import java.util.*; public class Solution { p...

बनाया गया: 12 जुलाई 2025

का उपयोग करके उत्तर दिया गया O4 Mini High द्वारा Chat01

प्रश्न

import java.util.*;

public class Solution {
public static long maxSumAfterOps(int[] a, int[] b) {
int n = a.length, m = b.length;
long best = Long.MIN_VALUE;
int totalMasks = (int) Math.pow(3, m);

text
for (int mask = 0; mask < totalMasks; mask++) { int[] ops = new int[m]; int t = mask; for (int j = 0; j < m; j++, t /= 3) { ops[j] = t % 3; // 0→op1, 1→op2, 2→op3 } long[] cur = new long[n]; for (int i = 0; i < n; i++) cur[i] = a[i]; for (int j = 0; j < m; j++) { long x = b[j]; switch (ops[j]) { case 0: long bestLocal = Long.MIN_VALUE; int bestL = 0, bestR = -1; for (int L = 0; L < n; L++) { for (int R = L; R < n; R++) { long sum = 0; for (int i = 0; i < n; i++) { long v = cur[i]; if (i >= L && i <= R) v ^= x; else if (i > R) v ^= 2 * x; sum += v; } if (sum > bestLocal) { bestLocal = sum; bestL = L; bestR = R; } } } // apply best op1 for (int i = 0; i < n; i++) { if (i >= bestL && i <= bestR) cur[i] ^= x; else if (i > bestR) cur[i] ^= 2 * x; } break; case 1: long bestLocal2 = Long.MIN_VALUE; int bestStart = 0; for (int L = 0; L < n; L++) { long sum = 0; for (int i = 0; i < n; i++) { long v = cur[i]; if (i >= L) v ^= 2 * x; sum += v; } if (sum > bestLocal2) { bestLocal2 = sum; bestStart = L; } } // apply best op2 for (int i = bestStart; i < n; i++) { cur[i] ^= 2 * x; } break; case 2: // do nothing break; } } long total = 0; for (long v : cur) total += v; best = Math.max(best, total); } return best; } public static void main(String[] args) { int[] a = {5, 1, 3, 7}; int[] b = {2, 4}; System.out.println(maxSumAfterOps(a, b)); }

}
can you optimize for l and r currently using brute force but I want it to be O((3^m).n)

विचार

Optimizing loop selection

Okay, the user wants to optimize their algorithm where currently L and R are brute-forced within O(n^3). The goal is to get this down to O(3^m * n). To achieve this, I'll look into using prefix sums and dynamic programming to speed up the selection of L and R. Instead of testing every combination, we need to find a way to select a segment [L, R] that maximizes the improvement to the sum efficiently in linear time. Let’s start by deriving the formula for this step.

Optimizing L and R selection

I'm trying to optimize the calculation for op1 between segments [L, R]. The goal is to maximize the sum of the transformed cur array after applying XOR operations. For this, I need to calculate the increase in the sum after XOR, which translates to maximizing Δ(L,R), the change in the sum.

By precomputing arrays a[i] and b[i], I can efficiently calculate Δ(L,R) as the sum over those arrays and the suffix sum of b[i]. To maximize the sum, I'll compute the prefix sum of a[i] values and identify the best L for each R.

Optimizing op1 and op2

For op1, the idea is to precompute prefix and suffix sums to reduce the search time for L and R to O(n). I’ll also implement dynamic programming to find the best segment, apply XOR operations, and then accumulate the sum. For op2, I can similarly optimize it by calculating the best starting point L for maximizing the sum and applying XOR. This results in O(n) for each operation.

I’ll propose an approach using prefix and suffix sums for fast L, R selection in the main loop and show the optimized code.

Thought for 15 seconds

उत्तर

You can eliminate the O(n3)O(n^3) brute-force over (L,R)(L,R) in your “op1” case by observing that

Δ(L,R)  =  i=LR((cur[i]x)cur[i])  +  i=R+1n1((cur[i]2x)cur[i])\Delta(L,R) \;=\; \sum_{i=L}^{R}\bigl((\text{cur}[i]\oplus x)-\text{cur}[i]\bigr) \;+\; \sum_{i=R+1}^{n-1}\bigl((\text{cur}[i]\oplus 2x)-\text{cur}[i]\bigr)

Let

java
// the gain if you XOR x at position i long[] diff1 = new long[n]; for(int i=0;i<n;i++) diff1[i] = (cur[i]^x) - cur[i]; // the gain if you XOR 2*x at position i long[] diff2 = new long[n]; for(int i=0;i<n;i++) diff2[i] = (cur[i]^(2*x)) - cur[i];

Then

Δ(L,R)  =  i=LRdiff1[i]  +  i=R+1n1diff2[i]  =  (prefix1[R]prefix1[L1])+suffix2[R+1].\Delta(L,R) \;=\; \sum_{i=L}^R diff1[i]\;+\;\sum_{i=R+1}^{n-1}diff2[i] \;=\; (\text{prefix1}[R]-\text{prefix1}[L-1]) + \text{suffix2}[R+1].

You can find

max0LR<nΔ(L,R)\max_{0\le L\le R<n}\Delta(L,R)

in a single O(n)O(n) scan by maintaining:

  • a running prefix-sum of diff1,
  • the minimum prefix-sum seen so far (to pick the best LL),
  • a precomputed suffix-sum array of diff2.

Likewise your “op2” (case 1) is just “pick the best suffix” of ((cur[i]2x)cur[i])\bigl((\text{cur}[i]\oplus 2x) - \text{cur}[i]\bigr), also doable in O(n)O(n).

Putting it all together gives you an O(3mn)O(3^m\cdot n) (up to a factor mm) solution:

java
public static long maxSumAfterOps(int[] a, int[] b) { int n = a.length, m = b.length; long best = Long.MIN_VALUE; int totalMasks = (int) Math.pow(3, m); for (int mask = 0; mask < totalMasks; mask++) { // decode ops int[] ops = new int[m]; for (int j = 0, t = mask; j < m; j++, t /= 3) { ops[j] = t % 3; } // working copy of a[] long[] cur = new long[n]; for (int i = 0; i < n; i++) cur[i] = a[i]; // apply each operation in O(n) for (int j = 0; j < m; j++) { long x = b[j]; if (ops[j] == 0) { // -- op1: choose best (L,R) long[] diff1 = new long[n], suffix2 = new long[n+1]; for (int i = 0; i < n; i++) { diff1[i] = (cur[i] ^ x) - cur[i]; } suffix2[n] = 0; for (int i = n - 1; i >= 0; i--) { suffix2[i] = suffix2[i+1] + ((cur[i] ^ (2*x)) - cur[i]); } long maxGain = Long.MIN_VALUE; long prefixSum = 0, minPrefix = 0; int minPos = -1, bestL = 0, bestR = 0; for (int r = 0; r < n; r++) { prefixSum += diff1[r]; long gain = prefixSum - minPrefix + suffix2[r+1]; if (gain > maxGain) { maxGain = gain; bestL = minPos + 1; bestR = r; } if (prefixSum < minPrefix) { minPrefix = prefixSum; minPos = r; } } // apply the XORs in one more O(n) for (int i = 0; i < n; i++) { if (i >= bestL && i <= bestR) cur[i] ^= x; else if (i > bestR) cur[i] ^= 2*x; } } else if (ops[j] == 1) { // -- op2: choose best suffix long[] diff2 = new long[n]; for (int i = 0; i < n; i++) { diff2[i] = (cur[i] ^ (2*x)) - cur[i]; } long maxGain = Long.MIN_VALUE; long suffixSum = 0; int bestStart = 0; for (int i = n - 1; i >= 0; i--) { suffixSum += diff2[i]; if (suffixSum > maxGain) { maxGain = suffixSum; bestStart = i; } } for (int i = bestStart; i < n; i++) { cur[i] ^= 2*x; } } // else ops[j] == 2 → do nothing } // compute final total long total = 0; for (long v : cur) total += v; best = Math.max(best, total); } return best; }

Complexity breakdown

  • There are   3m  \;3^m\; masks.
  • For each mask, each of the mm ops takes O(n)O(n) to
    1. build the diff arrays,
    2. scan once to pick optimal (L,R)(L,R) or suffix,
    3. apply the XOR in O(n)O(n).
  • Overall O(3m×m×n)O\bigl(3^m \times m \times n\bigr), which for small constant mm is effectively O(3mn)O(3^m\cdot n).

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