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This approach involves iterating over each element of the array, applying the provided function to determine the key for each element, and then organizing elements in an object based on these keys.
Time Complexity: O(n * m) where n is the number of elements and m is the average length of strings (due to strcmp operation).
Space Complexity: O(n) for storage of key-value mappings.
1This Python solution uses a dictionary to perform grouping. It iterates over items, applies the function to determine keys, and aggregates accordingly.
This approach recursively divides the array, processes elements with memoization to store previously encountered keys, thus reducing redundant calculations, which optimizes processing of large arrays.
Time Complexity: O(n * m), where m is affected by recursion depth.
Space Complexity: O(n) for memo storage and call stack.
1using System;
2using System.Collections.Generic;
3
4public class RecursiveGroupBy {
5 public static Dictionary<string, List<T>> RecursiveGroup<T>(List<T> list, Func<T, string> fn, Dictionary<string, List<T>> memo = null) {
6 if (memo == null) memo = new Dictionary<string, List<T>>();
7 if (list.Count == 0) return memo;
8 T item = list[0];
9 string key = fn(item);
10 if (!memo.ContainsKey(key)) {
11 memo[key] = new List<T>();
12 }
13 memo[key].Add(item);
14 return RecursiveGroup(list.GetRange(1, list.Count - 1), fn, memo);
15 }
16}This C# version exemplifies recursive decomposition of a list to build a grouping map, tapping into memoization for key tracking across recursive paths.