Given n non-negative integers representing the histogram's bar height where the width of each bar is 1, find the area of largest rectangle in the histogram.

Above is a histogram where width of each bar is 1, given height = [2,1,5,6,2,3].

The largest rectangle is shown in the shaded area, which has area = 10 unit.
For example,
Given heights = [2,1,5,6,2,3],
return 10.
Another exammple
The O(n2) solution:
http://www.geeksforgeeks.org/largest-rectangle-under-histogram/
For each bar extending it as far as possible, time complexity O(n2);
The O(nlgn) solution:
http://www.geeksforgeeks.org/largest-rectangle-under-histogram/
We define a method findMax(int start, int end, int[] h)
first we find the minValue of the h, h[k],
we pick up the max from the findMax(start,k-1,h), findMax(k+1,end,h) and (end - start + 1) * h[k] as the result.
how to find the minValue ? Scan in O(n) , OR Segment Tree in O(lgn).
The O(n) solution:
maintain a Monotone stack, increasing:
do the following step:
1) Create an empty stack.
2) Start from first bar, and do following for every bar ‘hist[i]’ where ‘i’ varies from 0 to n-1.
……a) If stack is empty or hist[i] is higher than the bar at top of stack, then push ‘i’ to stack.
……b) If this bar is smaller than the top of stack, then keep removing the top of stack while top of the stack is greater. Let the removed bar be hist[tp]. Calculate area of rectangle with hist[tp] as smallest bar. For hist[tp], the ‘left index’ is previous (previous to tp) item in stack and ‘right index’ is ‘i’ (current index).
3) If the stack is not empty, then one by one remove all bars from stack and do step 2.b for every removed bar.
Code:
public class Solution {
public int largestRectangleArea(int[] h) {
int ret = 0;
Stack<Integer> s = new Stack<>();
for(int i = 0 ; i < h.length; i++){
if(s.empty() || h[s.peek()] <= h[i]){
s.push(i);
} else {
while(!s.empty() && h[s.peek()] >= h[i]){
int index = s.pop();
int left = s.empty() ? -1 : s.peek();
ret = Math.max(ret,h[index] * (i - 1 - left)); // (i - 1) right index included, left left index excluded,
}
s.push(i);
}
}
while(!s.empty()){
int index = s.pop();
int left = s.empty() ? -1 : s.peek();
ret = Math.max(ret,h[index] * (h.length - 1 - left)); // h.length - 1 right index included, left left index excluded
}
return ret;
}
}
最大直方图面积算法
本文介绍了一种求解最大直方图面积的问题,并提供了三种不同时间复杂度的解决方案:O(n²)、O(nlogn) 和 O(n)。其中,O(n) 解决方案使用单调栈来实现,通过一系列步骤确保了效率。

275

被折叠的 条评论
为什么被折叠?



