Leetcode 1262: Greatest Sum Divisible by Three
Given an array of integers, find the maximum sum of any subsequence where the sum is divisible by 3.
Problem
Approach
Steps
Complexity
Input: The input consists of a single integer array 'nums'.
Example: [2, 5, 8, 7, 3]
Constraints:
• 1 <= nums.length <= 4 * 10^4
• 1 <= nums[i] <= 10^4
Output: The output is a single integer representing the maximum sum of any subsequence that is divisible by 3.
Example: 21
Constraints:
• The sum must be divisible by 3.
Goal: Find the maximum possible sum of a subsequence divisible by 3.
Steps:
• 1. Initialize a dynamic programming array 'dp' to store the maximum sum for each remainder modulo 3.
• 2. Iterate through the numbers in 'nums' and update 'dp' based on the current number.
• 3. Return the value in 'dp[0]', which represents the maximum sum divisible by 3.
Goal: The constraints ensure that the array length and values are within a manageable range.
Steps:
• 1 <= nums.length <= 4 * 10^4
• 1 <= nums[i] <= 10^4
Assumptions:
• The input is a valid integer array with values within the given constraints.
• A subsequence is any set of elements in the array, not necessarily contiguous.
• Input: [2, 5, 8, 7, 3]
• Explanation: The sum of the numbers 2, 5, 8, and 7 equals 21, which is divisible by 3. Thus, 21 is the maximum sum.
Approach: Use dynamic programming to track the maximum possible sum for each remainder when divided by 3.
Observations:
• The problem can be reduced to finding the maximum sum for each modulo 3 remainder.
• We can use dynamic programming to keep track of the best possible sums for remainders 0, 1, and 2.
Steps:
• 1. Initialize a dynamic programming array 'dp' of size 3 to keep track of the best sums for each remainder.
• 2. For each number in the array, update the 'dp' array to reflect the best possible sums.
• 3. The maximum sum divisible by 3 will be stored in 'dp[0]'.
Empty Inputs:
• The array is not empty based on the problem constraints.
Large Inputs:
• Ensure that the solution handles arrays with a size up to 40,000 efficiently.
Special Values:
• All numbers in the array could be divisible by 3, or none could be.
Constraints:
• The input array is guaranteed to have at least one element.
int maxSumDivThree(vector<int>& nums) {
int n = nums.size();
vector<int> dp(3, 0);
for(int a : nums)
for(int i : vector<int>(dp))
dp[(i + a) % 3] = max(dp[(i + a) % 3], i + a);
return dp[0];
}
1 : Function Definition
int maxSumDivThree(vector<int>& nums) {
This defines the function `maxSumDivThree`, which takes a vector of integers `nums` and returns the maximum sum divisible by 3 that can be obtained by selecting elements from the list.
2 : Variable Declaration
int n = nums.size();
This declares an integer `n` to store the size of the input array `nums`.
3 : Vector Initialization
vector<int> dp(3, 0);
This initializes a vector `dp` of size 3, with all values set to 0. The vector will store the maximum sum possible for each remainder when divided by 3.
4 : Loop (Array Iteration)
for(int a : nums)
This loop iterates over each element `a` in the input array `nums`.
5 : Loop (Dynamic Programming Update)
for(int i : vector<int>(dp))
This loop iterates over the current values in the `dp` vector to update the possible sums with the current element `a`.
6 : Dynamic Programming Transition
dp[(i + a) % 3] = max(dp[(i + a) % 3], i + a);
This updates the `dp` vector by calculating the maximum sum for each possible remainder modulo 3 after adding the current element `a`.
7 : Return Statement
return dp[0];
This returns the value stored in `dp[0]`, which represents the maximum sum divisible by 3 after processing all elements.
Best Case: O(N), where N is the number of elements in the array.
Average Case: O(N) for each iteration over the array.
Worst Case: O(N), where N is the number of elements in the array.
Description: The time complexity is linear because we iterate over the array once.
Best Case: O(1), since the space usage does not depend on the input size.
Worst Case: O(1), since we only store a fixed-size array (dp) of size 3.
Description: The space complexity is constant because we only need a fixed amount of extra space.
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