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88 | Merge Sorted Array | impl Solution { pub fn merge(nums1: &mut Vec<i32>, m: i32, nums2: &mut Vec<i32>, n: i32) { } } | You are given two integer arrays `nums1` and `nums2`, sorted in **non-decreasing order**, and two integers `m` and `n`, representing the number of elements in `nums1` and `nums2` respectively.
**Merge** `nums1` and `nums2` into a single array sorted in **non-decreasing order**.
The final sorted array should not be returned by the function, but instead be _stored inside the array_ `nums1`. To accommodate this, `nums1` has a length of `m + n`, where the first `m` elements denote the elements that should be merged, and the last `n` elements are set to `0` and should be ignored. `nums2` has a length of `n`.
**Example 1:**
**Input:** nums1 = \[1,2,3,0,0,0\], m = 3, nums2 = \[2,5,6\], n = 3
**Output:** \[1,2,2,3,5,6\]
**Explanation:** The arrays we are merging are \[1,2,3\] and \[2,5,6\].
The result of the merge is \[1,2,2,3,5,6\] with the underlined elements coming from nums1.
**Example 2:**
**Input:** nums1 = \[1\], m = 1, nums2 = \[\], n = 0
**Output:** \[1\]
**Explanation:** The arrays we are merging are \[1\] and \[\].
The result of the merge is \[1\].
**Example 3:**
**Input:** nums1 = \[0\], m = 0, nums2 = \[1\], n = 1
**Output:** \[1\]
**Explanation:** The arrays we are merging are \[\] and \[1\].
The result of the merge is \[1\].
Note that because m = 0, there are no elements in nums1. The 0 is only there to ensure the merge result can fit in nums1.
**Constraints:**
* `nums1.length == m + n`
* `nums2.length == n`
* `0 <= m, n <= 200`
* `1 <= m + n <= 200`
* `-109 <= nums1[i], nums2[j] <= 109`
**Follow up:** Can you come up with an algorithm that runs in `O(m + n)` time? |
27 | Remove Element | impl Solution { pub fn remove_element(nums: &mut Vec<i32>, val: i32) -> i32 { } } | Given an integer array `nums` and an integer `val`, remove all occurrences of `val` in `nums` [**in-place**](https://en.wikipedia.org/wiki/In-place_algorithm). The order of the elements may be changed. Then return _the number of elements in_ `nums` _which are not equal to_ `val`.
Consider the number of elements in `nums` which are not equal to `val` be `k`, to get accepted, you need to do the following things:
* Change the array `nums` such that the first `k` elements of `nums` contain the elements which are not equal to `val`. The remaining elements of `nums` are not important as well as the size of `nums`.
* Return `k`.
**Custom Judge:**
The judge will test your solution with the following code:
int\[\] nums = \[...\]; // Input array
int val = ...; // Value to remove
int\[\] expectedNums = \[...\]; // The expected answer with correct length.
// It is sorted with no values equaling val.
int k = removeElement(nums, val); // Calls your implementation
assert k == expectedNums.length;
sort(nums, 0, k); // Sort the first k elements of nums
for (int i = 0; i < actualLength; i++) {
assert nums\[i\] == expectedNums\[i\];
}
If all assertions pass, then your solution will be **accepted**.
**Example 1:**
**Input:** nums = \[3,2,2,3\], val = 3
**Output:** 2, nums = \[2,2,\_,\_\]
**Explanation:** Your function should return k = 2, with the first two elements of nums being 2.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Example 2:**
**Input:** nums = \[0,1,2,2,3,0,4,2\], val = 2
**Output:** 5, nums = \[0,1,4,0,3,\_,\_,\_\]
**Explanation:** Your function should return k = 5, with the first five elements of nums containing 0, 0, 1, 3, and 4.
Note that the five elements can be returned in any order.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Constraints:**
* `0 <= nums.length <= 100`
* `0 <= nums[i] <= 50`
* `0 <= val <= 100` |
26 | Remove Duplicates from Sorted Array | impl Solution { pub fn remove_duplicates(nums: &mut Vec<i32>) -> i32 { } } | Given an integer array `nums` sorted in **non-decreasing order**, remove the duplicates [**in-place**](https://en.wikipedia.org/wiki/In-place_algorithm) such that each unique element appears only **once**. The **relative order** of the elements should be kept the **same**. Then return _the number of unique elements in_ `nums`.
Consider the number of unique elements of `nums` be `k`, to get accepted, you need to do the following things:
* Change the array `nums` such that the first `k` elements of `nums` contain the unique elements in the order they were present in `nums` initially. The remaining elements of `nums` are not important as well as the size of `nums`.
* Return `k`.
**Custom Judge:**
The judge will test your solution with the following code:
int\[\] nums = \[...\]; // Input array
int\[\] expectedNums = \[...\]; // The expected answer with correct length
int k = removeDuplicates(nums); // Calls your implementation
assert k == expectedNums.length;
for (int i = 0; i < k; i++) {
assert nums\[i\] == expectedNums\[i\];
}
If all assertions pass, then your solution will be **accepted**.
**Example 1:**
**Input:** nums = \[1,1,2\]
**Output:** 2, nums = \[1,2,\_\]
**Explanation:** Your function should return k = 2, with the first two elements of nums being 1 and 2 respectively.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Example 2:**
**Input:** nums = \[0,0,1,1,1,2,2,3,3,4\]
**Output:** 5, nums = \[0,1,2,3,4,\_,\_,\_,\_,\_\]
**Explanation:** Your function should return k = 5, with the first five elements of nums being 0, 1, 2, 3, and 4 respectively.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Constraints:**
* `1 <= nums.length <= 3 * 104`
* `-100 <= nums[i] <= 100`
* `nums` is sorted in **non-decreasing** order. |
80 | Remove Duplicates from Sorted Array II | impl Solution { pub fn remove_duplicates(nums: &mut Vec<i32>) -> i32 { } } | Given an integer array `nums` sorted in **non-decreasing order**, remove some duplicates [**in-place**](https://en.wikipedia.org/wiki/In-place_algorithm) such that each unique element appears **at most twice**. The **relative order** of the elements should be kept the **same**.
Since it is impossible to change the length of the array in some languages, you must instead have the result be placed in the **first part** of the array `nums`. More formally, if there are `k` elements after removing the duplicates, then the first `k` elements of `nums` should hold the final result. It does not matter what you leave beyond the first `k` elements.
Return `k` _after placing the final result in the first_ `k` _slots of_ `nums`.
Do **not** allocate extra space for another array. You must do this by **modifying the input array [in-place](https://en.wikipedia.org/wiki/In-place_algorithm)** with O(1) extra memory.
**Custom Judge:**
The judge will test your solution with the following code:
int\[\] nums = \[...\]; // Input array
int\[\] expectedNums = \[...\]; // The expected answer with correct length
int k = removeDuplicates(nums); // Calls your implementation
assert k == expectedNums.length;
for (int i = 0; i < k; i++) {
assert nums\[i\] == expectedNums\[i\];
}
If all assertions pass, then your solution will be **accepted**.
**Example 1:**
**Input:** nums = \[1,1,1,2,2,3\]
**Output:** 5, nums = \[1,1,2,2,3,\_\]
**Explanation:** Your function should return k = 5, with the first five elements of nums being 1, 1, 2, 2 and 3 respectively.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Example 2:**
**Input:** nums = \[0,0,1,1,1,1,2,3,3\]
**Output:** 7, nums = \[0,0,1,1,2,3,3,\_,\_\]
**Explanation:** Your function should return k = 7, with the first seven elements of nums being 0, 0, 1, 1, 2, 3 and 3 respectively.
It does not matter what you leave beyond the returned k (hence they are underscores).
**Constraints:**
* `1 <= nums.length <= 3 * 104`
* `-104 <= nums[i] <= 104`
* `nums` is sorted in **non-decreasing** order. |
169 | Majority Element | impl Solution { pub fn majority_element(nums: Vec<i32>) -> i32 { } } | Given an array `nums` of size `n`, return _the majority element_.
The majority element is the element that appears more than `⌊n / 2⌋` times. You may assume that the majority element always exists in the array.
**Example 1:**
**Input:** nums = \[3,2,3\]
**Output:** 3
**Example 2:**
**Input:** nums = \[2,2,1,1,1,2,2\]
**Output:** 2
**Constraints:**
* `n == nums.length`
* `1 <= n <= 5 * 104`
* `-109 <= nums[i] <= 109`
**Follow-up:** Could you solve the problem in linear time and in `O(1)` space? |
189 | Rotate Array | impl Solution { pub fn rotate(nums: &mut Vec<i32>, k: i32) { } } | Given an integer array `nums`, rotate the array to the right by `k` steps, where `k` is non-negative.
**Example 1:**
**Input:** nums = \[1,2,3,4,5,6,7\], k = 3
**Output:** \[5,6,7,1,2,3,4\]
**Explanation:**
rotate 1 steps to the right: \[7,1,2,3,4,5,6\]
rotate 2 steps to the right: \[6,7,1,2,3,4,5\]
rotate 3 steps to the right: \[5,6,7,1,2,3,4\]
**Example 2:**
**Input:** nums = \[-1,-100,3,99\], k = 2
**Output:** \[3,99,-1,-100\]
**Explanation:**
rotate 1 steps to the right: \[99,-1,-100,3\]
rotate 2 steps to the right: \[3,99,-1,-100\]
**Constraints:**
* `1 <= nums.length <= 105`
* `-231 <= nums[i] <= 231 - 1`
* `0 <= k <= 105`
**Follow up:**
* Try to come up with as many solutions as you can. There are at least **three** different ways to solve this problem.
* Could you do it in-place with `O(1)` extra space? |
121 | Best Time to Buy and Sell Stock | impl Solution { pub fn max_profit(prices: Vec<i32>) -> i32 { } } | You are given an array `prices` where `prices[i]` is the price of a given stock on the `ith` day.
You want to maximize your profit by choosing a **single day** to buy one stock and choosing a **different day in the future** to sell that stock.
Return _the maximum profit you can achieve from this transaction_. If you cannot achieve any profit, return `0`.
**Example 1:**
**Input:** prices = \[7,1,5,3,6,4\]
**Output:** 5
**Explanation:** Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell.
**Example 2:**
**Input:** prices = \[7,6,4,3,1\]
**Output:** 0
**Explanation:** In this case, no transactions are done and the max profit = 0.
**Constraints:**
* `1 <= prices.length <= 105`
* `0 <= prices[i] <= 104` |
122 | Best Time to Buy and Sell Stock II | impl Solution { pub fn max_profit(prices: Vec<i32>) -> i32 { } } | You are given an integer array `prices` where `prices[i]` is the price of a given stock on the `ith` day.
On each day, you may decide to buy and/or sell the stock. You can only hold **at most one** share of the stock at any time. However, you can buy it then immediately sell it on the **same day**.
Find and return _the **maximum** profit you can achieve_.
**Example 1:**
**Input:** prices = \[7,1,5,3,6,4\]
**Output:** 7
**Explanation:** Buy on day 2 (price = 1) and sell on day 3 (price = 5), profit = 5-1 = 4.
Then buy on day 4 (price = 3) and sell on day 5 (price = 6), profit = 6-3 = 3.
Total profit is 4 + 3 = 7.
**Example 2:**
**Input:** prices = \[1,2,3,4,5\]
**Output:** 4
**Explanation:** Buy on day 1 (price = 1) and sell on day 5 (price = 5), profit = 5-1 = 4.
Total profit is 4.
**Example 3:**
**Input:** prices = \[7,6,4,3,1\]
**Output:** 0
**Explanation:** There is no way to make a positive profit, so we never buy the stock to achieve the maximum profit of 0.
**Constraints:**
* `1 <= prices.length <= 3 * 104`
* `0 <= prices[i] <= 104` |
55 | Jump Game | impl Solution { pub fn can_jump(nums: Vec<i32>) -> bool { } } | You are given an integer array `nums`. You are initially positioned at the array's **first index**, and each element in the array represents your maximum jump length at that position.
Return `true` _if you can reach the last index, or_ `false` _otherwise_.
**Example 1:**
**Input:** nums = \[2,3,1,1,4\]
**Output:** true
**Explanation:** Jump 1 step from index 0 to 1, then 3 steps to the last index.
**Example 2:**
**Input:** nums = \[3,2,1,0,4\]
**Output:** false
**Explanation:** You will always arrive at index 3 no matter what. Its maximum jump length is 0, which makes it impossible to reach the last index.
**Constraints:**
* `1 <= nums.length <= 104`
* `0 <= nums[i] <= 105` |
45 | Jump Game II | impl Solution { pub fn jump(nums: Vec<i32>) -> i32 { } } | You are given a **0-indexed** array of integers `nums` of length `n`. You are initially positioned at `nums[0]`.
Each element `nums[i]` represents the maximum length of a forward jump from index `i`. In other words, if you are at `nums[i]`, you can jump to any `nums[i + j]` where:
* `0 <= j <= nums[i]` and
* `i + j < n`
Return _the minimum number of jumps to reach_ `nums[n - 1]`. The test cases are generated such that you can reach `nums[n - 1]`.
**Example 1:**
**Input:** nums = \[2,3,1,1,4\]
**Output:** 2
**Explanation:** The minimum number of jumps to reach the last index is 2. Jump 1 step from index 0 to 1, then 3 steps to the last index.
**Example 2:**
**Input:** nums = \[2,3,0,1,4\]
**Output:** 2
**Constraints:**
* `1 <= nums.length <= 104`
* `0 <= nums[i] <= 1000`
* It's guaranteed that you can reach `nums[n - 1]`. |
274 | H-Index | impl Solution { pub fn h_index(citations: Vec<i32>) -> i32 { } } | Given an array of integers `citations` where `citations[i]` is the number of citations a researcher received for their `ith` paper, return _the researcher's h-index_.
According to the [definition of h-index on Wikipedia](https://en.wikipedia.org/wiki/H-index): The h-index is defined as the maximum value of `h` such that the given researcher has published at least `h` papers that have each been cited at least `h` times.
**Example 1:**
**Input:** citations = \[3,0,6,1,5\]
**Output:** 3
**Explanation:** \[3,0,6,1,5\] means the researcher has 5 papers in total and each of them had received 3, 0, 6, 1, 5 citations respectively.
Since the researcher has 3 papers with at least 3 citations each and the remaining two with no more than 3 citations each, their h-index is 3.
**Example 2:**
**Input:** citations = \[1,3,1\]
**Output:** 1
**Constraints:**
* `n == citations.length`
* `1 <= n <= 5000`
* `0 <= citations[i] <= 1000` |
380 | Insert Delete GetRandom O(1) | struct RandomizedSet { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl RandomizedSet { fn new() -> Self { } fn insert(&self, val: i32) -> bool { } fn remove(&self, val: i32) -> bool { } fn get_random(&self) -> i32 { } } /** * Your RandomizedSet object will be instantiated and called as such: * let obj = RandomizedSet::new(); * let ret_1: bool = obj.insert(val); * let ret_2: bool = obj.remove(val); * let ret_3: i32 = obj.get_random(); */ | Implement the `RandomizedSet` class:
* `RandomizedSet()` Initializes the `RandomizedSet` object.
* `bool insert(int val)` Inserts an item `val` into the set if not present. Returns `true` if the item was not present, `false` otherwise.
* `bool remove(int val)` Removes an item `val` from the set if present. Returns `true` if the item was present, `false` otherwise.
* `int getRandom()` Returns a random element from the current set of elements (it's guaranteed that at least one element exists when this method is called). Each element must have the **same probability** of being returned.
You must implement the functions of the class such that each function works in **average** `O(1)` time complexity.
**Example 1:**
**Input**
\[ "RandomizedSet ", "insert ", "remove ", "insert ", "getRandom ", "remove ", "insert ", "getRandom "\]
\[\[\], \[1\], \[2\], \[2\], \[\], \[1\], \[2\], \[\]\]
**Output**
\[null, true, false, true, 2, true, false, 2\]
**Explanation**
RandomizedSet randomizedSet = new RandomizedSet();
randomizedSet.insert(1); // Inserts 1 to the set. Returns true as 1 was inserted successfully.
randomizedSet.remove(2); // Returns false as 2 does not exist in the set.
randomizedSet.insert(2); // Inserts 2 to the set, returns true. Set now contains \[1,2\].
randomizedSet.getRandom(); // getRandom() should return either 1 or 2 randomly.
randomizedSet.remove(1); // Removes 1 from the set, returns true. Set now contains \[2\].
randomizedSet.insert(2); // 2 was already in the set, so return false.
randomizedSet.getRandom(); // Since 2 is the only number in the set, getRandom() will always return 2.
**Constraints:**
* `-231 <= val <= 231 - 1`
* At most `2 *` `105` calls will be made to `insert`, `remove`, and `getRandom`.
* There will be **at least one** element in the data structure when `getRandom` is called. |
238 | Product of Array Except Self | impl Solution { pub fn product_except_self(nums: Vec<i32>) -> Vec<i32> { } } | Given an integer array `nums`, return _an array_ `answer` _such that_ `answer[i]` _is equal to the product of all the elements of_ `nums` _except_ `nums[i]`.
The product of any prefix or suffix of `nums` is **guaranteed** to fit in a **32-bit** integer.
You must write an algorithm that runs in `O(n)` time and without using the division operation.
**Example 1:**
**Input:** nums = \[1,2,3,4\]
**Output:** \[24,12,8,6\]
**Example 2:**
**Input:** nums = \[-1,1,0,-3,3\]
**Output:** \[0,0,9,0,0\]
**Constraints:**
* `2 <= nums.length <= 105`
* `-30 <= nums[i] <= 30`
* The product of any prefix or suffix of `nums` is **guaranteed** to fit in a **32-bit** integer.
**Follow up:** Can you solve the problem in `O(1)` extra space complexity? (The output array **does not** count as extra space for space complexity analysis.) |
134 | Gas Station | impl Solution { pub fn can_complete_circuit(gas: Vec<i32>, cost: Vec<i32>) -> i32 { } } | There are `n` gas stations along a circular route, where the amount of gas at the `ith` station is `gas[i]`.
You have a car with an unlimited gas tank and it costs `cost[i]` of gas to travel from the `ith` station to its next `(i + 1)th` station. You begin the journey with an empty tank at one of the gas stations.
Given two integer arrays `gas` and `cost`, return _the starting gas station's index if you can travel around the circuit once in the clockwise direction, otherwise return_ `-1`. If there exists a solution, it is **guaranteed** to be **unique**
**Example 1:**
**Input:** gas = \[1,2,3,4,5\], cost = \[3,4,5,1,2\]
**Output:** 3
**Explanation:**
Start at station 3 (index 3) and fill up with 4 unit of gas. Your tank = 0 + 4 = 4
Travel to station 4. Your tank = 4 - 1 + 5 = 8
Travel to station 0. Your tank = 8 - 2 + 1 = 7
Travel to station 1. Your tank = 7 - 3 + 2 = 6
Travel to station 2. Your tank = 6 - 4 + 3 = 5
Travel to station 3. The cost is 5. Your gas is just enough to travel back to station 3.
Therefore, return 3 as the starting index.
**Example 2:**
**Input:** gas = \[2,3,4\], cost = \[3,4,3\]
**Output:** -1
**Explanation:**
You can't start at station 0 or 1, as there is not enough gas to travel to the next station.
Let's start at station 2 and fill up with 4 unit of gas. Your tank = 0 + 4 = 4
Travel to station 0. Your tank = 4 - 3 + 2 = 3
Travel to station 1. Your tank = 3 - 3 + 3 = 3
You cannot travel back to station 2, as it requires 4 unit of gas but you only have 3.
Therefore, you can't travel around the circuit once no matter where you start.
**Constraints:**
* `n == gas.length == cost.length`
* `1 <= n <= 105`
* `0 <= gas[i], cost[i] <= 104` |
135 | Candy | impl Solution { pub fn candy(ratings: Vec<i32>) -> i32 { } } | There are `n` children standing in a line. Each child is assigned a rating value given in the integer array `ratings`.
You are giving candies to these children subjected to the following requirements:
* Each child must have at least one candy.
* Children with a higher rating get more candies than their neighbors.
Return _the minimum number of candies you need to have to distribute the candies to the children_.
**Example 1:**
**Input:** ratings = \[1,0,2\]
**Output:** 5
**Explanation:** You can allocate to the first, second and third child with 2, 1, 2 candies respectively.
**Example 2:**
**Input:** ratings = \[1,2,2\]
**Output:** 4
**Explanation:** You can allocate to the first, second and third child with 1, 2, 1 candies respectively.
The third child gets 1 candy because it satisfies the above two conditions.
**Constraints:**
* `n == ratings.length`
* `1 <= n <= 2 * 104`
* `0 <= ratings[i] <= 2 * 104` |
42 | Trapping Rain Water | impl Solution { pub fn trap(height: Vec<i32>) -> i32 { } } | Given `n` non-negative integers representing an elevation map where the width of each bar is `1`, compute how much water it can trap after raining.
**Example 1:**
**Input:** height = \[0,1,0,2,1,0,1,3,2,1,2,1\]
**Output:** 6
**Explanation:** The above elevation map (black section) is represented by array \[0,1,0,2,1,0,1,3,2,1,2,1\]. In this case, 6 units of rain water (blue section) are being trapped.
**Example 2:**
**Input:** height = \[4,2,0,3,2,5\]
**Output:** 9
**Constraints:**
* `n == height.length`
* `1 <= n <= 2 * 104`
* `0 <= height[i] <= 105` |
13 | Roman to Integer | impl Solution { pub fn roman_to_int(s: String) -> i32 { } } | Roman numerals are represented by seven different symbols: `I`, `V`, `X`, `L`, `C`, `D` and `M`.
**Symbol** **Value**
I 1
V 5
X 10
L 50
C 100
D 500
M 1000
For example, `2` is written as `II` in Roman numeral, just two ones added together. `12` is written as `XII`, which is simply `X + II`. The number `27` is written as `XXVII`, which is `XX + V + II`.
Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not `IIII`. Instead, the number four is written as `IV`. Because the one is before the five we subtract it making four. The same principle applies to the number nine, which is written as `IX`. There are six instances where subtraction is used:
* `I` can be placed before `V` (5) and `X` (10) to make 4 and 9.
* `X` can be placed before `L` (50) and `C` (100) to make 40 and 90.
* `C` can be placed before `D` (500) and `M` (1000) to make 400 and 900.
Given a roman numeral, convert it to an integer.
**Example 1:**
**Input:** s = "III "
**Output:** 3
**Explanation:** III = 3.
**Example 2:**
**Input:** s = "LVIII "
**Output:** 58
**Explanation:** L = 50, V= 5, III = 3.
**Example 3:**
**Input:** s = "MCMXCIV "
**Output:** 1994
**Explanation:** M = 1000, CM = 900, XC = 90 and IV = 4.
**Constraints:**
* `1 <= s.length <= 15`
* `s` contains only the characters `('I', 'V', 'X', 'L', 'C', 'D', 'M')`.
* It is **guaranteed** that `s` is a valid roman numeral in the range `[1, 3999]`. |
12 | Integer to Roman | impl Solution { pub fn int_to_roman(num: i32) -> String { } } | Roman numerals are represented by seven different symbols: `I`, `V`, `X`, `L`, `C`, `D` and `M`.
**Symbol** **Value**
I 1
V 5
X 10
L 50
C 100
D 500
M 1000
For example, `2` is written as `II` in Roman numeral, just two one's added together. `12` is written as `XII`, which is simply `X + II`. The number `27` is written as `XXVII`, which is `XX + V + II`.
Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not `IIII`. Instead, the number four is written as `IV`. Because the one is before the five we subtract it making four. The same principle applies to the number nine, which is written as `IX`. There are six instances where subtraction is used:
* `I` can be placed before `V` (5) and `X` (10) to make 4 and 9.
* `X` can be placed before `L` (50) and `C` (100) to make 40 and 90.
* `C` can be placed before `D` (500) and `M` (1000) to make 400 and 900.
Given an integer, convert it to a roman numeral.
**Example 1:**
**Input:** num = 3
**Output:** "III "
**Explanation:** 3 is represented as 3 ones.
**Example 2:**
**Input:** num = 58
**Output:** "LVIII "
**Explanation:** L = 50, V = 5, III = 3.
**Example 3:**
**Input:** num = 1994
**Output:** "MCMXCIV "
**Explanation:** M = 1000, CM = 900, XC = 90 and IV = 4.
**Constraints:**
* `1 <= num <= 3999` |
58 | Length of Last Word | impl Solution { pub fn length_of_last_word(s: String) -> i32 { } } | Given a string `s` consisting of words and spaces, return _the length of the **last** word in the string._
A **word** is a maximal substring consisting of non-space characters only.
**Example 1:**
**Input:** s = "Hello World "
**Output:** 5
**Explanation:** The last word is "World " with length 5.
**Example 2:**
**Input:** s = " fly me to the moon "
**Output:** 4
**Explanation:** The last word is "moon " with length 4.
**Example 3:**
**Input:** s = "luffy is still joyboy "
**Output:** 6
**Explanation:** The last word is "joyboy " with length 6.
**Constraints:**
* `1 <= s.length <= 104`
* `s` consists of only English letters and spaces `' '`.
* There will be at least one word in `s`. |
14 | Longest Common Prefix | impl Solution { pub fn longest_common_prefix(strs: Vec<String>) -> String { } } | Write a function to find the longest common prefix string amongst an array of strings.
If there is no common prefix, return an empty string `" "`.
**Example 1:**
**Input:** strs = \[ "flower ", "flow ", "flight "\]
**Output:** "fl "
**Example 2:**
**Input:** strs = \[ "dog ", "racecar ", "car "\]
**Output:** " "
**Explanation:** There is no common prefix among the input strings.
**Constraints:**
* `1 <= strs.length <= 200`
* `0 <= strs[i].length <= 200`
* `strs[i]` consists of only lowercase English letters. |
151 | Reverse Words in a String | impl Solution { pub fn reverse_words(s: String) -> String { } } | Given an input string `s`, reverse the order of the **words**.
A **word** is defined as a sequence of non-space characters. The **words** in `s` will be separated by at least one space.
Return _a string of the words in reverse order concatenated by a single space._
**Note** that `s` may contain leading or trailing spaces or multiple spaces between two words. The returned string should only have a single space separating the words. Do not include any extra spaces.
**Example 1:**
**Input:** s = "the sky is blue "
**Output:** "blue is sky the "
**Example 2:**
**Input:** s = " hello world "
**Output:** "world hello "
**Explanation:** Your reversed string should not contain leading or trailing spaces.
**Example 3:**
**Input:** s = "a good example "
**Output:** "example good a "
**Explanation:** You need to reduce multiple spaces between two words to a single space in the reversed string.
**Constraints:**
* `1 <= s.length <= 104`
* `s` contains English letters (upper-case and lower-case), digits, and spaces `' '`.
* There is **at least one** word in `s`.
**Follow-up:** If the string data type is mutable in your language, can you solve it **in-place** with `O(1)` extra space? |
6 | Zigzag Conversion | impl Solution { pub fn convert(s: String, num_rows: i32) -> String { } } | The string `"PAYPALISHIRING "` is written in a zigzag pattern on a given number of rows like this: (you may want to display this pattern in a fixed font for better legibility)
P A H N
A P L S I I G
Y I R
And then read line by line: `"PAHNAPLSIIGYIR "`
Write the code that will take a string and make this conversion given a number of rows:
string convert(string s, int numRows);
**Example 1:**
**Input:** s = "PAYPALISHIRING ", numRows = 3
**Output:** "PAHNAPLSIIGYIR "
**Example 2:**
**Input:** s = "PAYPALISHIRING ", numRows = 4
**Output:** "PINALSIGYAHRPI "
**Explanation:**
P I N
A L S I G
Y A H R
P I
**Example 3:**
**Input:** s = "A ", numRows = 1
**Output:** "A "
**Constraints:**
* `1 <= s.length <= 1000`
* `s` consists of English letters (lower-case and upper-case), `','` and `'.'`.
* `1 <= numRows <= 1000` |
28 | Find the Index of the First Occurrence in a String | impl Solution { pub fn str_str(haystack: String, needle: String) -> i32 { } } | Given two strings `needle` and `haystack`, return the index of the first occurrence of `needle` in `haystack`, or `-1` if `needle` is not part of `haystack`.
**Example 1:**
**Input:** haystack = "sadbutsad ", needle = "sad "
**Output:** 0
**Explanation:** "sad " occurs at index 0 and 6.
The first occurrence is at index 0, so we return 0.
**Example 2:**
**Input:** haystack = "leetcode ", needle = "leeto "
**Output:** -1
**Explanation:** "leeto " did not occur in "leetcode ", so we return -1.
**Constraints:**
* `1 <= haystack.length, needle.length <= 104`
* `haystack` and `needle` consist of only lowercase English characters. |
68 | Text Justification | impl Solution { pub fn full_justify(words: Vec<String>, max_width: i32) -> Vec<String> { } } | Given an array of strings `words` and a width `maxWidth`, format the text such that each line has exactly `maxWidth` characters and is fully (left and right) justified.
You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly `maxWidth` characters.
Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line does not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
For the last line of text, it should be left-justified, and no extra space is inserted between words.
**Note:**
* A word is defined as a character sequence consisting of non-space characters only.
* Each word's length is guaranteed to be greater than `0` and not exceed `maxWidth`.
* The input array `words` contains at least one word.
**Example 1:**
**Input:** words = \[ "This ", "is ", "an ", "example ", "of ", "text ", "justification. "\], maxWidth = 16
**Output:**
\[
"This is an ",
"example of text ",
"justification. "
\]
**Example 2:**
**Input:** words = \[ "What ", "must ", "be ", "acknowledgment ", "shall ", "be "\], maxWidth = 16
**Output:**
\[
"What must be ",
"acknowledgment ",
"shall be "
\]
**Explanation:** Note that the last line is "shall be " instead of "shall be ", because the last line must be left-justified instead of fully-justified.
Note that the second line is also left-justified because it contains only one word.
**Example 3:**
**Input:** words = \[ "Science ", "is ", "what ", "we ", "understand ", "well ", "enough ", "to ", "explain ", "to ", "a ", "computer. ", "Art ", "is ", "everything ", "else ", "we ", "do "\], maxWidth = 20
**Output:**
\[
"Science is what we ",
"understand well ",
"enough to explain to ",
"a computer. Art is ",
"everything else we ",
"do "
\]
**Constraints:**
* `1 <= words.length <= 300`
* `1 <= words[i].length <= 20`
* `words[i]` consists of only English letters and symbols.
* `1 <= maxWidth <= 100`
* `words[i].length <= maxWidth` |
125 | Valid Palindrome | impl Solution { pub fn is_palindrome(s: String) -> bool { } } | A phrase is a **palindrome** if, after converting all uppercase letters into lowercase letters and removing all non-alphanumeric characters, it reads the same forward and backward. Alphanumeric characters include letters and numbers.
Given a string `s`, return `true` _if it is a **palindrome**, or_ `false` _otherwise_.
**Example 1:**
**Input:** s = "A man, a plan, a canal: Panama "
**Output:** true
**Explanation:** "amanaplanacanalpanama " is a palindrome.
**Example 2:**
**Input:** s = "race a car "
**Output:** false
**Explanation:** "raceacar " is not a palindrome.
**Example 3:**
**Input:** s = " "
**Output:** true
**Explanation:** s is an empty string " " after removing non-alphanumeric characters.
Since an empty string reads the same forward and backward, it is a palindrome.
**Constraints:**
* `1 <= s.length <= 2 * 105`
* `s` consists only of printable ASCII characters. |
392 | Is Subsequence | impl Solution { pub fn is_subsequence(s: String, t: String) -> bool { } } | Given two strings `s` and `t`, return `true` _if_ `s` _is a **subsequence** of_ `t`_, or_ `false` _otherwise_.
A **subsequence** of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., `"ace "` is a subsequence of `"abcde "` while `"aec "` is not).
**Example 1:**
**Input:** s = "abc", t = "ahbgdc"
**Output:** true
**Example 2:**
**Input:** s = "axc", t = "ahbgdc"
**Output:** false
**Constraints:**
* `0 <= s.length <= 100`
* `0 <= t.length <= 104`
* `s` and `t` consist only of lowercase English letters.
**Follow up:** Suppose there are lots of incoming `s`, say `s1, s2, ..., sk` where `k >= 109`, and you want to check one by one to see if `t` has its subsequence. In this scenario, how would you change your code? |
167 | Two Sum II - Input Array Is Sorted | impl Solution { pub fn two_sum(numbers: Vec<i32>, target: i32) -> Vec<i32> { } } | Given a **1-indexed** array of integers `numbers` that is already **_sorted in non-decreasing order_**, find two numbers such that they add up to a specific `target` number. Let these two numbers be `numbers[index1]` and `numbers[index2]` where `1 <= index1 < index2 <= numbers.length`.
Return _the indices of the two numbers,_ `index1` _and_ `index2`_, **added by one** as an integer array_ `[index1, index2]` _of length 2._
The tests are generated such that there is **exactly one solution**. You **may not** use the same element twice.
Your solution must use only constant extra space.
**Example 1:**
**Input:** numbers = \[2,7,11,15\], target = 9
**Output:** \[1,2\]
**Explanation:** The sum of 2 and 7 is 9. Therefore, index1 = 1, index2 = 2. We return \[1, 2\].
**Example 2:**
**Input:** numbers = \[2,3,4\], target = 6
**Output:** \[1,3\]
**Explanation:** The sum of 2 and 4 is 6. Therefore index1 = 1, index2 = 3. We return \[1, 3\].
**Example 3:**
**Input:** numbers = \[\-1,0\], target = -1
**Output:** \[1,2\]
**Explanation:** The sum of -1 and 0 is -1. Therefore index1 = 1, index2 = 2. We return \[1, 2\].
**Constraints:**
* `2 <= numbers.length <= 3 * 104`
* `-1000 <= numbers[i] <= 1000`
* `numbers` is sorted in **non-decreasing order**.
* `-1000 <= target <= 1000`
* The tests are generated such that there is **exactly one solution**. |
11 | Container With Most Water | impl Solution { pub fn max_area(height: Vec<i32>) -> i32 { let mut max_area = 0; let mut left = 0; let mut right = height.len() - 1; while left < right { let area = (right - left) as i32 * (height[left].min(height[right])); max_area = max_area.max(area); if height[left] < height[right] { left += 1; } else { right -= 1; } } max_area } } | You are given an integer array `height` of length `n`. There are `n` vertical lines drawn such that the two endpoints of the `ith` line are `(i, 0)` and `(i, height[i])`.
Find two lines that together with the x-axis form a container, such that the container contains the most water.
Return _the maximum amount of water a container can store_.
**Notice** that you may not slant the container.
**Example 1:**
**Input:** height = \[1,8,6,2,5,4,8,3,7\]
**Output:** 49
**Explanation:** The above vertical lines are represented by array \[1,8,6,2,5,4,8,3,7\]. In this case, the max area of water (blue section) the container can contain is 49.
**Example 2:**
**Input:** height = \[1,1\]
**Output:** 1
**Constraints:**
* `n == height.length`
* `2 <= n <= 105`
* `0 <= height[i] <= 104` |
15 | 3Sum | impl Solution { pub fn three_sum(nums: Vec<i32>) -> Vec<Vec<i32>> { } } | Given an integer array nums, return all the triplets `[nums[i], nums[j], nums[k]]` such that `i != j`, `i != k`, and `j != k`, and `nums[i] + nums[j] + nums[k] == 0`.
Notice that the solution set must not contain duplicate triplets.
**Example 1:**
**Input:** nums = \[-1,0,1,2,-1,-4\]
**Output:** \[\[-1,-1,2\],\[-1,0,1\]\]
**Explanation:**
nums\[0\] + nums\[1\] + nums\[2\] = (-1) + 0 + 1 = 0.
nums\[1\] + nums\[2\] + nums\[4\] = 0 + 1 + (-1) = 0.
nums\[0\] + nums\[3\] + nums\[4\] = (-1) + 2 + (-1) = 0.
The distinct triplets are \[-1,0,1\] and \[-1,-1,2\].
Notice that the order of the output and the order of the triplets does not matter.
**Example 2:**
**Input:** nums = \[0,1,1\]
**Output:** \[\]
**Explanation:** The only possible triplet does not sum up to 0.
**Example 3:**
**Input:** nums = \[0,0,0\]
**Output:** \[\[0,0,0\]\]
**Explanation:** The only possible triplet sums up to 0.
**Constraints:**
* `3 <= nums.length <= 3000`
* `-105 <= nums[i] <= 105` |
209 | Minimum Size Subarray Sum | impl Solution { pub fn min_sub_array_len(target: i32, nums: Vec<i32>) -> i32 { } } | Given an array of positive integers `nums` and a positive integer `target`, return _the **minimal length** of a_ _subarray_ _whose sum is greater than or equal to_ `target`. If there is no such subarray, return `0` instead.
**Example 1:**
**Input:** target = 7, nums = \[2,3,1,2,4,3\]
**Output:** 2
**Explanation:** The subarray \[4,3\] has the minimal length under the problem constraint.
**Example 2:**
**Input:** target = 4, nums = \[1,4,4\]
**Output:** 1
**Example 3:**
**Input:** target = 11, nums = \[1,1,1,1,1,1,1,1\]
**Output:** 0
**Constraints:**
* `1 <= target <= 109`
* `1 <= nums.length <= 105`
* `1 <= nums[i] <= 104`
**Follow up:** If you have figured out the `O(n)` solution, try coding another solution of which the time complexity is `O(n log(n))`. |
3 | Longest Substring Without Repeating Characters | impl Solution { pub fn length_of_longest_substring(s: String) -> i32 { } } | Given a string `s`, find the length of the **longest** **substring** without repeating characters.
**Example 1:**
**Input:** s = "abcabcbb "
**Output:** 3
**Explanation:** The answer is "abc ", with the length of 3.
**Example 2:**
**Input:** s = "bbbbb "
**Output:** 1
**Explanation:** The answer is "b ", with the length of 1.
**Example 3:**
**Input:** s = "pwwkew "
**Output:** 3
**Explanation:** The answer is "wke ", with the length of 3.
Notice that the answer must be a substring, "pwke " is a subsequence and not a substring.
**Constraints:**
* `0 <= s.length <= 5 * 104`
* `s` consists of English letters, digits, symbols and spaces. |
30 | Substring with Concatenation of All Words | impl Solution { pub fn find_substring(s: String, words: Vec<String>) -> Vec<i32> { } } | You are given a string `s` and an array of strings `words`. All the strings of `words` are of **the same length**.
A **concatenated substring** in `s` is a substring that contains all the strings of any permutation of `words` concatenated.
* For example, if `words = [ "ab ", "cd ", "ef "]`, then `"abcdef "`, `"abefcd "`, `"cdabef "`, `"cdefab "`, `"efabcd "`, and `"efcdab "` are all concatenated strings. `"acdbef "` is not a concatenated substring because it is not the concatenation of any permutation of `words`.
Return _the starting indices of all the concatenated substrings in_ `s`. You can return the answer in **any order**.
**Example 1:**
**Input:** s = "barfoothefoobarman ", words = \[ "foo ", "bar "\]
**Output:** \[0,9\]
**Explanation:** Since words.length == 2 and words\[i\].length == 3, the concatenated substring has to be of length 6.
The substring starting at 0 is "barfoo ". It is the concatenation of \[ "bar ", "foo "\] which is a permutation of words.
The substring starting at 9 is "foobar ". It is the concatenation of \[ "foo ", "bar "\] which is a permutation of words.
The output order does not matter. Returning \[9,0\] is fine too.
**Example 2:**
**Input:** s = "wordgoodgoodgoodbestword ", words = \[ "word ", "good ", "best ", "word "\]
**Output:** \[\]
**Explanation:** Since words.length == 4 and words\[i\].length == 4, the concatenated substring has to be of length 16.
There is no substring of length 16 is s that is equal to the concatenation of any permutation of words.
We return an empty array.
**Example 3:**
**Input:** s = "barfoofoobarthefoobarman ", words = \[ "bar ", "foo ", "the "\]
**Output:** \[6,9,12\]
**Explanation:** Since words.length == 3 and words\[i\].length == 3, the concatenated substring has to be of length 9.
The substring starting at 6 is "foobarthe ". It is the concatenation of \[ "foo ", "bar ", "the "\] which is a permutation of words.
The substring starting at 9 is "barthefoo ". It is the concatenation of \[ "bar ", "the ", "foo "\] which is a permutation of words.
The substring starting at 12 is "thefoobar ". It is the concatenation of \[ "the ", "foo ", "bar "\] which is a permutation of words.
**Constraints:**
* `1 <= s.length <= 104`
* `1 <= words.length <= 5000`
* `1 <= words[i].length <= 30`
* `s` and `words[i]` consist of lowercase English letters. |
76 | Minimum Window Substring | impl Solution { pub fn min_window(s: String, t: String) -> String { } } | Given two strings `s` and `t` of lengths `m` and `n` respectively, return _the **minimum window**_ **_substring_** _of_ `s` _such that every character in_ `t` _(**including duplicates**) is included in the window_. If there is no such substring, return _the empty string_ `" "`.
The testcases will be generated such that the answer is **unique**.
**Example 1:**
**Input:** s = "ADOBECODEBANC ", t = "ABC "
**Output:** "BANC "
**Explanation:** The minimum window substring "BANC " includes 'A', 'B', and 'C' from string t.
**Example 2:**
**Input:** s = "a ", t = "a "
**Output:** "a "
**Explanation:** The entire string s is the minimum window.
**Example 3:**
**Input:** s = "a ", t = "aa "
**Output:** " "
**Explanation:** Both 'a's from t must be included in the window.
Since the largest window of s only has one 'a', return empty string.
**Constraints:**
* `m == s.length`
* `n == t.length`
* `1 <= m, n <= 105`
* `s` and `t` consist of uppercase and lowercase English letters.
**Follow up:** Could you find an algorithm that runs in `O(m + n)` time? |
36 | Valid Sudoku | impl Solution { pub fn is_valid_sudoku(board: Vec<Vec<char>>) -> bool { } } | Determine if a `9 x 9` Sudoku board is valid. Only the filled cells need to be validated **according to the following rules**:
1. Each row must contain the digits `1-9` without repetition.
2. Each column must contain the digits `1-9` without repetition.
3. Each of the nine `3 x 3` sub-boxes of the grid must contain the digits `1-9` without repetition.
**Note:**
* A Sudoku board (partially filled) could be valid but is not necessarily solvable.
* Only the filled cells need to be validated according to the mentioned rules.
**Example 1:**
**Input:** board =
\[\[ "5 ", "3 ", ". ", ". ", "7 ", ". ", ". ", ". ", ". "\]
,\[ "6 ", ". ", ". ", "1 ", "9 ", "5 ", ". ", ". ", ". "\]
,\[ ". ", "9 ", "8 ", ". ", ". ", ". ", ". ", "6 ", ". "\]
,\[ "8 ", ". ", ". ", ". ", "6 ", ". ", ". ", ". ", "3 "\]
,\[ "4 ", ". ", ". ", "8 ", ". ", "3 ", ". ", ". ", "1 "\]
,\[ "7 ", ". ", ". ", ". ", "2 ", ". ", ". ", ". ", "6 "\]
,\[ ". ", "6 ", ". ", ". ", ". ", ". ", "2 ", "8 ", ". "\]
,\[ ". ", ". ", ". ", "4 ", "1 ", "9 ", ". ", ". ", "5 "\]
,\[ ". ", ". ", ". ", ". ", "8 ", ". ", ". ", "7 ", "9 "\]\]
**Output:** true
**Example 2:**
**Input:** board =
\[\[ "8 ", "3 ", ". ", ". ", "7 ", ". ", ". ", ". ", ". "\]
,\[ "6 ", ". ", ". ", "1 ", "9 ", "5 ", ". ", ". ", ". "\]
,\[ ". ", "9 ", "8 ", ". ", ". ", ". ", ". ", "6 ", ". "\]
,\[ "8 ", ". ", ". ", ". ", "6 ", ". ", ". ", ". ", "3 "\]
,\[ "4 ", ". ", ". ", "8 ", ". ", "3 ", ". ", ". ", "1 "\]
,\[ "7 ", ". ", ". ", ". ", "2 ", ". ", ". ", ". ", "6 "\]
,\[ ". ", "6 ", ". ", ". ", ". ", ". ", "2 ", "8 ", ". "\]
,\[ ". ", ". ", ". ", "4 ", "1 ", "9 ", ". ", ". ", "5 "\]
,\[ ". ", ". ", ". ", ". ", "8 ", ". ", ". ", "7 ", "9 "\]\]
**Output:** false
**Explanation:** Same as Example 1, except with the **5** in the top left corner being modified to **8**. Since there are two 8's in the top left 3x3 sub-box, it is invalid.
**Constraints:**
* `board.length == 9`
* `board[i].length == 9`
* `board[i][j]` is a digit `1-9` or `'.'`. |
54 | Spiral Matrix | impl Solution { pub fn spiral_order(matrix: Vec<Vec<i32>>) -> Vec<i32> { } } | Given an `m x n` `matrix`, return _all elements of the_ `matrix` _in spiral order_.
**Example 1:**
**Input:** matrix = \[\[1,2,3\],\[4,5,6\],\[7,8,9\]\]
**Output:** \[1,2,3,6,9,8,7,4,5\]
**Example 2:**
**Input:** matrix = \[\[1,2,3,4\],\[5,6,7,8\],\[9,10,11,12\]\]
**Output:** \[1,2,3,4,8,12,11,10,9,5,6,7\]
**Constraints:**
* `m == matrix.length`
* `n == matrix[i].length`
* `1 <= m, n <= 10`
* `-100 <= matrix[i][j] <= 100` |
48 | Rotate Image | impl Solution { pub fn rotate(matrix: &mut Vec<Vec<i32>>) { } } | You are given an `n x n` 2D `matrix` representing an image, rotate the image by **90** degrees (clockwise).
You have to rotate the image [**in-place**](https://en.wikipedia.org/wiki/In-place_algorithm), which means you have to modify the input 2D matrix directly. **DO NOT** allocate another 2D matrix and do the rotation.
**Example 1:**
**Input:** matrix = \[\[1,2,3\],\[4,5,6\],\[7,8,9\]\]
**Output:** \[\[7,4,1\],\[8,5,2\],\[9,6,3\]\]
**Example 2:**
**Input:** matrix = \[\[5,1,9,11\],\[2,4,8,10\],\[13,3,6,7\],\[15,14,12,16\]\]
**Output:** \[\[15,13,2,5\],\[14,3,4,1\],\[12,6,8,9\],\[16,7,10,11\]\]
**Constraints:**
* `n == matrix.length == matrix[i].length`
* `1 <= n <= 20`
* `-1000 <= matrix[i][j] <= 1000` |
73 | Set Matrix Zeroes | impl Solution { pub fn set_zeroes(matrix: &mut Vec<Vec<i32>>) { } } | Given an `m x n` integer matrix `matrix`, if an element is `0`, set its entire row and column to `0`'s.
You must do it [in place](https://en.wikipedia.org/wiki/In-place_algorithm).
**Example 1:**
**Input:** matrix = \[\[1,1,1\],\[1,0,1\],\[1,1,1\]\]
**Output:** \[\[1,0,1\],\[0,0,0\],\[1,0,1\]\]
**Example 2:**
**Input:** matrix = \[\[0,1,2,0\],\[3,4,5,2\],\[1,3,1,5\]\]
**Output:** \[\[0,0,0,0\],\[0,4,5,0\],\[0,3,1,0\]\]
**Constraints:**
* `m == matrix.length`
* `n == matrix[0].length`
* `1 <= m, n <= 200`
* `-231 <= matrix[i][j] <= 231 - 1`
**Follow up:**
* A straightforward solution using `O(mn)` space is probably a bad idea.
* A simple improvement uses `O(m + n)` space, but still not the best solution.
* Could you devise a constant space solution? |
289 | Game of Life | impl Solution { pub fn game_of_life(board: &mut Vec<Vec<i32>>) { } } | According to [Wikipedia's article](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life): "The **Game of Life**, also known simply as **Life**, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. "
The board is made up of an `m x n` grid of cells, where each cell has an initial state: **live** (represented by a `1`) or **dead** (represented by a `0`). Each cell interacts with its [eight neighbors](https://en.wikipedia.org/wiki/Moore_neighborhood) (horizontal, vertical, diagonal) using the following four rules (taken from the above Wikipedia article):
1. Any live cell with fewer than two live neighbors dies as if caused by under-population.
2. Any live cell with two or three live neighbors lives on to the next generation.
3. Any live cell with more than three live neighbors dies, as if by over-population.
4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.
The next state is created by applying the above rules simultaneously to every cell in the current state, where births and deaths occur simultaneously. Given the current state of the `m x n` grid `board`, return _the next state_.
**Example 1:**
**Input:** board = \[\[0,1,0\],\[0,0,1\],\[1,1,1\],\[0,0,0\]\]
**Output:** \[\[0,0,0\],\[1,0,1\],\[0,1,1\],\[0,1,0\]\]
**Example 2:**
**Input:** board = \[\[1,1\],\[1,0\]\]
**Output:** \[\[1,1\],\[1,1\]\]
**Constraints:**
* `m == board.length`
* `n == board[i].length`
* `1 <= m, n <= 25`
* `board[i][j]` is `0` or `1`.
**Follow up:**
* Could you solve it in-place? Remember that the board needs to be updated simultaneously: You cannot update some cells first and then use their updated values to update other cells.
* In this question, we represent the board using a 2D array. In principle, the board is infinite, which would cause problems when the active area encroaches upon the border of the array (i.e., live cells reach the border). How would you address these problems? |
383 | Ransom Note | impl Solution { pub fn can_construct(ransom_note: String, magazine: String) -> bool { } } | Given two strings `ransomNote` and `magazine`, return `true` _if_ `ransomNote` _can be constructed by using the letters from_ `magazine` _and_ `false` _otherwise_.
Each letter in `magazine` can only be used once in `ransomNote`.
**Example 1:**
**Input:** ransomNote = "a", magazine = "b"
**Output:** false
**Example 2:**
**Input:** ransomNote = "aa", magazine = "ab"
**Output:** false
**Example 3:**
**Input:** ransomNote = "aa", magazine = "aab"
**Output:** true
**Constraints:**
* `1 <= ransomNote.length, magazine.length <= 105`
* `ransomNote` and `magazine` consist of lowercase English letters. |
205 | Isomorphic Strings | impl Solution { pub fn is_isomorphic(s: String, t: String) -> bool { } } | Given two strings `s` and `t`, _determine if they are isomorphic_.
Two strings `s` and `t` are isomorphic if the characters in `s` can be replaced to get `t`.
All occurrences of a character must be replaced with another character while preserving the order of characters. No two characters may map to the same character, but a character may map to itself.
**Example 1:**
**Input:** s = "egg", t = "add"
**Output:** true
**Example 2:**
**Input:** s = "foo", t = "bar"
**Output:** false
**Example 3:**
**Input:** s = "paper", t = "title"
**Output:** true
**Constraints:**
* `1 <= s.length <= 5 * 104`
* `t.length == s.length`
* `s` and `t` consist of any valid ascii character. |
290 | Word Pattern | impl Solution { pub fn word_pattern(pattern: String, s: String) -> bool { } } | Given a `pattern` and a string `s`, find if `s` follows the same pattern.
Here **follow** means a full match, such that there is a bijection between a letter in `pattern` and a **non-empty** word in `s`.
**Example 1:**
**Input:** pattern = "abba ", s = "dog cat cat dog "
**Output:** true
**Example 2:**
**Input:** pattern = "abba ", s = "dog cat cat fish "
**Output:** false
**Example 3:**
**Input:** pattern = "aaaa ", s = "dog cat cat dog "
**Output:** false
**Constraints:**
* `1 <= pattern.length <= 300`
* `pattern` contains only lower-case English letters.
* `1 <= s.length <= 3000`
* `s` contains only lowercase English letters and spaces `' '`.
* `s` **does not contain** any leading or trailing spaces.
* All the words in `s` are separated by a **single space**. |
242 | Valid Anagram | impl Solution { pub fn is_anagram(s: String, t: String) -> bool { } } | Given two strings `s` and `t`, return `true` _if_ `t` _is an anagram of_ `s`_, and_ `false` _otherwise_.
An **Anagram** is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.
**Example 1:**
**Input:** s = "anagram", t = "nagaram"
**Output:** true
**Example 2:**
**Input:** s = "rat", t = "car"
**Output:** false
**Constraints:**
* `1 <= s.length, t.length <= 5 * 104`
* `s` and `t` consist of lowercase English letters.
**Follow up:** What if the inputs contain Unicode characters? How would you adapt your solution to such a case? |
49 | Group Anagrams | impl Solution { pub fn group_anagrams(strs: Vec<String>) -> Vec<Vec<String>> { } } | Given an array of strings `strs`, group **the anagrams** together. You can return the answer in **any order**.
An **Anagram** is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.
**Example 1:**
**Input:** strs = \["eat","tea","tan","ate","nat","bat"\]
**Output:** \[\["bat"\],\["nat","tan"\],\["ate","eat","tea"\]\]
**Example 2:**
**Input:** strs = \[""\]
**Output:** \[\[""\]\]
**Example 3:**
**Input:** strs = \["a"\]
**Output:** \[\["a"\]\]
**Constraints:**
* `1 <= strs.length <= 104`
* `0 <= strs[i].length <= 100`
* `strs[i]` consists of lowercase English letters. |
1 | Two Sum | impl Solution { pub fn two_sum(nums: Vec<i32>, target: i32) -> Vec<i32> { } } | Given an array of integers `nums` and an integer `target`, return _indices of the two numbers such that they add up to `target`_.
You may assume that each input would have **_exactly_ one solution**, and you may not use the _same_ element twice.
You can return the answer in any order.
**Example 1:**
**Input:** nums = \[2,7,11,15\], target = 9
**Output:** \[0,1\]
**Explanation:** Because nums\[0\] + nums\[1\] == 9, we return \[0, 1\].
**Example 2:**
**Input:** nums = \[3,2,4\], target = 6
**Output:** \[1,2\]
**Example 3:**
**Input:** nums = \[3,3\], target = 6
**Output:** \[0,1\]
**Constraints:**
* `2 <= nums.length <= 104`
* `-109 <= nums[i] <= 109`
* `-109 <= target <= 109`
* **Only one valid answer exists.**
**Follow-up:** Can you come up with an algorithm that is less than `O(n2)` time complexity? |
202 | Happy Number | impl Solution { pub fn is_happy(n: i32) -> bool { } } | Write an algorithm to determine if a number `n` is happy.
A **happy number** is a number defined by the following process:
* Starting with any positive integer, replace the number by the sum of the squares of its digits.
* Repeat the process until the number equals 1 (where it will stay), or it **loops endlessly in a cycle** which does not include 1.
* Those numbers for which this process **ends in 1** are happy.
Return `true` _if_ `n` _is a happy number, and_ `false` _if not_.
**Example 1:**
**Input:** n = 19
**Output:** true
**Explanation:**
12 + 92 = 82
82 + 22 = 68
62 + 82 = 100
12 + 02 + 02 = 1
**Example 2:**
**Input:** n = 2
**Output:** false
**Constraints:**
* `1 <= n <= 231 - 1` |
219 | Contains Duplicate II | impl Solution { pub fn contains_nearby_duplicate(nums: Vec<i32>, k: i32) -> bool { } } | Given an integer array `nums` and an integer `k`, return `true` _if there are two **distinct indices**_ `i` _and_ `j` _in the array such that_ `nums[i] == nums[j]` _and_ `abs(i - j) <= k`.
**Example 1:**
**Input:** nums = \[1,2,3,1\], k = 3
**Output:** true
**Example 2:**
**Input:** nums = \[1,0,1,1\], k = 1
**Output:** true
**Example 3:**
**Input:** nums = \[1,2,3,1,2,3\], k = 2
**Output:** false
**Constraints:**
* `1 <= nums.length <= 105`
* `-109 <= nums[i] <= 109`
* `0 <= k <= 105` |
128 | Longest Consecutive Sequence | impl Solution { pub fn longest_consecutive(nums: Vec<i32>) -> i32 { } } | Given an unsorted array of integers `nums`, return _the length of the longest consecutive elements sequence._
You must write an algorithm that runs in `O(n)` time.
**Example 1:**
**Input:** nums = \[100,4,200,1,3,2\]
**Output:** 4
**Explanation:** The longest consecutive elements sequence is `[1, 2, 3, 4]`. Therefore its length is 4.
**Example 2:**
**Input:** nums = \[0,3,7,2,5,8,4,6,0,1\]
**Output:** 9
**Constraints:**
* `0 <= nums.length <= 105`
* `-109 <= nums[i] <= 109` |
228 | Summary Ranges | impl Solution { pub fn summary_ranges(nums: Vec<i32>) -> Vec<String> { } } | You are given a **sorted unique** integer array `nums`.
A **range** `[a,b]` is the set of all integers from `a` to `b` (inclusive).
Return _the **smallest sorted** list of ranges that **cover all the numbers in the array exactly**_. That is, each element of `nums` is covered by exactly one of the ranges, and there is no integer `x` such that `x` is in one of the ranges but not in `nums`.
Each range `[a,b]` in the list should be output as:
* `"a->b "` if `a != b`
* `"a "` if `a == b`
**Example 1:**
**Input:** nums = \[0,1,2,4,5,7\]
**Output:** \[ "0->2 ", "4->5 ", "7 "\]
**Explanation:** The ranges are:
\[0,2\] --> "0->2 "
\[4,5\] --> "4->5 "
\[7,7\] --> "7 "
**Example 2:**
**Input:** nums = \[0,2,3,4,6,8,9\]
**Output:** \[ "0 ", "2->4 ", "6 ", "8->9 "\]
**Explanation:** The ranges are:
\[0,0\] --> "0 "
\[2,4\] --> "2->4 "
\[6,6\] --> "6 "
\[8,9\] --> "8->9 "
**Constraints:**
* `0 <= nums.length <= 20`
* `-231 <= nums[i] <= 231 - 1`
* All the values of `nums` are **unique**.
* `nums` is sorted in ascending order. |
56 | Merge Intervals | impl Solution { pub fn merge(intervals: Vec<Vec<i32>>) -> Vec<Vec<i32>> { } } | Given an array of `intervals` where `intervals[i] = [starti, endi]`, merge all overlapping intervals, and return _an array of the non-overlapping intervals that cover all the intervals in the input_.
**Example 1:**
**Input:** intervals = \[\[1,3\],\[2,6\],\[8,10\],\[15,18\]\]
**Output:** \[\[1,6\],\[8,10\],\[15,18\]\]
**Explanation:** Since intervals \[1,3\] and \[2,6\] overlap, merge them into \[1,6\].
**Example 2:**
**Input:** intervals = \[\[1,4\],\[4,5\]\]
**Output:** \[\[1,5\]\]
**Explanation:** Intervals \[1,4\] and \[4,5\] are considered overlapping.
**Constraints:**
* `1 <= intervals.length <= 104`
* `intervals[i].length == 2`
* `0 <= starti <= endi <= 104` |
57 | Insert Interval | impl Solution { pub fn insert(intervals: Vec<Vec<i32>>, new_interval: Vec<i32>) -> Vec<Vec<i32>> { } } | You are given an array of non-overlapping intervals `intervals` where `intervals[i] = [starti, endi]` represent the start and the end of the `ith` interval and `intervals` is sorted in ascending order by `starti`. You are also given an interval `newInterval = [start, end]` that represents the start and end of another interval.
Insert `newInterval` into `intervals` such that `intervals` is still sorted in ascending order by `starti` and `intervals` still does not have any overlapping intervals (merge overlapping intervals if necessary).
Return `intervals` _after the insertion_.
**Example 1:**
**Input:** intervals = \[\[1,3\],\[6,9\]\], newInterval = \[2,5\]
**Output:** \[\[1,5\],\[6,9\]\]
**Example 2:**
**Input:** intervals = \[\[1,2\],\[3,5\],\[6,7\],\[8,10\],\[12,16\]\], newInterval = \[4,8\]
**Output:** \[\[1,2\],\[3,10\],\[12,16\]\]
**Explanation:** Because the new interval \[4,8\] overlaps with \[3,5\],\[6,7\],\[8,10\].
**Constraints:**
* `0 <= intervals.length <= 104`
* `intervals[i].length == 2`
* `0 <= starti <= endi <= 105`
* `intervals` is sorted by `starti` in **ascending** order.
* `newInterval.length == 2`
* `0 <= start <= end <= 105` |
452 | Minimum Number of Arrows to Burst Balloons | impl Solution { pub fn find_min_arrow_shots(points: Vec<Vec<i32>>) -> i32 { } } | There are some spherical balloons taped onto a flat wall that represents the XY-plane. The balloons are represented as a 2D integer array `points` where `points[i] = [xstart, xend]` denotes a balloon whose **horizontal diameter** stretches between `xstart` and `xend`. You do not know the exact y-coordinates of the balloons.
Arrows can be shot up **directly vertically** (in the positive y-direction) from different points along the x-axis. A balloon with `xstart` and `xend` is **burst** by an arrow shot at `x` if `xstart <= x <= xend`. There is **no limit** to the number of arrows that can be shot. A shot arrow keeps traveling up infinitely, bursting any balloons in its path.
Given the array `points`, return _the **minimum** number of arrows that must be shot to burst all balloons_.
**Example 1:**
**Input:** points = \[\[10,16\],\[2,8\],\[1,6\],\[7,12\]\]
**Output:** 2
**Explanation:** The balloons can be burst by 2 arrows:
- Shoot an arrow at x = 6, bursting the balloons \[2,8\] and \[1,6\].
- Shoot an arrow at x = 11, bursting the balloons \[10,16\] and \[7,12\].
**Example 2:**
**Input:** points = \[\[1,2\],\[3,4\],\[5,6\],\[7,8\]\]
**Output:** 4
**Explanation:** One arrow needs to be shot for each balloon for a total of 4 arrows.
**Example 3:**
**Input:** points = \[\[1,2\],\[2,3\],\[3,4\],\[4,5\]\]
**Output:** 2
**Explanation:** The balloons can be burst by 2 arrows:
- Shoot an arrow at x = 2, bursting the balloons \[1,2\] and \[2,3\].
- Shoot an arrow at x = 4, bursting the balloons \[3,4\] and \[4,5\].
**Constraints:**
* `1 <= points.length <= 105`
* `points[i].length == 2`
* `-231 <= xstart < xend <= 231 - 1` |
20 | Valid Parentheses | impl Solution { pub fn is_valid(s: String) -> bool { } } | Given a string `s` containing just the characters `'('`, `')'`, `'{'`, `'}'`, `'['` and `']'`, determine if the input string is valid.
An input string is valid if:
1. Open brackets must be closed by the same type of brackets.
2. Open brackets must be closed in the correct order.
3. Every close bracket has a corresponding open bracket of the same type.
**Example 1:**
**Input:** s = "() "
**Output:** true
**Example 2:**
**Input:** s = "()\[\]{} "
**Output:** true
**Example 3:**
**Input:** s = "(\] "
**Output:** false
**Constraints:**
* `1 <= s.length <= 104`
* `s` consists of parentheses only `'()[]{}'`. |
71 | Simplify Path | impl Solution { pub fn simplify_path(path: String) -> String { } } | Given a string `path`, which is an **absolute path** (starting with a slash `'/'`) to a file or directory in a Unix-style file system, convert it to the simplified **canonical path**.
In a Unix-style file system, a period `'.'` refers to the current directory, a double period `'..'` refers to the directory up a level, and any multiple consecutive slashes (i.e. `'//'`) are treated as a single slash `'/'`. For this problem, any other format of periods such as `'...'` are treated as file/directory names.
The **canonical path** should have the following format:
* The path starts with a single slash `'/'`.
* Any two directories are separated by a single slash `'/'`.
* The path does not end with a trailing `'/'`.
* The path only contains the directories on the path from the root directory to the target file or directory (i.e., no period `'.'` or double period `'..'`)
Return _the simplified **canonical path**_.
**Example 1:**
**Input:** path = "/home/ "
**Output:** "/home "
**Explanation:** Note that there is no trailing slash after the last directory name.
**Example 2:**
**Input:** path = "/../ "
**Output:** "/ "
**Explanation:** Going one level up from the root directory is a no-op, as the root level is the highest level you can go.
**Example 3:**
**Input:** path = "/home//foo/ "
**Output:** "/home/foo "
**Explanation:** In the canonical path, multiple consecutive slashes are replaced by a single one.
**Constraints:**
* `1 <= path.length <= 3000`
* `path` consists of English letters, digits, period `'.'`, slash `'/'` or `'_'`.
* `path` is a valid absolute Unix path. |
155 | Min Stack | struct MinStack { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl MinStack { fn new() -> Self { } fn push(&self, val: i32) { } fn pop(&self) { } fn top(&self) -> i32 { } fn get_min(&self) -> i32 { } } /** * Your MinStack object will be instantiated and called as such: * let obj = MinStack::new(); * obj.push(val); * obj.pop(); * let ret_3: i32 = obj.top(); * let ret_4: i32 = obj.get_min(); */ | Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
Implement the `MinStack` class:
* `MinStack()` initializes the stack object.
* `void push(int val)` pushes the element `val` onto the stack.
* `void pop()` removes the element on the top of the stack.
* `int top()` gets the top element of the stack.
* `int getMin()` retrieves the minimum element in the stack.
You must implement a solution with `O(1)` time complexity for each function.
**Example 1:**
**Input**
\[ "MinStack ", "push ", "push ", "push ", "getMin ", "pop ", "top ", "getMin "\]
\[\[\],\[-2\],\[0\],\[-3\],\[\],\[\],\[\],\[\]\]
**Output**
\[null,null,null,null,-3,null,0,-2\]
**Explanation**
MinStack minStack = new MinStack();
minStack.push(-2);
minStack.push(0);
minStack.push(-3);
minStack.getMin(); // return -3
minStack.pop();
minStack.top(); // return 0
minStack.getMin(); // return -2
**Constraints:**
* `-231 <= val <= 231 - 1`
* Methods `pop`, `top` and `getMin` operations will always be called on **non-empty** stacks.
* At most `3 * 104` calls will be made to `push`, `pop`, `top`, and `getMin`. |
150 | Evaluate Reverse Polish Notation | impl Solution { pub fn eval_rpn(tokens: Vec<String>) -> i32 { } } | You are given an array of strings `tokens` that represents an arithmetic expression in a [Reverse Polish Notation](http://en.wikipedia.org/wiki/Reverse_Polish_notation).
Evaluate the expression. Return _an integer that represents the value of the expression_.
**Note** that:
* The valid operators are `'+'`, `'-'`, `'*'`, and `'/'`.
* Each operand may be an integer or another expression.
* The division between two integers always **truncates toward zero**.
* There will not be any division by zero.
* The input represents a valid arithmetic expression in a reverse polish notation.
* The answer and all the intermediate calculations can be represented in a **32-bit** integer.
**Example 1:**
**Input:** tokens = \[ "2 ", "1 ", "+ ", "3 ", "\* "\]
**Output:** 9
**Explanation:** ((2 + 1) \* 3) = 9
**Example 2:**
**Input:** tokens = \[ "4 ", "13 ", "5 ", "/ ", "+ "\]
**Output:** 6
**Explanation:** (4 + (13 / 5)) = 6
**Example 3:**
**Input:** tokens = \[ "10 ", "6 ", "9 ", "3 ", "+ ", "-11 ", "\* ", "/ ", "\* ", "17 ", "+ ", "5 ", "+ "\]
**Output:** 22
**Explanation:** ((10 \* (6 / ((9 + 3) \* -11))) + 17) + 5
= ((10 \* (6 / (12 \* -11))) + 17) + 5
= ((10 \* (6 / -132)) + 17) + 5
= ((10 \* 0) + 17) + 5
= (0 + 17) + 5
= 17 + 5
= 22
**Constraints:**
* `1 <= tokens.length <= 104`
* `tokens[i]` is either an operator: `"+ "`, `"- "`, `"* "`, or `"/ "`, or an integer in the range `[-200, 200]`. |
224 | Basic Calculator | impl Solution { pub fn calculate(s: String) -> i32 { } } | Given a string `s` representing a valid expression, implement a basic calculator to evaluate it, and return _the result of the evaluation_.
**Note:** You are **not** allowed to use any built-in function which evaluates strings as mathematical expressions, such as `eval()`.
**Example 1:**
**Input:** s = "1 + 1 "
**Output:** 2
**Example 2:**
**Input:** s = " 2-1 + 2 "
**Output:** 3
**Example 3:**
**Input:** s = "(1+(4+5+2)-3)+(6+8) "
**Output:** 23
**Constraints:**
* `1 <= s.length <= 3 * 105`
* `s` consists of digits, `'+'`, `'-'`, `'('`, `')'`, and `' '`.
* `s` represents a valid expression.
* `'+'` is **not** used as a unary operation (i.e., `"+1 "` and `"+(2 + 3) "` is invalid).
* `'-'` could be used as a unary operation (i.e., `"-1 "` and `"-(2 + 3) "` is valid).
* There will be no two consecutive operators in the input.
* Every number and running calculation will fit in a signed 32-bit integer. |
141 | Linked List Cycle | /** * Definition for singly-linked list. * struct ListNode { * int val; * ListNode *next; * ListNode(int x) : val(x), next(NULL) {} * }; */ class Solution { public: bool hasCycle(ListNode *head) { } }; | Given `head`, the head of a linked list, determine if the linked list has a cycle in it.
There is a cycle in a linked list if there is some node in the list that can be reached again by continuously following the `next` pointer. Internally, `pos` is used to denote the index of the node that tail's `next` pointer is connected to. **Note that `pos` is not passed as a parameter**.
Return `true` _if there is a cycle in the linked list_. Otherwise, return `false`.
**Example 1:**
**Input:** head = \[3,2,0,-4\], pos = 1
**Output:** true
**Explanation:** There is a cycle in the linked list, where the tail connects to the 1st node (0-indexed).
**Example 2:**
**Input:** head = \[1,2\], pos = 0
**Output:** true
**Explanation:** There is a cycle in the linked list, where the tail connects to the 0th node.
**Example 3:**
**Input:** head = \[1\], pos = -1
**Output:** false
**Explanation:** There is no cycle in the linked list.
**Constraints:**
* The number of the nodes in the list is in the range `[0, 104]`.
* `-105 <= Node.val <= 105`
* `pos` is `-1` or a **valid index** in the linked-list.
**Follow up:** Can you solve it using `O(1)` (i.e. constant) memory? |
2 | Add Two Numbers | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn add_two_numbers(l1: Option<Box<ListNode>>, l2: Option<Box<ListNode>>) -> Option<Box<ListNode>> { } } | You are given two **non-empty** linked lists representing two non-negative integers. The digits are stored in **reverse order**, and each of their nodes contains a single digit. Add the two numbers and return the sum as a linked list.
You may assume the two numbers do not contain any leading zero, except the number 0 itself.
**Example 1:**
**Input:** l1 = \[2,4,3\], l2 = \[5,6,4\]
**Output:** \[7,0,8\]
**Explanation:** 342 + 465 = 807.
**Example 2:**
**Input:** l1 = \[0\], l2 = \[0\]
**Output:** \[0\]
**Example 3:**
**Input:** l1 = \[9,9,9,9,9,9,9\], l2 = \[9,9,9,9\]
**Output:** \[8,9,9,9,0,0,0,1\]
**Constraints:**
* The number of nodes in each linked list is in the range `[1, 100]`.
* `0 <= Node.val <= 9`
* It is guaranteed that the list represents a number that does not have leading zeros. |
21 | Merge Two Sorted Lists | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn merge_two_lists(list1: Option<Box<ListNode>>, list2: Option<Box<ListNode>>) -> Option<Box<ListNode>> { } } | You are given the heads of two sorted linked lists `list1` and `list2`.
Merge the two lists in a one **sorted** list. The list should be made by splicing together the nodes of the first two lists.
Return _the head of the merged linked list_.
**Example 1:**
**Input:** list1 = \[1,2,4\], list2 = \[1,3,4\]
**Output:** \[1,1,2,3,4,4\]
**Example 2:**
**Input:** list1 = \[\], list2 = \[\]
**Output:** \[\]
**Example 3:**
**Input:** list1 = \[\], list2 = \[0\]
**Output:** \[0\]
**Constraints:**
* The number of nodes in both lists is in the range `[0, 50]`.
* `-100 <= Node.val <= 100`
* Both `list1` and `list2` are sorted in **non-decreasing** order. |
138 | Copy List with Random Pointer | /* // Definition for a Node. class Node { public: int val; Node* next; Node* random; Node(int _val) { val = _val; next = NULL; random = NULL; } }; */ class Solution { public: Node* copyRandomList(Node* head) { } }; | A linked list of length `n` is given such that each node contains an additional random pointer, which could point to any node in the list, or `null`.
Construct a [**deep copy**](https://en.wikipedia.org/wiki/Object_copying#Deep_copy) of the list. The deep copy should consist of exactly `n` **brand new** nodes, where each new node has its value set to the value of its corresponding original node. Both the `next` and `random` pointer of the new nodes should point to new nodes in the copied list such that the pointers in the original list and copied list represent the same list state. **None of the pointers in the new list should point to nodes in the original list**.
For example, if there are two nodes `X` and `Y` in the original list, where `X.random --> Y`, then for the corresponding two nodes `x` and `y` in the copied list, `x.random --> y`.
Return _the head of the copied linked list_.
The linked list is represented in the input/output as a list of `n` nodes. Each node is represented as a pair of `[val, random_index]` where:
* `val`: an integer representing `Node.val`
* `random_index`: the index of the node (range from `0` to `n-1`) that the `random` pointer points to, or `null` if it does not point to any node.
Your code will **only** be given the `head` of the original linked list.
**Example 1:**
**Input:** head = \[\[7,null\],\[13,0\],\[11,4\],\[10,2\],\[1,0\]\]
**Output:** \[\[7,null\],\[13,0\],\[11,4\],\[10,2\],\[1,0\]\]
**Example 2:**
**Input:** head = \[\[1,1\],\[2,1\]\]
**Output:** \[\[1,1\],\[2,1\]\]
**Example 3:**
**Input:** head = \[\[3,null\],\[3,0\],\[3,null\]\]
**Output:** \[\[3,null\],\[3,0\],\[3,null\]\]
**Constraints:**
* `0 <= n <= 1000`
* `-104 <= Node.val <= 104`
* `Node.random` is `null` or is pointing to some node in the linked list. |
92 | Reverse Linked List II | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn reverse_between(head: Option<Box<ListNode>>, left: i32, right: i32) -> Option<Box<ListNode>> { } } | Given the `head` of a singly linked list and two integers `left` and `right` where `left <= right`, reverse the nodes of the list from position `left` to position `right`, and return _the reversed list_.
**Example 1:**
**Input:** head = \[1,2,3,4,5\], left = 2, right = 4
**Output:** \[1,4,3,2,5\]
**Example 2:**
**Input:** head = \[5\], left = 1, right = 1
**Output:** \[5\]
**Constraints:**
* The number of nodes in the list is `n`.
* `1 <= n <= 500`
* `-500 <= Node.val <= 500`
* `1 <= left <= right <= n`
**Follow up:** Could you do it in one pass? |
25 | Reverse Nodes in k-Group | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn reverse_k_group(head: Option<Box<ListNode>>, k: i32) -> Option<Box<ListNode>> { } } | Given the `head` of a linked list, reverse the nodes of the list `k` at a time, and return _the modified list_.
`k` is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of `k` then left-out nodes, in the end, should remain as it is.
You may not alter the values in the list's nodes, only nodes themselves may be changed.
**Example 1:**
**Input:** head = \[1,2,3,4,5\], k = 2
**Output:** \[2,1,4,3,5\]
**Example 2:**
**Input:** head = \[1,2,3,4,5\], k = 3
**Output:** \[3,2,1,4,5\]
**Constraints:**
* The number of nodes in the list is `n`.
* `1 <= k <= n <= 5000`
* `0 <= Node.val <= 1000`
**Follow-up:** Can you solve the problem in `O(1)` extra memory space? |
19 | Remove Nth Node From End of List | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn remove_nth_from_end(head: Option<Box<ListNode>>, n: i32) -> Option<Box<ListNode>> { } } | Given the `head` of a linked list, remove the `nth` node from the end of the list and return its head.
**Example 1:**
**Input:** head = \[1,2,3,4,5\], n = 2
**Output:** \[1,2,3,5\]
**Example 2:**
**Input:** head = \[1\], n = 1
**Output:** \[\]
**Example 3:**
**Input:** head = \[1,2\], n = 1
**Output:** \[1\]
**Constraints:**
* The number of nodes in the list is `sz`.
* `1 <= sz <= 30`
* `0 <= Node.val <= 100`
* `1 <= n <= sz`
**Follow up:** Could you do this in one pass? |
82 | Remove Duplicates from Sorted List II | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn delete_duplicates(head: Option<Box<ListNode>>) -> Option<Box<ListNode>> { } } | Given the `head` of a sorted linked list, _delete all nodes that have duplicate numbers, leaving only distinct numbers from the original list_. Return _the linked list **sorted** as well_.
**Example 1:**
**Input:** head = \[1,2,3,3,4,4,5\]
**Output:** \[1,2,5\]
**Example 2:**
**Input:** head = \[1,1,1,2,3\]
**Output:** \[2,3\]
**Constraints:**
* The number of nodes in the list is in the range `[0, 300]`.
* `-100 <= Node.val <= 100`
* The list is guaranteed to be **sorted** in ascending order. |
61 | Rotate List | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn rotate_right(head: Option<Box<ListNode>>, k: i32) -> Option<Box<ListNode>> { } } | Given the `head` of a linked list, rotate the list to the right by `k` places.
**Example 1:**
**Input:** head = \[1,2,3,4,5\], k = 2
**Output:** \[4,5,1,2,3\]
**Example 2:**
**Input:** head = \[0,1,2\], k = 4
**Output:** \[2,0,1\]
**Constraints:**
* The number of nodes in the list is in the range `[0, 500]`.
* `-100 <= Node.val <= 100`
* `0 <= k <= 2 * 109` |
86 | Partition List | // Definition for singly-linked list. // #[derive(PartialEq, Eq, Clone, Debug)] // pub struct ListNode { // pub val: i32, // pub next: Option<Box<ListNode>> // } // // impl ListNode { // #[inline] // fn new(val: i32) -> Self { // ListNode { // next: None, // val // } // } // } impl Solution { pub fn partition(head: Option<Box<ListNode>>, x: i32) -> Option<Box<ListNode>> { } } | Given the `head` of a linked list and a value `x`, partition it such that all nodes **less than** `x` come before nodes **greater than or equal** to `x`.
You should **preserve** the original relative order of the nodes in each of the two partitions.
**Example 1:**
**Input:** head = \[1,4,3,2,5,2\], x = 3
**Output:** \[1,2,2,4,3,5\]
**Example 2:**
**Input:** head = \[2,1\], x = 2
**Output:** \[1,2\]
**Constraints:**
* The number of nodes in the list is in the range `[0, 200]`.
* `-100 <= Node.val <= 100`
* `-200 <= x <= 200` |
146 | LRU Cache | struct LRUCache { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl LRUCache { fn new(capacity: i32) -> Self { } fn get(&self, key: i32) -> i32 { } fn put(&self, key: i32, value: i32) { } } /** * Your LRUCache object will be instantiated and called as such: * let obj = LRUCache::new(capacity); * let ret_1: i32 = obj.get(key); * obj.put(key, value); */ | Design a data structure that follows the constraints of a **[Least Recently Used (LRU) cache](https://en.wikipedia.org/wiki/Cache_replacement_policies#LRU)**.
Implement the `LRUCache` class:
* `LRUCache(int capacity)` Initialize the LRU cache with **positive** size `capacity`.
* `int get(int key)` Return the value of the `key` if the key exists, otherwise return `-1`.
* `void put(int key, int value)` Update the value of the `key` if the `key` exists. Otherwise, add the `key-value` pair to the cache. If the number of keys exceeds the `capacity` from this operation, **evict** the least recently used key.
The functions `get` and `put` must each run in `O(1)` average time complexity.
**Example 1:**
**Input**
\[ "LRUCache ", "put ", "put ", "get ", "put ", "get ", "put ", "get ", "get ", "get "\]
\[\[2\], \[1, 1\], \[2, 2\], \[1\], \[3, 3\], \[2\], \[4, 4\], \[1\], \[3\], \[4\]\]
**Output**
\[null, null, null, 1, null, -1, null, -1, 3, 4\]
**Explanation**
LRUCache lRUCache = new LRUCache(2);
lRUCache.put(1, 1); // cache is {1=1}
lRUCache.put(2, 2); // cache is {1=1, 2=2}
lRUCache.get(1); // return 1
lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
lRUCache.get(2); // returns -1 (not found)
lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
lRUCache.get(1); // return -1 (not found)
lRUCache.get(3); // return 3
lRUCache.get(4); // return 4
**Constraints:**
* `1 <= capacity <= 3000`
* `0 <= key <= 104`
* `0 <= value <= 105`
* At most `2 * 105` calls will be made to `get` and `put`. |
104 | Maximum Depth of Binary Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn max_depth(root: Option<Rc<RefCell<TreeNode>>>) -> i32 { } } | Given the `root` of a binary tree, return _its maximum depth_.
A binary tree's **maximum depth** is the number of nodes along the longest path from the root node down to the farthest leaf node.
**Example 1:**
**Input:** root = \[3,9,20,null,null,15,7\]
**Output:** 3
**Example 2:**
**Input:** root = \[1,null,2\]
**Output:** 2
**Constraints:**
* The number of nodes in the tree is in the range `[0, 104]`.
* `-100 <= Node.val <= 100` |
100 | Same Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn is_same_tree(p: Option<Rc<RefCell<TreeNode>>>, q: Option<Rc<RefCell<TreeNode>>>) -> bool { } } | Given the roots of two binary trees `p` and `q`, write a function to check if they are the same or not.
Two binary trees are considered the same if they are structurally identical, and the nodes have the same value.
**Example 1:**
**Input:** p = \[1,2,3\], q = \[1,2,3\]
**Output:** true
**Example 2:**
**Input:** p = \[1,2\], q = \[1,null,2\]
**Output:** false
**Example 3:**
**Input:** p = \[1,2,1\], q = \[1,1,2\]
**Output:** false
**Constraints:**
* The number of nodes in both trees is in the range `[0, 100]`.
* `-104 <= Node.val <= 104` |
226 | Invert Binary Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn invert_tree(root: Option<Rc<RefCell<TreeNode>>>) -> Option<Rc<RefCell<TreeNode>>> { } } | Given the `root` of a binary tree, invert the tree, and return _its root_.
**Example 1:**
**Input:** root = \[4,2,7,1,3,6,9\]
**Output:** \[4,7,2,9,6,3,1\]
**Example 2:**
**Input:** root = \[2,1,3\]
**Output:** \[2,3,1\]
**Example 3:**
**Input:** root = \[\]
**Output:** \[\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 100]`.
* `-100 <= Node.val <= 100` |
101 | Symmetric Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn is_symmetric(root: Option<Rc<RefCell<TreeNode>>>) -> bool { fn is_symmetric_helper(left: Option<&TreeNode>, right: Option<&TreeNode>) -> bool { match (left, right) { (None, None) => true, (Some(l), Some(r)) => { l.val == r.val && is_symmetric_helper(l.left.as_deref(), r.right.as_deref()) && is_symmetric_helper(l.right.as_deref(), r.left.as_deref()) } _ => false, } } is_symmetric_helper(root, root) } } | Given the `root` of a binary tree, _check whether it is a mirror of itself_ (i.e., symmetric around its center).
**Example 1:**
**Input:** root = \[1,2,2,3,4,4,3\]
**Output:** true
**Example 2:**
**Input:** root = \[1,2,2,null,3,null,3\]
**Output:** false
**Constraints:**
* The number of nodes in the tree is in the range `[1, 1000]`.
* `-100 <= Node.val <= 100`
**Follow up:** Could you solve it both recursively and iteratively? |
105 | Construct Binary Tree from Preorder and Inorder Traversal | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn build_tree(preorder: Vec<i32>, inorder: Vec<i32>) -> Option<Rc<RefCell<TreeNode>>> { } } | Given two integer arrays `preorder` and `inorder` where `preorder` is the preorder traversal of a binary tree and `inorder` is the inorder traversal of the same tree, construct and return _the binary tree_.
**Example 1:**
**Input:** preorder = \[3,9,20,15,7\], inorder = \[9,3,15,20,7\]
**Output:** \[3,9,20,null,null,15,7\]
**Example 2:**
**Input:** preorder = \[-1\], inorder = \[-1\]
**Output:** \[-1\]
**Constraints:**
* `1 <= preorder.length <= 3000`
* `inorder.length == preorder.length`
* `-3000 <= preorder[i], inorder[i] <= 3000`
* `preorder` and `inorder` consist of **unique** values.
* Each value of `inorder` also appears in `preorder`.
* `preorder` is **guaranteed** to be the preorder traversal of the tree.
* `inorder` is **guaranteed** to be the inorder traversal of the tree. |
106 | Construct Binary Tree from Inorder and Postorder Traversal | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn build_tree(inorder: Vec<i32>, postorder: Vec<i32>) -> Option<Rc<RefCell<TreeNode>>> { } } | Given two integer arrays `inorder` and `postorder` where `inorder` is the inorder traversal of a binary tree and `postorder` is the postorder traversal of the same tree, construct and return _the binary tree_.
**Example 1:**
**Input:** inorder = \[9,3,15,20,7\], postorder = \[9,15,7,20,3\]
**Output:** \[3,9,20,null,null,15,7\]
**Example 2:**
**Input:** inorder = \[-1\], postorder = \[-1\]
**Output:** \[-1\]
**Constraints:**
* `1 <= inorder.length <= 3000`
* `postorder.length == inorder.length`
* `-3000 <= inorder[i], postorder[i] <= 3000`
* `inorder` and `postorder` consist of **unique** values.
* Each value of `postorder` also appears in `inorder`.
* `inorder` is **guaranteed** to be the inorder traversal of the tree.
* `postorder` is **guaranteed** to be the postorder traversal of the tree. |
117 | Populating Next Right Pointers in Each Node II | /* // Definition for a Node. class Node { public: int val; Node* left; Node* right; Node* next; Node() : val(0), left(NULL), right(NULL), next(NULL) {} Node(int _val) : val(_val), left(NULL), right(NULL), next(NULL) {} Node(int _val, Node* _left, Node* _right, Node* _next) : val(_val), left(_left), right(_right), next(_next) {} }; */ class Solution { public: Node* connect(Node* root) { } }; | Given a binary tree
struct Node {
int val;
Node \*left;
Node \*right;
Node \*next;
}
Populate each next pointer to point to its next right node. If there is no next right node, the next pointer should be set to `NULL`.
Initially, all next pointers are set to `NULL`.
**Example 1:**
**Input:** root = \[1,2,3,4,5,null,7\]
**Output:** \[1,#,2,3,#,4,5,7,#\]
**Explanation:** Given the above binary tree (Figure A), your function should populate each next pointer to point to its next right node, just like in Figure B. The serialized output is in level order as connected by the next pointers, with '#' signifying the end of each level.
**Example 2:**
**Input:** root = \[\]
**Output:** \[\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 6000]`.
* `-100 <= Node.val <= 100`
**Follow-up:**
* You may only use constant extra space.
* The recursive approach is fine. You may assume implicit stack space does not count as extra space for this problem. |
114 | Flatten Binary Tree to Linked List | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn flatten(root: &mut Option<Rc<RefCell<TreeNode>>>) { } } | Given the `root` of a binary tree, flatten the tree into a "linked list ":
* The "linked list " should use the same `TreeNode` class where the `right` child pointer points to the next node in the list and the `left` child pointer is always `null`.
* The "linked list " should be in the same order as a [**pre-order** **traversal**](https://en.wikipedia.org/wiki/Tree_traversal#Pre-order,_NLR) of the binary tree.
**Example 1:**
**Input:** root = \[1,2,5,3,4,null,6\]
**Output:** \[1,null,2,null,3,null,4,null,5,null,6\]
**Example 2:**
**Input:** root = \[\]
**Output:** \[\]
**Example 3:**
**Input:** root = \[0\]
**Output:** \[0\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 2000]`.
* `-100 <= Node.val <= 100`
**Follow up:** Can you flatten the tree in-place (with `O(1)` extra space)? |
112 | Path Sum | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn has_path_sum(root: Option<Rc<RefCell<TreeNode>>>, target_sum: i32) -> bool { } } | Given the `root` of a binary tree and an integer `targetSum`, return `true` if the tree has a **root-to-leaf** path such that adding up all the values along the path equals `targetSum`.
A **leaf** is a node with no children.
**Example 1:**
**Input:** root = \[5,4,8,11,null,13,4,7,2,null,null,null,1\], targetSum = 22
**Output:** true
**Explanation:** The root-to-leaf path with the target sum is shown.
**Example 2:**
**Input:** root = \[1,2,3\], targetSum = 5
**Output:** false
**Explanation:** There two root-to-leaf paths in the tree:
(1 --> 2): The sum is 3.
(1 --> 3): The sum is 4.
There is no root-to-leaf path with sum = 5.
**Example 3:**
**Input:** root = \[\], targetSum = 0
**Output:** false
**Explanation:** Since the tree is empty, there are no root-to-leaf paths.
**Constraints:**
* The number of nodes in the tree is in the range `[0, 5000]`.
* `-1000 <= Node.val <= 1000`
* `-1000 <= targetSum <= 1000` |
129 | Sum Root to Leaf Numbers | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn sum_numbers(root: Option<Rc<RefCell<TreeNode>>>) -> i32 { } } | You are given the `root` of a binary tree containing digits from `0` to `9` only.
Each root-to-leaf path in the tree represents a number.
* For example, the root-to-leaf path `1 -> 2 -> 3` represents the number `123`.
Return _the total sum of all root-to-leaf numbers_. Test cases are generated so that the answer will fit in a **32-bit** integer.
A **leaf** node is a node with no children.
**Example 1:**
**Input:** root = \[1,2,3\]
**Output:** 25
**Explanation:**
The root-to-leaf path `1->2` represents the number `12`.
The root-to-leaf path `1->3` represents the number `13`.
Therefore, sum = 12 + 13 = `25`.
**Example 2:**
**Input:** root = \[4,9,0,5,1\]
**Output:** 1026
**Explanation:**
The root-to-leaf path `4->9->5` represents the number 495.
The root-to-leaf path `4->9->1` represents the number 491.
The root-to-leaf path `4->0` represents the number 40.
Therefore, sum = 495 + 491 + 40 = `1026`.
**Constraints:**
* The number of nodes in the tree is in the range `[1, 1000]`.
* `0 <= Node.val <= 9`
* The depth of the tree will not exceed `10`. |
124 | Binary Tree Maximum Path Sum | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn max_path_sum(root: Option<Rc<RefCell<TreeNode>>>) -> i32 { } } | A **path** in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence **at most once**. Note that the path does not need to pass through the root.
The **path sum** of a path is the sum of the node's values in the path.
Given the `root` of a binary tree, return _the maximum **path sum** of any **non-empty** path_.
**Example 1:**
**Input:** root = \[1,2,3\]
**Output:** 6
**Explanation:** The optimal path is 2 -> 1 -> 3 with a path sum of 2 + 1 + 3 = 6.
**Example 2:**
**Input:** root = \[-10,9,20,null,null,15,7\]
**Output:** 42
**Explanation:** The optimal path is 15 -> 20 -> 7 with a path sum of 15 + 20 + 7 = 42.
**Constraints:**
* The number of nodes in the tree is in the range `[1, 3 * 104]`.
* `-1000 <= Node.val <= 1000` |
173 | Binary Search Tree Iterator | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } struct BSTIterator { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl BSTIterator { fn new(root: Option<Rc<RefCell<TreeNode>>>) -> Self { } fn next(&self) -> i32 { } fn has_next(&self) -> bool { } } /** * Your BSTIterator object will be instantiated and called as such: * let obj = BSTIterator::new(root); * let ret_1: i32 = obj.next(); * let ret_2: bool = obj.has_next(); */ | Implement the `BSTIterator` class that represents an iterator over the **[in-order traversal](https://en.wikipedia.org/wiki/Tree_traversal#In-order_(LNR))** of a binary search tree (BST):
* `BSTIterator(TreeNode root)` Initializes an object of the `BSTIterator` class. The `root` of the BST is given as part of the constructor. The pointer should be initialized to a non-existent number smaller than any element in the BST.
* `boolean hasNext()` Returns `true` if there exists a number in the traversal to the right of the pointer, otherwise returns `false`.
* `int next()` Moves the pointer to the right, then returns the number at the pointer.
Notice that by initializing the pointer to a non-existent smallest number, the first call to `next()` will return the smallest element in the BST.
You may assume that `next()` calls will always be valid. That is, there will be at least a next number in the in-order traversal when `next()` is called.
**Example 1:**
**Input**
\[ "BSTIterator ", "next ", "next ", "hasNext ", "next ", "hasNext ", "next ", "hasNext ", "next ", "hasNext "\]
\[\[\[7, 3, 15, null, null, 9, 20\]\], \[\], \[\], \[\], \[\], \[\], \[\], \[\], \[\], \[\]\]
**Output**
\[null, 3, 7, true, 9, true, 15, true, 20, false\]
**Explanation**
BSTIterator bSTIterator = new BSTIterator(\[7, 3, 15, null, null, 9, 20\]);
bSTIterator.next(); // return 3
bSTIterator.next(); // return 7
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 9
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 15
bSTIterator.hasNext(); // return True
bSTIterator.next(); // return 20
bSTIterator.hasNext(); // return False
**Constraints:**
* The number of nodes in the tree is in the range `[1, 105]`.
* `0 <= Node.val <= 106`
* At most `105` calls will be made to `hasNext`, and `next`.
**Follow up:**
* Could you implement `next()` and `hasNext()` to run in average `O(1)` time and use `O(h)` memory, where `h` is the height of the tree? |
222 | Count Complete Tree Nodes | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn count_nodes(root: Option<Rc<RefCell<TreeNode>>>) -> i32 { } } | Given the `root` of a **complete** binary tree, return the number of the nodes in the tree.
According to **[Wikipedia](http://en.wikipedia.org/wiki/Binary_tree#Types_of_binary_trees)**, every level, except possibly the last, is completely filled in a complete binary tree, and all nodes in the last level are as far left as possible. It can have between `1` and `2h` nodes inclusive at the last level `h`.
Design an algorithm that runs in less than `O(n)` time complexity.
**Example 1:**
**Input:** root = \[1,2,3,4,5,6\]
**Output:** 6
**Example 2:**
**Input:** root = \[\]
**Output:** 0
**Example 3:**
**Input:** root = \[1\]
**Output:** 1
**Constraints:**
* The number of nodes in the tree is in the range `[0, 5 * 104]`.
* `0 <= Node.val <= 5 * 104`
* The tree is guaranteed to be **complete**. |
236 | Lowest Common Ancestor of a Binary Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn lowest_common_ancestor(root: Option<Rc<RefCell<TreeNode>>>, p: Option<Rc<RefCell<TreeNode>>>, q: Option<Rc<RefCell<TreeNode>>>) -> Option<Rc<RefCell<TreeNode>>> { } } | Given a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.
According to the [definition of LCA on Wikipedia](https://en.wikipedia.org/wiki/Lowest_common_ancestor): "The lowest common ancestor is defined between two nodes `p` and `q` as the lowest node in `T` that has both `p` and `q` as descendants (where we allow **a node to be a descendant of itself**)."
**Example 1:**
**Input:** root = \[3,5,1,6,2,0,8,null,null,7,4\], p = 5, q = 1
**Output:** 3
**Explanation:** The LCA of nodes 5 and 1 is 3.
**Example 2:**
**Input:** root = \[3,5,1,6,2,0,8,null,null,7,4\], p = 5, q = 4
**Output:** 5
**Explanation:** The LCA of nodes 5 and 4 is 5, since a node can be a descendant of itself according to the LCA definition.
**Example 3:**
**Input:** root = \[1,2\], p = 1, q = 2
**Output:** 1
**Constraints:**
* The number of nodes in the tree is in the range `[2, 105]`.
* `-109 <= Node.val <= 109`
* All `Node.val` are **unique**.
* `p != q`
* `p` and `q` will exist in the tree. |
199 | Binary Tree Right Side View | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn right_side_view(root: Option<Rc<RefCell<TreeNode>>>) -> Vec<i32> { } } | Given the `root` of a binary tree, imagine yourself standing on the **right side** of it, return _the values of the nodes you can see ordered from top to bottom_.
**Example 1:**
**Input:** root = \[1,2,3,null,5,null,4\]
**Output:** \[1,3,4\]
**Example 2:**
**Input:** root = \[1,null,3\]
**Output:** \[1,3\]
**Example 3:**
**Input:** root = \[\]
**Output:** \[\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 100]`.
* `-100 <= Node.val <= 100` |
637 | Average of Levels in Binary Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn average_of_levels(root: Option<Rc<RefCell<TreeNode>>>) -> Vec<f64> { } } | Given the `root` of a binary tree, return _the average value of the nodes on each level in the form of an array_. Answers within `10-5` of the actual answer will be accepted.
**Example 1:**
**Input:** root = \[3,9,20,null,null,15,7\]
**Output:** \[3.00000,14.50000,11.00000\]
Explanation: The average value of nodes on level 0 is 3, on level 1 is 14.5, and on level 2 is 11.
Hence return \[3, 14.5, 11\].
**Example 2:**
**Input:** root = \[3,9,20,15,7\]
**Output:** \[3.00000,14.50000,11.00000\]
**Constraints:**
* The number of nodes in the tree is in the range `[1, 104]`.
* `-231 <= Node.val <= 231 - 1` |
102 | Binary Tree Level Order Traversal | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn level_order(root: Option<Rc<RefCell<TreeNode>>>) -> Vec<Vec<i32>> { } } | Given the `root` of a binary tree, return _the level order traversal of its nodes' values_. (i.e., from left to right, level by level).
**Example 1:**
**Input:** root = \[3,9,20,null,null,15,7\]
**Output:** \[\[3\],\[9,20\],\[15,7\]\]
**Example 2:**
**Input:** root = \[1\]
**Output:** \[\[1\]\]
**Example 3:**
**Input:** root = \[\]
**Output:** \[\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 2000]`.
* `-1000 <= Node.val <= 1000` |
103 | Binary Tree Zigzag Level Order Traversal | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn zigzag_level_order(root: Option<Rc<RefCell<TreeNode>>>) -> Vec<Vec<i32>> { } } | Given the `root` of a binary tree, return _the zigzag level order traversal of its nodes' values_. (i.e., from left to right, then right to left for the next level and alternate between).
**Example 1:**
**Input:** root = \[3,9,20,null,null,15,7\]
**Output:** \[\[3\],\[20,9\],\[15,7\]\]
**Example 2:**
**Input:** root = \[1\]
**Output:** \[\[1\]\]
**Example 3:**
**Input:** root = \[\]
**Output:** \[\]
**Constraints:**
* The number of nodes in the tree is in the range `[0, 2000]`.
* `-100 <= Node.val <= 100` |
530 | Minimum Absolute Difference in BST | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn get_minimum_difference(root: Option<Rc<RefCell<TreeNode>>>) -> i32 { } } | Given the `root` of a Binary Search Tree (BST), return _the minimum absolute difference between the values of any two different nodes in the tree_.
**Example 1:**
**Input:** root = \[4,2,6,1,3\]
**Output:** 1
**Example 2:**
**Input:** root = \[1,0,48,null,null,12,49\]
**Output:** 1
**Constraints:**
* The number of nodes in the tree is in the range `[2, 104]`.
* `0 <= Node.val <= 105`
**Note:** This question is the same as 783: [https://leetcode.com/problems/minimum-distance-between-bst-nodes/](https://leetcode.com/problems/minimum-distance-between-bst-nodes/) |
230 | Kth Smallest Element in a BST | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn kth_smallest(root: Option<Rc<RefCell<TreeNode>>>, k: i32) -> i32 { } } | Given the `root` of a binary search tree, and an integer `k`, return _the_ `kth` _smallest value (**1-indexed**) of all the values of the nodes in the tree_.
**Example 1:**
**Input:** root = \[3,1,4,null,2\], k = 1
**Output:** 1
**Example 2:**
**Input:** root = \[5,3,6,2,4,null,null,1\], k = 3
**Output:** 3
**Constraints:**
* The number of nodes in the tree is `n`.
* `1 <= k <= n <= 104`
* `0 <= Node.val <= 104`
**Follow up:** If the BST is modified often (i.e., we can do insert and delete operations) and you need to find the kth smallest frequently, how would you optimize? |
98 | Validate Binary Search Tree | // Definition for a binary tree node. // #[derive(Debug, PartialEq, Eq)] // pub struct TreeNode { // pub val: i32, // pub left: Option<Rc<RefCell<TreeNode>>>, // pub right: Option<Rc<RefCell<TreeNode>>>, // } // // impl TreeNode { // #[inline] // pub fn new(val: i32) -> Self { // TreeNode { // val, // left: None, // right: None // } // } // } use std::rc::Rc; use std::cell::RefCell; impl Solution { pub fn is_valid_bst(root: Option<Rc<RefCell<TreeNode>>>) -> bool { } } | Given the `root` of a binary tree, _determine if it is a valid binary search tree (BST)_.
A **valid BST** is defined as follows:
* The left subtree of a node contains only nodes with keys **less than** the node's key.
* The right subtree of a node contains only nodes with keys **greater than** the node's key.
* Both the left and right subtrees must also be binary search trees.
**Example 1:**
**Input:** root = \[2,1,3\]
**Output:** true
**Example 2:**
**Input:** root = \[5,1,4,null,null,3,6\]
**Output:** false
**Explanation:** The root node's value is 5 but its right child's value is 4.
**Constraints:**
* The number of nodes in the tree is in the range `[1, 104]`.
* `-231 <= Node.val <= 231 - 1` |
200 | Number of Islands | impl Solution { pub fn num_islands(grid: Vec<Vec<char>>) -> i32 { } } | Given an `m x n` 2D binary grid `grid` which represents a map of `'1'`s (land) and `'0'`s (water), return _the number of islands_.
An **island** is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water.
**Example 1:**
**Input:** grid = \[
\[ "1 ", "1 ", "1 ", "1 ", "0 "\],
\[ "1 ", "1 ", "0 ", "1 ", "0 "\],
\[ "1 ", "1 ", "0 ", "0 ", "0 "\],
\[ "0 ", "0 ", "0 ", "0 ", "0 "\]
\]
**Output:** 1
**Example 2:**
**Input:** grid = \[
\[ "1 ", "1 ", "0 ", "0 ", "0 "\],
\[ "1 ", "1 ", "0 ", "0 ", "0 "\],
\[ "0 ", "0 ", "1 ", "0 ", "0 "\],
\[ "0 ", "0 ", "0 ", "1 ", "1 "\]
\]
**Output:** 3
**Constraints:**
* `m == grid.length`
* `n == grid[i].length`
* `1 <= m, n <= 300`
* `grid[i][j]` is `'0'` or `'1'`. |
130 | Surrounded Regions | impl Solution { pub fn solve(board: &mut Vec<Vec<char>>) { } } | Given an `m x n` matrix `board` containing `'X'` and `'O'`, _capture all regions that are 4-directionally surrounded by_ `'X'`.
A region is **captured** by flipping all `'O'`s into `'X'`s in that surrounded region.
**Example 1:**
**Input:** board = \[\[ "X ", "X ", "X ", "X "\],\[ "X ", "O ", "O ", "X "\],\[ "X ", "X ", "O ", "X "\],\[ "X ", "O ", "X ", "X "\]\]
**Output:** \[\[ "X ", "X ", "X ", "X "\],\[ "X ", "X ", "X ", "X "\],\[ "X ", "X ", "X ", "X "\],\[ "X ", "O ", "X ", "X "\]\]
**Explanation:** Notice that an 'O' should not be flipped if:
- It is on the border, or
- It is adjacent to an 'O' that should not be flipped.
The bottom 'O' is on the border, so it is not flipped.
The other three 'O' form a surrounded region, so they are flipped.
**Example 2:**
**Input:** board = \[\[ "X "\]\]
**Output:** \[\[ "X "\]\]
**Constraints:**
* `m == board.length`
* `n == board[i].length`
* `1 <= m, n <= 200`
* `board[i][j]` is `'X'` or `'O'`. |
133 | Clone Graph | /* // Definition for a Node. class Node { public: int val; vector<Node*> neighbors; Node() { val = 0; neighbors = vector<Node*>(); } Node(int _val) { val = _val; neighbors = vector<Node*>(); } Node(int _val, vector<Node*> _neighbors) { val = _val; neighbors = _neighbors; } }; */ class Solution { public: Node* cloneGraph(Node* node) { } }; | Given a reference of a node in a **[connected](https://en.wikipedia.org/wiki/Connectivity_(graph_theory)#Connected_graph)** undirected graph.
Return a [**deep copy**](https://en.wikipedia.org/wiki/Object_copying#Deep_copy) (clone) of the graph.
Each node in the graph contains a value (`int`) and a list (`List[Node]`) of its neighbors.
class Node {
public int val;
public List neighbors;
}
**Test case format:**
For simplicity, each node's value is the same as the node's index (1-indexed). For example, the first node with `val == 1`, the second node with `val == 2`, and so on. The graph is represented in the test case using an adjacency list.
**An adjacency list** is a collection of unordered **lists** used to represent a finite graph. Each list describes the set of neighbors of a node in the graph.
The given node will always be the first node with `val = 1`. You must return the **copy of the given node** as a reference to the cloned graph.
**Example 1:**
**Input:** adjList = \[\[2,4\],\[1,3\],\[2,4\],\[1,3\]\]
**Output:** \[\[2,4\],\[1,3\],\[2,4\],\[1,3\]\]
**Explanation:** There are 4 nodes in the graph.
1st node (val = 1)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
2nd node (val = 2)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).
3rd node (val = 3)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).
4th node (val = 4)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).
**Example 2:**
**Input:** adjList = \[\[\]\]
**Output:** \[\[\]\]
**Explanation:** Note that the input contains one empty list. The graph consists of only one node with val = 1 and it does not have any neighbors.
**Example 3:**
**Input:** adjList = \[\]
**Output:** \[\]
**Explanation:** This an empty graph, it does not have any nodes.
**Constraints:**
* The number of nodes in the graph is in the range `[0, 100]`.
* `1 <= Node.val <= 100`
* `Node.val` is unique for each node.
* There are no repeated edges and no self-loops in the graph.
* The Graph is connected and all nodes can be visited starting from the given node. |
399 | Evaluate Division | impl Solution { pub fn calc_equation(equations: Vec<Vec<String>>, values: Vec<f64>, queries: Vec<Vec<String>>) -> Vec<f64> { } } | You are given an array of variable pairs `equations` and an array of real numbers `values`, where `equations[i] = [Ai, Bi]` and `values[i]` represent the equation `Ai / Bi = values[i]`. Each `Ai` or `Bi` is a string that represents a single variable.
You are also given some `queries`, where `queries[j] = [Cj, Dj]` represents the `jth` query where you must find the answer for `Cj / Dj = ?`.
Return _the answers to all queries_. If a single answer cannot be determined, return `-1.0`.
**Note:** The input is always valid. You may assume that evaluating the queries will not result in division by zero and that there is no contradiction.
**Example 1:**
**Input:** equations = \[\[ "a ", "b "\],\[ "b ", "c "\]\], values = \[2.0,3.0\], queries = \[\[ "a ", "c "\],\[ "b ", "a "\],\[ "a ", "e "\],\[ "a ", "a "\],\[ "x ", "x "\]\]
**Output:** \[6.00000,0.50000,-1.00000,1.00000,-1.00000\]
**Explanation:**
Given: _a / b = 2.0_, _b / c = 3.0_
queries are: _a / c = ?_, _b / a = ?_, _a / e = ?_, _a / a = ?_, _x / x = ?_
return: \[6.0, 0.5, -1.0, 1.0, -1.0 \]
**Example 2:**
**Input:** equations = \[\[ "a ", "b "\],\[ "b ", "c "\],\[ "bc ", "cd "\]\], values = \[1.5,2.5,5.0\], queries = \[\[ "a ", "c "\],\[ "c ", "b "\],\[ "bc ", "cd "\],\[ "cd ", "bc "\]\]
**Output:** \[3.75000,0.40000,5.00000,0.20000\]
**Example 3:**
**Input:** equations = \[\[ "a ", "b "\]\], values = \[0.5\], queries = \[\[ "a ", "b "\],\[ "b ", "a "\],\[ "a ", "c "\],\[ "x ", "y "\]\]
**Output:** \[0.50000,2.00000,-1.00000,-1.00000\]
**Constraints:**
* `1 <= equations.length <= 20`
* `equations[i].length == 2`
* `1 <= Ai.length, Bi.length <= 5`
* `values.length == equations.length`
* `0.0 < values[i] <= 20.0`
* `1 <= queries.length <= 20`
* `queries[i].length == 2`
* `1 <= Cj.length, Dj.length <= 5`
* `Ai, Bi, Cj, Dj` consist of lower case English letters and digits. |
207 | Course Schedule | impl Solution { pub fn can_finish(num_courses: i32, prerequisites: Vec<Vec<i32>>) -> bool { } } | There are a total of `numCourses` courses you have to take, labeled from `0` to `numCourses - 1`. You are given an array `prerequisites` where `prerequisites[i] = [ai, bi]` indicates that you **must** take course `bi` first if you want to take course `ai`.
* For example, the pair `[0, 1]`, indicates that to take course `0` you have to first take course `1`.
Return `true` if you can finish all courses. Otherwise, return `false`.
**Example 1:**
**Input:** numCourses = 2, prerequisites = \[\[1,0\]\]
**Output:** true
**Explanation:** There are a total of 2 courses to take.
To take course 1 you should have finished course 0. So it is possible.
**Example 2:**
**Input:** numCourses = 2, prerequisites = \[\[1,0\],\[0,1\]\]
**Output:** false
**Explanation:** There are a total of 2 courses to take.
To take course 1 you should have finished course 0, and to take course 0 you should also have finished course 1. So it is impossible.
**Constraints:**
* `1 <= numCourses <= 2000`
* `0 <= prerequisites.length <= 5000`
* `prerequisites[i].length == 2`
* `0 <= ai, bi < numCourses`
* All the pairs prerequisites\[i\] are **unique**. |
210 | Course Schedule II | impl Solution { pub fn find_order(num_courses: i32, prerequisites: Vec<Vec<i32>>) -> Vec<i32> { } } | There are a total of `numCourses` courses you have to take, labeled from `0` to `numCourses - 1`. You are given an array `prerequisites` where `prerequisites[i] = [ai, bi]` indicates that you **must** take course `bi` first if you want to take course `ai`.
* For example, the pair `[0, 1]`, indicates that to take course `0` you have to first take course `1`.
Return _the ordering of courses you should take to finish all courses_. If there are many valid answers, return **any** of them. If it is impossible to finish all courses, return **an empty array**.
**Example 1:**
**Input:** numCourses = 2, prerequisites = \[\[1,0\]\]
**Output:** \[0,1\]
**Explanation:** There are a total of 2 courses to take. To take course 1 you should have finished course 0. So the correct course order is \[0,1\].
**Example 2:**
**Input:** numCourses = 4, prerequisites = \[\[1,0\],\[2,0\],\[3,1\],\[3,2\]\]
**Output:** \[0,2,1,3\]
**Explanation:** There are a total of 4 courses to take. To take course 3 you should have finished both courses 1 and 2. Both courses 1 and 2 should be taken after you finished course 0.
So one correct course order is \[0,1,2,3\]. Another correct ordering is \[0,2,1,3\].
**Example 3:**
**Input:** numCourses = 1, prerequisites = \[\]
**Output:** \[0\]
**Constraints:**
* `1 <= numCourses <= 2000`
* `0 <= prerequisites.length <= numCourses * (numCourses - 1)`
* `prerequisites[i].length == 2`
* `0 <= ai, bi < numCourses`
* `ai != bi`
* All the pairs `[ai, bi]` are **distinct**. |
909 | Snakes and Ladders | impl Solution { pub fn snakes_and_ladders(board: Vec<Vec<i32>>) -> i32 { } } | You are given an `n x n` integer matrix `board` where the cells are labeled from `1` to `n2` in a [**Boustrophedon style**](https://en.wikipedia.org/wiki/Boustrophedon) starting from the bottom left of the board (i.e. `board[n - 1][0]`) and alternating direction each row.
You start on square `1` of the board. In each move, starting from square `curr`, do the following:
* Choose a destination square `next` with a label in the range `[curr + 1, min(curr + 6, n2)]`.
* This choice simulates the result of a standard **6-sided die roll**: i.e., there are always at most 6 destinations, regardless of the size of the board.
* If `next` has a snake or ladder, you **must** move to the destination of that snake or ladder. Otherwise, you move to `next`.
* The game ends when you reach the square `n2`.
A board square on row `r` and column `c` has a snake or ladder if `board[r][c] != -1`. The destination of that snake or ladder is `board[r][c]`. Squares `1` and `n2` do not have a snake or ladder.
Note that you only take a snake or ladder at most once per move. If the destination to a snake or ladder is the start of another snake or ladder, you do **not** follow the subsequent snake or ladder.
* For example, suppose the board is `[[-1,4],[-1,3]]`, and on the first move, your destination square is `2`. You follow the ladder to square `3`, but do **not** follow the subsequent ladder to `4`.
Return _the least number of moves required to reach the square_ `n2`_. If it is not possible to reach the square, return_ `-1`.
**Example 1:**
**Input:** board = \[\[-1,-1,-1,-1,-1,-1\],\[-1,-1,-1,-1,-1,-1\],\[-1,-1,-1,-1,-1,-1\],\[-1,35,-1,-1,13,-1\],\[-1,-1,-1,-1,-1,-1\],\[-1,15,-1,-1,-1,-1\]\]
**Output:** 4
**Explanation:**
In the beginning, you start at square 1 (at row 5, column 0).
You decide to move to square 2 and must take the ladder to square 15.
You then decide to move to square 17 and must take the snake to square 13.
You then decide to move to square 14 and must take the ladder to square 35.
You then decide to move to square 36, ending the game.
This is the lowest possible number of moves to reach the last square, so return 4.
**Example 2:**
**Input:** board = \[\[-1,-1\],\[-1,3\]\]
**Output:** 1
**Constraints:**
* `n == board.length == board[i].length`
* `2 <= n <= 20`
* `board[i][j]` is either `-1` or in the range `[1, n2]`.
* The squares labeled `1` and `n2` do not have any ladders or snakes. |
433 | Minimum Genetic Mutation | impl Solution { pub fn min_mutation(start_gene: String, end_gene: String, bank: Vec<String>) -> i32 { } } | A gene string can be represented by an 8-character long string, with choices from `'A'`, `'C'`, `'G'`, and `'T'`.
Suppose we need to investigate a mutation from a gene string `startGene` to a gene string `endGene` where one mutation is defined as one single character changed in the gene string.
* For example, `"AACCGGTT " --> "AACCGGTA "` is one mutation.
There is also a gene bank `bank` that records all the valid gene mutations. A gene must be in `bank` to make it a valid gene string.
Given the two gene strings `startGene` and `endGene` and the gene bank `bank`, return _the minimum number of mutations needed to mutate from_ `startGene` _to_ `endGene`. If there is no such a mutation, return `-1`.
Note that the starting point is assumed to be valid, so it might not be included in the bank.
**Example 1:**
**Input:** startGene = "AACCGGTT ", endGene = "AACCGGTA ", bank = \[ "AACCGGTA "\]
**Output:** 1
**Example 2:**
**Input:** startGene = "AACCGGTT ", endGene = "AAACGGTA ", bank = \[ "AACCGGTA ", "AACCGCTA ", "AAACGGTA "\]
**Output:** 2
**Constraints:**
* `0 <= bank.length <= 10`
* `startGene.length == endGene.length == bank[i].length == 8`
* `startGene`, `endGene`, and `bank[i]` consist of only the characters `['A', 'C', 'G', 'T']`. |
127 | Word Ladder | impl Solution { pub fn ladder_length(begin_word: String, end_word: String, word_list: Vec<String>) -> i32 { } } | A **transformation sequence** from word `beginWord` to word `endWord` using a dictionary `wordList` is a sequence of words `beginWord -> s1 -> s2 -> ... -> sk` such that:
* Every adjacent pair of words differs by a single letter.
* Every `si` for `1 <= i <= k` is in `wordList`. Note that `beginWord` does not need to be in `wordList`.
* `sk == endWord`
Given two words, `beginWord` and `endWord`, and a dictionary `wordList`, return _the **number of words** in the **shortest transformation sequence** from_ `beginWord` _to_ `endWord`_, or_ `0` _if no such sequence exists._
**Example 1:**
**Input:** beginWord = "hit ", endWord = "cog ", wordList = \[ "hot ", "dot ", "dog ", "lot ", "log ", "cog "\]
**Output:** 5
**Explanation:** One shortest transformation sequence is "hit " -> "hot " -> "dot " -> "dog " -> cog ", which is 5 words long.
**Example 2:**
**Input:** beginWord = "hit ", endWord = "cog ", wordList = \[ "hot ", "dot ", "dog ", "lot ", "log "\]
**Output:** 0
**Explanation:** The endWord "cog " is not in wordList, therefore there is no valid transformation sequence.
**Constraints:**
* `1 <= beginWord.length <= 10`
* `endWord.length == beginWord.length`
* `1 <= wordList.length <= 5000`
* `wordList[i].length == beginWord.length`
* `beginWord`, `endWord`, and `wordList[i]` consist of lowercase English letters.
* `beginWord != endWord`
* All the words in `wordList` are **unique**. |
208 | Implement Trie (Prefix Tree) | struct Trie { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl Trie { fn new() -> Self { } fn insert(&self, word: String) { } fn search(&self, word: String) -> bool { } fn starts_with(&self, prefix: String) -> bool { } } /** * Your Trie object will be instantiated and called as such: * let obj = Trie::new(); * obj.insert(word); * let ret_2: bool = obj.search(word); * let ret_3: bool = obj.starts_with(prefix); */ | A [**trie**](https://en.wikipedia.org/wiki/Trie) (pronounced as "try ") or **prefix tree** is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker.
Implement the Trie class:
* `Trie()` Initializes the trie object.
* `void insert(String word)` Inserts the string `word` into the trie.
* `boolean search(String word)` Returns `true` if the string `word` is in the trie (i.e., was inserted before), and `false` otherwise.
* `boolean startsWith(String prefix)` Returns `true` if there is a previously inserted string `word` that has the prefix `prefix`, and `false` otherwise.
**Example 1:**
**Input**
\[ "Trie ", "insert ", "search ", "search ", "startsWith ", "insert ", "search "\]
\[\[\], \[ "apple "\], \[ "apple "\], \[ "app "\], \[ "app "\], \[ "app "\], \[ "app "\]\]
**Output**
\[null, null, true, false, true, null, true\]
**Explanation**
Trie trie = new Trie();
trie.insert( "apple ");
trie.search( "apple "); // return True
trie.search( "app "); // return False
trie.startsWith( "app "); // return True
trie.insert( "app ");
trie.search( "app "); // return True
**Constraints:**
* `1 <= word.length, prefix.length <= 2000`
* `word` and `prefix` consist only of lowercase English letters.
* At most `3 * 104` calls **in total** will be made to `insert`, `search`, and `startsWith`. |
211 | Design Add and Search Words Data Structure | struct WordDictionary { } /** * `&self` means the method takes an immutable reference. * If you need a mutable reference, change it to `&mut self` instead. */ impl WordDictionary { fn new() -> Self { } fn add_word(&self, word: String) { } fn search(&self, word: String) -> bool { } } /** * Your WordDictionary object will be instantiated and called as such: * let obj = WordDictionary::new(); * obj.add_word(word); * let ret_2: bool = obj.search(word); */ | Design a data structure that supports adding new words and finding if a string matches any previously added string.
Implement the `WordDictionary` class:
* `WordDictionary()` Initializes the object.
* `void addWord(word)` Adds `word` to the data structure, it can be matched later.
* `bool search(word)` Returns `true` if there is any string in the data structure that matches `word` or `false` otherwise. `word` may contain dots `'.'` where dots can be matched with any letter.
**Example:**
**Input**
\[ "WordDictionary ", "addWord ", "addWord ", "addWord ", "search ", "search ", "search ", "search "\]
\[\[\],\[ "bad "\],\[ "dad "\],\[ "mad "\],\[ "pad "\],\[ "bad "\],\[ ".ad "\],\[ "b.. "\]\]
**Output**
\[null,null,null,null,false,true,true,true\]
**Explanation**
WordDictionary wordDictionary = new WordDictionary();
wordDictionary.addWord( "bad ");
wordDictionary.addWord( "dad ");
wordDictionary.addWord( "mad ");
wordDictionary.search( "pad "); // return False
wordDictionary.search( "bad "); // return True
wordDictionary.search( ".ad "); // return True
wordDictionary.search( "b.. "); // return True
**Constraints:**
* `1 <= word.length <= 25`
* `word` in `addWord` consists of lowercase English letters.
* `word` in `search` consist of `'.'` or lowercase English letters.
* There will be at most `2` dots in `word` for `search` queries.
* At most `104` calls will be made to `addWord` and `search`. |
212 | Word Search II | impl Solution { pub fn find_words(board: Vec<Vec<char>>, words: Vec<String>) -> Vec<String> { } } | Given an `m x n` `board` of characters and a list of strings `words`, return _all words on the board_.
Each word must be constructed from letters of sequentially adjacent cells, where **adjacent cells** are horizontally or vertically neighboring. The same letter cell may not be used more than once in a word.
**Example 1:**
**Input:** board = \[\[ "o ", "a ", "a ", "n "\],\[ "e ", "t ", "a ", "e "\],\[ "i ", "h ", "k ", "r "\],\[ "i ", "f ", "l ", "v "\]\], words = \[ "oath ", "pea ", "eat ", "rain "\]
**Output:** \[ "eat ", "oath "\]
**Example 2:**
**Input:** board = \[\[ "a ", "b "\],\[ "c ", "d "\]\], words = \[ "abcb "\]
**Output:** \[\]
**Constraints:**
* `m == board.length`
* `n == board[i].length`
* `1 <= m, n <= 12`
* `board[i][j]` is a lowercase English letter.
* `1 <= words.length <= 3 * 104`
* `1 <= words[i].length <= 10`
* `words[i]` consists of lowercase English letters.
* All the strings of `words` are unique. |
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