# 42. Trapping Rain Water

Reference: LeetCode

Difficulty: Hard

My Post: [Java] Detailed Explanations & Illustrations (divide-and-conquer, DP, two pointers)

## Problem

Given

`n`

non-negative integers representing an elevation map where the width of each bar is`1`

, compute how much water it is able to trap after raining.

**Example:**

1 | Input: [0,1,0,2,1,0,1,3,2,1,2,1] |

## Analysis

**Wrong Idea:** One pointer to detect increase or decrease. My mind was influenced by the solution to the longest mountain in an array.

**A tricky test case:**

The correct answer is `w1 + w2`

. To compute the amount, we can’t just go by the bars `b`

and `c`

; instead, we must consider `a`

and `d`

with a broader perspective.

Come up with the brute-force first!

### Brute-Force

For each position `i`

in `height[0...n-1]`

, calculate how much water it contains. The amount of water can be computed by `min(leftMax, rightMax) - height[i]`

. where `leftMax`

is the highest left bar in `height[0...i-1]`

and `rightMax`

is the highest right bar in `height[i+1...n-1]`

. What does it mean? No Picture You Say J8~

**Note:**

- When
`i`

equals`0`

and`n - 1`

, the amount must be 0 since`min(leftMax, rightMax)`

is 0. - Notice that if the amount of water is negative, we just skip it. It happens when
`height[i]`

is greater than`min(leftMax, rightMax)`

, which means no water is stored on it.

1 | public int trap(int[] height) { |

**Time:** $O(N^2)$ since computing `leftMax`

and `rightMax`

in each round takes $O(N)$.**Space:** $O(1)$

### DP (pre-compute)

Based on the brute-force approach, we can pre-compute all `leftMax`

and `rightMax`

in advance, which reduces time complexity to $O(N)$.

We denote `leftMax[i]`

as the highest bar in `height[0...i]`

and `rightMax[i]`

as the highest bar in `height[i...n-1]`

.

**Note:** Before pre-computation, `leftMax[0]`

and `rightMax[n - 1]`

are each initialized by `height[0]`

and `height[n - 1]`

. By doing this, the code in the loop is cleaner.

1 | public int trap(int[] height) { |

**Time:** $O(N)$**Space:** $O(N)$

### Divide and Conquer

We can also solve this problem by divide-and-conquer approach.

Initially, `leftMax`

is `height[0]`

and `rightMax`

is `height[n - 1]`

. We recursively compute the amount of trapping water in the problem `subTrap[1, n - 2]`

with `leftMax`

and `rightMax`

.

**Divide:** We use `findMax`

to find the highest bar `i`

in `height[lo, hi]`

, then divide into three subproblems `p1`

, `p2`

, and `p3`

.**Conquer:** We compute `p1`

and `p2`

recursively. `p3`

can be computed by `height[i] - min(leftMax, rightMax)`

(remember to skip negative results).**Combine:** Returns `p1 + p2 + p3`

.

In each step, we update `rightMax`

for the left subproblem `p1`

and update `leftMax`

for the right subproblem `p2`

if they are greater than `height[findMax[i]]`

.

**Base Case:** When there is one position (`lo == hi`

), just solve it as we did for `p3`

. If there is no place (`lo > hi`

), returns `0`

.

1 | public int trap(int[] height) { |

**Time:**

- Best case: $O(N\log{N})$ where $T(N) = 2T(N/2) + O(N)$
- Worst case: $O(N^2)$ where $T(N) = T(N-1) + O(N)$

**Space:**

- Best case: $O(\log{N})$
- Worst case: $O(N)$

It depends on how the problem is divided into two subproblems by the highest bar.

**Note:** Time complexity can be reduced to $O(N)$ if the divide step `findMax()`

takes $O(1)$ (pre-compute), no matter how the problem is divided.

### Two Pointers (no extra space, clever)

**Note:** We have `leftMax`

and `rightMax`

that record the largest heights `lo`

and `hi`

have seen so far.

As in DP approach, instead of computing each `leftMax`

and `rightMax`

separately, we can actually consider one bar at each time as our `min(leftMax, rightMax)`

since we only care about the bar with a smaller height.

We denote two pointers `lo`

and `hi`

starting from two ends of the array. In the loop, we update `leftMax`

and `rightMax`

first.

If the current `leftMax`

is less than `rightMax`

, we can correctly compute the water at `lo`

, but not the water at `hi`

.

Let’s examine the case `leftMax < rightMax`

. We assume a `leftMax`

in `height[0, lo]`

. Since we do update first, `height[lo]`

should be **less than or equal to** `leftMax`

(it means that `leftMax`

could have been just updated by `height[lo]`

).

Since `leftMax < rightMax`

, the amount of water at `lo`

can be determined at this time, no matter what the heights between `[lo + 1, hi - 1]`

are. For example, if there is an `e`

higher than `b`

but lower than `leftMax`

, it still works because there is `rightMax`

on the right that can eliminate the effect of `e`

. In terms of `f`

, it is more obvious that it is right. (it is hard to explain well T_T)

**However**, it does not always hold true if we compute the amount of water at `hi`

in this case (`leftMax < rightMax`

). Consider `d`

that is less than `rightMax`

:

If we use

`leftMax`

to compute the amount (`leftMax - d`

), there could be an`f`

that gives a larger result (`min(f, rightMax) - d`

).If we use

`rightMax`

to compute the amount (`rightMax - d`

), there could be an`e`

that gives a smaller result (`min(e, rightMax) - d`

if`e > leftMax`

or`min(leftMax, rightMax) - d`

if`e < leftMax`

).

Here is the code:

1 | public int trap(int[] height) { |

**Time:** $O(N)$**Space:** $O(1)$

### Two Pointers (my first attempt)

Here is the solution I first came up with… It just uses `two pointers`

to keep track of two bars and compute the water by levels constrained by the minimum height of two bars. (a bit similar to Skyline problem…)

Left/right pointers are both aiming for higher bars when they move on to the center. It is not intuitive because each time we should not add computed amount of water (minus `wateredHt`

in the code).

How could I write such a solution instead of brute-force at first?

**See a better approach below.**

1 | public int trap(int[] height) { |

**Time:** $O(N^2)$**Space:** $O(1)$