Return the number of permutations of 1 to n so that prime numbers are at prime indices (1-indexed.)
(Recall that an integer is prime if and only if it is greater than 1, and cannot be written as a product of two positive integers both smaller than it.)
Since the answer may be large, return the answer modulo 10^9 + 7.
Example 1:
Input: n = 5
Output: 12
Explanation: For example [1,2,5,4,3] is a valid permutation, but [5,2,3,4,1] is not because the prime number 5 is at index 1.
Example 2:
Input: n = 100
Output: 682289015
Constraints:
1 <= n <= 100
Solution: Permutation
Count the number of primes in range [1, n], assuming there are p primes and n – p non-primes, we can permute each group separately. ans = p! * (n – p)!
Time complexity: O(nsqrt(n)) Space complexity: O(1)
Given two strings text1 and text2, return the length of their longest common subsequence.
A subsequence of a string is a new string generated from the original string with some characters(can be none) deleted without changing the relative order of the remaining characters. (eg, “ace” is a subsequence of “abcde” while “aec” is not). A common subsequence of two strings is a subsequence that is common to both strings.
If there is no common subsequence, return 0.
Example 1:
Input: text1 = "abcde", text2 = "ace"
Output: 3
Explanation: The longest common subsequence is "ace" and its length is 3.
Example 2:
Input: text1 = "abc", text2 = "abc"
Output: 3
Explanation: The longest common subsequence is "abc" and its length is 3.
Example 3:
Input: text1 = "abc", text2 = "def"
Output: 0
Explanation: There is no such common subsequence, so the result is 0.
Constraints:
1 <= text1.length <= 1000
1 <= text2.length <= 1000
The input strings consist of lowercase English characters only.
Solution: DP
Use dp[i][j] to represent the length of longest common sub-sequence of text1[0:i] and text2[0:j] dp[i][j] = dp[i – 1][j – 1] + 1 if text1[i – 1] == text2[j – 1] else max(dp[i][j – 1], dp[i – 1][j])
Time complexity: O(mn) Space complexity: O(mn) -> O(n)
Given the head of a linked list, we repeatedly delete consecutive sequences of nodes that sum to 0until there are no such sequences.
After doing so, return the head of the final linked list. You may return any such answer.
(Note that in the examples below, all sequences are serializations of ListNode objects.)
Example 1:
Input: head = [1,2,-3,3,1]
Output: [3,1]
Note: The answer [1,2,1] would also be accepted.
Example 2:
Input: head = [1,2,3,-3,4]
Output: [1,2,4]
Example 3:
Input: head = [1,2,3,-3,-2]
Output: [1]
Constraints:
The given linked list will contain between 1 and 1000 nodes.
Each node in the linked list has -1000 <= node.val <= 1000.
Solution: HashTable
Similar to target sum = 0, use a hashtable to store the first ListNode* that has a given prefix sum. Whenever the same prefix sum occurs, skip all the elements between the first occurrence and current one, e.g. first_sum_x.next = curr_sum_x.next
You have an infinite number of stacks arranged in a row and numbered (left to right) from 0, each of the stacks has the same maximum capacity.
Implement the DinnerPlates class:
DinnerPlates(int capacity) Initializes the object with the maximum capacity of the stacks.
void push(int val) pushes the given positive integer val into the leftmost stack with size less than capacity.
int pop() returns the value at the top of the rightmost non-empty stack and removes it from that stack, and returns -1 if all stacks are empty.
int popAtStack(int index) returns the value at the top of the stack with the given index and removes it from that stack, and returns -1 if the stack with that given index is empty.
Explanation:
DinnerPlates D = DinnerPlates(2); // Initialize with capacity = 2
D.push(1);
D.push(2);
D.push(3);
D.push(4);
D.push(5); // The stacks are now: 2 4
1 3 5
﹈ ﹈ ﹈
D.popAtStack(0); // Returns 2. The stacks are now: 4
1 3 5
﹈ ﹈ ﹈
D.push(20); // The stacks are now: 20 4
1 3 5
﹈ ﹈ ﹈
D.push(21); // The stacks are now: 20 4 21
1 3 5
﹈ ﹈ ﹈
D.popAtStack(0); // Returns 20. The stacks are now: 4 21
1 3 5
﹈ ﹈ ﹈
D.popAtStack(2); // Returns 21. The stacks are now: 4
1 3 5
﹈ ﹈ ﹈
D.pop() // Returns 5. The stacks are now: 4
1 3
﹈ ﹈
D.pop() // Returns 4. The stacks are now: 1 3
﹈ ﹈
D.pop() // Returns 3. The stacks are now: 1
﹈
D.pop() // Returns 1. There are no stacks.
D.pop() // Returns -1. There are still no stacks.
Constraints:
1 <= capacity <= 20000
1 <= val <= 20000
0 <= index <= 100000
At most 200000 calls will be made to push, pop, and popAtStack.
Solution: Array of stacks + TreeSet
Store all the stacks in an array, and store the indices of all free stacks in a tree set. 1. push(): find the first free stack and push onto it, if it becomes full, remove it from free set. 2. pop(): pop element from the last stack by calling popAtIndex(stacks.size()-1). 3. popAtIndex(): pop element from given index 3.1 add it to free set if it was full 3.2 remove it from free set if it becomes empty 3.2.1 remove it from stack array if it is the last stack
Time complexity: Push: O(logn) Pop: O(logn) PopAtIndex: O(logn)