문제

Located on a line are N segments, numbered from 0 to N − 1, whose positions are given in arrays A and B. For each I (0 ≤ I < N) the position of segment I is from A[I] to BI. The segments are sorted by their ends, which means that B[K] ≤ B[K + 1] for K such that 0 ≤ K < N − 1.

Two segments I and J, such that I ≠ J, are overlapping if they share at least one common point. In other words, A[I] ≤ A[J] ≤ B[I] or A[J] ≤ A[I] ≤ B[J].

We say that the set of segments is non-overlapping if it contains no two overlapping segments. The goal is to find the size of a non-overlapping set containing the maximal number of segments.

For example, consider arrays A, B such that:

A[0] = 1    B[0] = 5
A[1] = 3    B[1] = 6
A[2] = 7    B[2] = 8
A[3] = 9    B[3] = 9
A[4] = 9    B[4] = 10

The segments are shown in the figure below.

The size of a non-overlapping set containing a maximal number of segments is 3. For example, possible sets are {0, 2, 3}, {0, 2, 4}, {1, 2, 3} or {1, 2, 4}. There is no non-overlapping set with four segments.

Write a function:

class Solution { public int solution(int[] A, int[] B); }

that, given two arrays A and B consisting of N integers, returns the size of a non-overlapping set containing a maximal number of segments.

For example, given arrays A, B shown above, the function should return 3, as explained above.

Write an efficient algorithm for the following assumptions:

N is an integer within the range [0..30,000];
each element of arrays A and B is an integer within the range [0..1,000,000,000];
A[I] ≤ B[I], for each I (0 ≤ I < N);
B[K] ≤ B[K + 1], for each K (0 ≤ K < N − 1).
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코드

// you can also use imports, for example:
// import java.util.*;

// you can write to stdout for debugging purposes, e.g.
// System.out.println("this is a debug message");
import java.util.*;
class Solution {
    public int solution(int[] A, int[] B) {

        int count = 0;
        int endPoint = Integer.MIN_VALUE;

        for(int i=0; i<A.length; i++){
            if(A[i]>endPoint){
                count ++;
                endPoint = B[i];
            }
        }
        return count;
        // Implement your solution here
    }
}