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This approach involves using a hashmap (or dictionary) to keep track of members that belong to groups of the same size. You iterate over the groupSizes array, and for each person, add their ID to a list associated with their required group size in the hashmap. Once a list reaches the intended group size, you add it to the result and reset the list for that group size.
The time complexity is O(n), where n is the number of people, because each person is processed exactly once. The space complexity is also O(n), because we store the people in an intermediate dictionary before creating the result.
1#include <stdio.h>
2#include <stdlib.h>
3#include <string.h>
4
5// A helper struct for mapping size with people's list
6typedef struct {
This C implementation involves using an array of structs to maintain lists of people's indices based on their required group sizes. Similar to hashmap-based implementations, each list is filled until it reaches the correct size, at which point it is added to the result array. Memory management is required for dynamic arrays.
This approach uses direct index management without using a hashmap, iterating through the list and directly placing IDs into result groups once a correct-sized group is filled, simplifying the storage by maintaining an array or linked list for each group size.
The time complexity is O(n), and similarly, space complexity is O(n) due to linear storage requirements.
The JavaScript solution demonstrates indexed list management with arrays by required group size, ensuring reduced complexity by local groupings and direct listings.