Largest Rectangular Area in a Histogram

本文介绍了一种高效算法来求解直方图中最大矩形面积问题,通过维护一个栈来追踪每个柱状的高度,实现了O(n)的时间复杂度。文中详细解释了算法流程,并提供了Java代码实现。

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题目地址:https://oj.leetcode.com/problems/largest-rectangle-in-histogram/ ,刚开始其实没做这个题,而是在做https://oj.leetcode.com/problems/maximal-rectangle/其中很重要的一步就是用到Largest Rectangular Area in a Histogram题中的算法。

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 height = [2,1,5,6,2,3],
return 10.

参考这篇博文思想:http://www.geeksforgeeks.org/largest-rectangle-under-histogram/

histogram

For example, consider the following histogram with 7 bars of heights {6, 2, 5, 4, 5, 2, 6}. The largest possible rectangle possible is 12 (see the below figure, the max area rectangle is highlighted in red)

算法思想:

For every bar ‘x’, we calculate the area with ‘x’ as the smallest bar in the rectangle. If we calculate such area for every bar ‘x’ and find the maximum of all areas, our task is done. How to calculate area with ‘x’ as smallest bar? We need to know index of the first smaller (smaller than ‘x’) bar on left of ‘x’ and index of first smaller bar on right of ‘x’. Let us call these indexes as ‘left index’ and ‘right index’ respectively.
We traverse all bars from left to right, maintain a stack of bars. Every bar is pushed to stack once. A bar is popped from stack when a bar of smaller height is seen. When a bar is popped, we calculate the area with the popped bar as smallest bar. How do we get left and right indexes of the popped bar – the current index tells us the ‘right index’ and index of previous item in stack is the ‘left index’.

算法流程:

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.

代码java实现

public class Solution {
    	public int largestRectangleArea(int[] height) {
		int maxarea = 0;
		Stack<Integer> sta = new Stack<>();
		int top ;
		int top_area;
		int i = 0;
		while(i<height.length){
			if(sta.isEmpty() || height[sta.peek()]<=height[i] ){
				sta.push(i++);
			}else{
				top = sta.pop();
				top_area = height[top] * (sta.isEmpty()? i:i-sta.peek()-1);
				if(top_area>maxarea){
					maxarea = top_area;
				}
			}
		}
		while(!sta.isEmpty()){
			top = sta.pop();
			top_area = height[top] * (sta.isEmpty()? i:i-sta.peek()-1);
			if(top_area>maxarea){
				maxarea = top_area;
			}
		}
		return maxarea;
	}
}


时间复杂度:因为每个元素只push pop一次,时间复杂度O(n).

### 矩形函数的原型定义 矩形函数(Rectangular Function)在数学和信号处理中通常用于表示一个有限范围内的单位脉冲信号。其数学定义为: $$ \text{rect}(t) = \begin{cases} 1 & \text{if } |t| < \frac{1}{2} \\ 0 & \text{otherwise} \end{cases} $$ 在离散信号处理中,矩形函数通常被称为矩形窗(Rectangular Window),其形式为: $$ w(n) = 1, \quad 0 \leq n \leq N-1 $$ 其中 $ N $ 是信号的长度,$ n $ 是离散时间索引。这种窗函数在时域上对信号没有加权作用,即所有样本点的权重均为1 [^4]。 ### 矩形函数的编程实现 在编程中,矩形函数可以通过多种方式实现。以下是一个使用Python生成矩形函数信号的示例代码: ```python import numpy as np import matplotlib.pyplot as plt # 定义矩形函数 def rectangular_function(t): return np.where(np.abs(t) < 0.5, 1, 0) # 生成时间轴 t = np.linspace(-1, 1, 1000) # 计算矩形函数值 y = rectangular_function(t) # 绘制图形 plt.plot(t, y) plt.title('Rectangular Function') plt.xlabel('Time') plt.ylabel('Amplitude') plt.grid(True) plt.show() ``` 该代码首先定义了一个矩形函数 `rectangular_function`,然后生成了一个时间轴 `t`,并计算了该时间轴上每个点的矩形函数值。最后,使用 `matplotlib` 绘制了矩形函数的图形 [^4]。 ### 矩形函数在信号处理中的应用 在数字信号处理中,矩形函数常用于信号截断或作为窗函数。例如,在进行快速傅里叶变换(FFT)时,信号会被截断以适应有限的输入长度,而矩形窗则用于表示这种截断操作。尽管矩形窗实现简单,但其频域响应具有较宽的主瓣和较高的旁瓣,可能导致频谱泄漏问题 [^4]。 ### 相关问题
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