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In this approach, we perform the following steps:
Time Complexity: O((n + m) log n) where n = number of items and m = number of queries.
Space Complexity: O(n) for storing the processed items data.
1using System;
2using System.Collections.Generic;
3
4public class Solution {
5 public int[] MaxBeautyForQueries(int[][] items, int[] queries) {
6 Array.Sort(items, (a, b) => a[0] - b[0]);
7 int[] maxBeauties = new int[items.Length];
8 int maxBeauty = 0;
9
10 for (int i = 0; i < items.Length; i++) {
11 maxBeauty = Math.Max(maxBeauty, items[i][1]);
12 maxBeauties[i] = maxBeauty;
13 }
14
15 int[] result = new int[queries.Length];
16 for (int i = 0; i < queries.Length; i++) {
17 int idx = Array.BinarySearch(items, new int[]{queries[i], int.MaxValue}, Comparer<int[]>.Create((a, b) => a[0] - b[0]));
18 if (idx < 0) idx = ~idx - 1;
19 result[i] = idx >= 0 ? maxBeauties[idx] : 0;
20 }
21 return result;
22 }
23
24 public static void Main(string[] args) {
25 var solution = new Solution();
26 int[][] items = new int[][] { new int[] {1,2}, new int[] {3,2}, new int[] {2,4}, new int[] {5,6}, new int[] {3,5} };
27 int[] queries = new int[] { 1, 2, 3, 4, 5, 6 };
28 int[] result = solution.MaxBeautyForQueries(items, queries);
29 Console.WriteLine(string.Join(" ", result));
30 }
31}
This method leverages C# collections and algorithms to supply a projected maximum beauty for sorted item data. Here, a binary search optimizes query resolution by narrowing potential indexes efficiently.
Coordinate compression can be used to reduce problem complexity when dealing with large range values like prices. In this approach:
Time Complexity: O((n + m) log(n + m)) due to sorting during compression.
Space Complexity: O(n + m) for storing all price points and decision arrays.
JavaScript code makes full use of Sets and Maps for efficient price compression, mapping these prices to maintained beauty maximums for fast index-based query responses. This enables quick filter and calculate actions post-compression.