possible to satisfy the condition? Is the condition sufficient to deter- mine the unknown? Or is it insufficient? Or redundant? Or contradictory?
Draw a figure. Introduce suitable notation.
Separate the various parts of the condition. Can you write them down?

DEVISING A PLAN

Second. Find the connection between the data and the unknown. You may be obliged to consider auxiliary problems if an immediate connection cannot be found. You should obtain eventually a plan of the solution.
Have you seen it before? Or have you seen the same problem in a slightly different form?
Do you know a related problem? Do you know a theorem that could be useful?
Look at the unknown! Try to think of a familiar problem having the same or a similar unknown.
Here is a problem related to yours and solved before. Could you use it? Could you use its result? Could you use its method? Should you introduce some auxiliary element in order to make its use possible?
Could you restate the problem? Could you restate it still differently? Go back to definitions.
If you cannot solve the proposed problem, try to solve first some related problem. Could you imagine a more accessible related problem? A more general problem? A more special problem? An analogous problem? Could you solve a part of the problem? Keep only a part of the condition, drop the other part; how far is the unknown then determined, how can it vary? Could you derive something useful from the data? Could you think of other data appropriate to determine the unknown? Could you change the unknown or data, or both if necessary, so that the new unknown and the new data are nearer to each other?
Did you use all the data? Did you use the whole condition? Have you taken into account all essential notions involved in the problem?

CARRYING OUT THE PLAN

Third. Carry out your plan.
Carrying out your plan of the solution, check each step. Can you see clearly that the step is correct? Can you prove that it is correct?

LOOKING BACK

Fourth. Examine the solution obtained.
Can you check the result? Can you check the argument?
Can you derive the solution differently? Can you see it at a glance? • Can you use the result, or the method, for some other problem?

Resources

tech interview handbook intro to graph and many more interesting posts

linyaoli / acm Goto Github PK

acm's Introduction

Code Test

Reference

Feature ideas

How to solve it

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