CPMagical CP Library

Problem solution · Java

Maximum Coins From K Consecutive Bags

Maximum Coins From K Consecutive Bags: a Java solution using binary search. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Binary search
Source
walkccc LeetCode Solutions
Length
58 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Binary search

For Maximum Coins From K Consecutive Bags, the implementation exploits a monotonic condition to discard half of the remaining search space after every check.

  1. Identify the ordered answer range or sorted search domain.
  2. Write a predicate whose truth changes only once.
  3. Move the appropriate boundary after each midpoint check and return the final feasible position.

Code notes

  • 58 lines of Java from the credited upstream file 3413.java.
  • The implementation visibly relies on sequence storage.
  • 3 loop blocks detected.

Complexity

Multiply the logarithmic number of midpoint checks by the cost of one predicate evaluation.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeMaximum Coins From K Consecutive Bags · JavaJava
Use this to learn the idea, then write your own version.
class Solution {  public long maximumCoins(int[][] coins, int k) {    int[][] negatedCoins = negateLeftRight(coins);    return Math.max(slide(coins, k), slide(negatedCoins, k));  }   private int[][] negateLeftRight(int[][] coins) {    int[][] res = new int[coins.length][3];    for (int i = 0; i < coins.length; ++i) {      final int l = coins[i][0];      final int r = coins[i][1];      final int c = coins[i][2];      res[i][0] = -r;      res[i][1] = -l;      res[i][2] = c;    }    return res;  }   private long slide(int[][] coins, int k) {    long res = 0;    long windowSum = 0;    int j = 0;     Arrays.sort(coins, Comparator.comparingInt((int[] coin) -> coin[0]));     for (int[] coin : coins) {      final int li = coin[0];      final int ri = coin[1];      final int ci = coin[2];      final int rightBoundary = li + k;       // [lj, rj] is fully in [li..li + k).      while (j + 1 < coins.length && coins[j + 1][0] < rightBoundary) {        final int lj = coins[j][0];        final int rj = coins[j][1];        final int cj = coins[j][2];        windowSum += (long) (rj - lj + 1) * cj;        ++j;      }       // [lj, rj] may be partially in [l..l + k).      long last = 0;      if (j < coins.length && coins[j][0] < rightBoundary) {        final int lj = coins[j][0];        final int rj = coins[j][1];        final int cj = coins[j][2];        last = (long) (Math.min(rightBoundary - 1, rj) - lj + 1) * cj;      }       res = Math.max(res, windowSum + last);      windowSum -= (long) (ri - li + 1) * ci;    }     return res;  }}

Keep practising

Did this explanation save you time? I'm a Grade 11 student building this free library to make difficult algorithms easier to understand.

Buy me a coffee ↗