There are n buildings in a line. You are given an integer array heights of size n that represents the heights of the buildings in the line.

The ocean is to the right of the buildings. A building has an ocean view if the building can see the ocean without obstructions. Formally, a building has an ocean view if all the buildings to its right have a smaller height.

Return a list of indices (0-indexed) of buildings that have an ocean view, sorted in increasing order.

Thinking Process

  1. Right-to-left iteration: Essential for efficiently checking if buildings to the right are shorter
  • Stack matches nested or LIFO structure (parentheses, monotonic scans).
  • Push on open / larger; pop when the current element resolves pending work.
  • Monotonic stack finds next greater/smaller in O(n).
Two pointers 1 3 5 7 9 L R move L/R based on comparison

Common Approaches

Typical techniques for this pattern:

Approach Time Space Notes
Monotonic stack (this problem) O(n) O(n) Next greater/smaller element
Parentheses matching O(n) O(n) Push open, pop on close
Expression evaluation O(n) O(n) Operand + operator stacks
Stack simulation O(n) O(n) Process in LIFO order

Examples

Example 1:

Input: heights = [4,2,3,1]
Output: [0,2,3]
Explanation: Building at index 1 (height 2) does not have an ocean view because building at index 2 (height 3) is taller.

Example 2:

Input: heights = [4,3,2,1]
Output: [0,1,2,3]
Explanation: All the buildings have an ocean view.

Example 3:

Input: heights = [1,3,2,4]
Output: [3]
Explanation: Only building at index 3 (height 4) has an ocean view.

Example 4:

Input: heights = [2,2,2,2]
Output: [3]
Explanation: Only the rightmost building has an ocean view (buildings of equal height block the view).

Constraints

  • 1 <= heights.length <= 10^5
  • 1 <= heights[i] <= 10^9

Solution Approaches

The key insight is that a building has an ocean view if it’s taller than all buildings to its right. We can solve this by:

  1. Iterating from right to left: Start from the rightmost building and track the maximum height seen so far
  2. Track maximum height: If current building is taller than or equal to the maximum height seen, it has an ocean view
  3. Reverse result: Since we iterate right-to-left, reverse the result to get indices in increasing order

Time Complexity: O(n)
Space Complexity: O(1) excluding output array

The simplest and most efficient approach: iterate from right to left, tracking the maximum height seen so far.

class Solution:
    def findBuildings(self, heights):
        n = len(heights)
        rtn = []
        maxHeight = -1

        for curr in range(n - 1, -1, -1):
            if maxHeight < heights[curr]:
                rtn.append(curr)
                maxHeight = heights[curr]

        rtn.reverse()
        return rtn

Solution Explanation

Approach: Monotonic stack (this problem)

Key idea: 1. Right-to-left iteration: Essential for efficiently checking if buildings to the right are shorter

How the code works:

  1. Right-to-left iteration: Essential for efficiently checking if buildings to the right are shorter
    • Stack matches nested or LIFO structure (parentheses, monotonic scans).
    • Push on open / larger; pop when the current element resolves pending work.
    • Monotonic stack finds next greater/smaller in O(n).

Walkthrough — input heights = [4,2,3,1], expected output [0,2,3]:

Building at index 1 (height 2) does not have an ocean view because building at index 2 (height 3) is taller.

Approach Time Space Pros Cons
Simple Greedy O(n) O(1) Simple, efficient, no extra space -
Monotonic Stack O(n) O(n) More general pattern Extra space, more complex

How it works:

  1. Start from the rightmost building (index n-1)
  2. Track maxHeight - the maximum height seen so far
  3. If current building is taller than maxHeight, it has an ocean view
  4. Update maxHeight to the current building’s height
  5. Reverse the result to get indices in increasing order

Why this works:

  • A building has an ocean view if it’s taller than all buildings to its right
  • By iterating right-to-left, we can check this condition efficiently
  • We only need to track the maximum height, not all buildings

Solution 2: Monotonic Stack

Time Complexity: O(n)
Space Complexity: O(n) for the stack

Uses a monotonic stack to maintain buildings in decreasing order of height.

class Solution:
    def findBuildings(self, heights):
        n = len(heights)
        rtn = []
        stk = []

        for curr in range(n - 1, -1, -1):
            # Remove buildings that are shorter than current
            while stk and heights[stk[-1]] < heights[curr]:
                stk.pop()

            # If stack is empty, current building has ocean view
            if not stk:
                rtn.append(curr)

            stk.append(curr)

        rtn.reverse()
        return rtn

How it works:

  1. Iterate from right to left
  2. Use a stack to maintain indices of buildings that could block the view
  3. Pop buildings from stack that are shorter than current building
  4. If stack is empty after popping, current building has ocean view
  5. Push current building index to stack

Why this works:

  • Stack maintains buildings in decreasing order of height
  • If a building is shorter than current, it can’t block current’s view
  • If stack is empty, no building to the right can block current’s view

Key Insight: Right-to-Left Iteration

Problem: For each building, check if all buildings to its right are shorter.

Solution: Iterate from right to left, tracking the maximum height seen so far.

Why right-to-left:

  • We need to know the maximum height of all buildings to the right
  • By iterating right-to-left, we can maintain this information efficiently
  • Each building only needs to compare with the maximum height seen so far

Step-by-Step Example: heights = [4,2,3,1]

Initial: maxHeight = -1, rtn = []

i = 3: heights[3] = 1
  maxHeight = -1 < 1 → has ocean view
  rtn = [3], maxHeight = 1

i = 2: heights[2] = 3
  maxHeight = 1 < 3 → has ocean view
  rtn = [3, 2], maxHeight = 3

i = 1: heights[1] = 2
  maxHeight = 3 >= 2 → no ocean view
  rtn = [3, 2], maxHeight = 3

i = 0: heights[0] = 4
  maxHeight = 3 < 4 → has ocean view
  rtn = [3, 2, 0], maxHeight = 4

After reverse: rtn = [0, 2, 3]

Visual Representation:

Buildings: [4, 2, 3, 1]
Indices:    0  1  2  3
            │  │  │  │
            │  │  └──┴──> Ocean
            │  │     │
            │  └─────┼──> Blocked by building 2
            └────────┼──> Ocean view (tallest)
                     │
            Ocean view: [0, 2, 3]

Step-by-Step Example: heights = [1,3,2,4]

Initial: maxHeight = -1, rtn = []

i = 3: heights[3] = 4
  maxHeight = -1 < 4 → has ocean view
  rtn = [3], maxHeight = 4

i = 2: heights[2] = 2
  maxHeight = 4 >= 2 → no ocean view
  rtn = [3], maxHeight = 4

i = 1: heights[1] = 3
  maxHeight = 4 >= 3 → no ocean view
  rtn = [3], maxHeight = 4

i = 0: heights[0] = 1
  maxHeight = 4 >= 1 → no ocean view
  rtn = [3], maxHeight = 4

After reverse: rtn = [3]

Algorithm Breakdown

Solution 1: Simple Greedy

def find_buildings(heights: list[int]) -> list[int]:
    n = len(heights)
    rtn: list[int] = []
    max_height = -1
    for curr in range(n - 1, -1, -1):
        if max_height < heights[curr]:
            rtn.append(curr)
            max_height = heights[curr]
    rtn.reverse()
    return rtn

Key Points:

  • Right-to-left iteration: Start from the rightmost building
  • Track maximum: Maintain maximum height seen so far
  • Compare and update: If current is taller, add to result and update max
  • Reverse result: Get indices in increasing order

Solution 2: Monotonic Stack

def find_buildings_stack(heights: list[int]) -> list[int]:
    n = len(heights)
    rtn: list[int] = []
    stk: list[int] = []
    for curr in range(n - 1, -1, -1):
        while stk and heights[stk[-1]] < heights[curr]:
            stk.pop()
        if not stk:
            rtn.append(curr)
        stk.append(curr)
    rtn.reverse()
    return rtn

Key Points:

  • Monotonic stack: Maintains buildings in decreasing order
  • Pop shorter buildings: Remove buildings that can’t block current
  • Check if empty: Empty stack means no blocking buildings
  • Push current: Add current building to stack

Complexity

| Approach | Time | Space | Pros | Cons | |———-|——|——-|——|——| | Simple Greedy | O(n) | O(1) | Simple, efficient, no extra space | - | | Monotonic Stack | O(n) | O(n) | More general pattern | Extra space, more complex |

Common Mistakes

  1. All buildings same height: Only rightmost building has ocean view
    • heights = [2,2,2,2] → [3]
  2. Increasing heights: All buildings have ocean view
    • heights = [1,2,3,4] → [0,1,2,3]
  3. Decreasing heights: Only leftmost building has ocean view
    • heights = [4,3,2,1] → [0,1,2,3] (all have view since all to right are shorter)
  4. Single building: Always has ocean view
    • heights = [5] → [0]
  5. Left-to-right iteration: Can’t efficiently check if buildings to right are shorter
  6. Wrong comparison: Using <= instead of < for equal heights
  7. Forgetting to reverse: Result indices should be in increasing order
  8. Not handling edge cases: Single building, all same height, etc.

Optimization Tips

  1. Use Solution 1: Simpler and more space-efficient
  2. Reserve space: Can reserve space for result vector if needed
  3. Early termination: Not applicable here (need to check all buildings)

Pattern Recognition

This problem demonstrates the “Monotonic Stack” and “Greedy” patterns:

1. Iterate from right to left
2. Track maximum (or use stack) to maintain order
3. Compare current with tracked value
4. Update result based on comparison

Similar problems:

  • Next greater/smaller element
  • Trapping rain water
  • Largest rectangle in histogram
  • Problems requiring “next” or “previous” element information

Real-World Applications

  1. City Planning: Determining which buildings have unobstructed views
  2. Real Estate: Identifying properties with ocean/mountain views
  3. Signal Processing: Finding peaks in signal data
  4. Algorithm Design: Understanding monotonic stack patterns

Why Solution 1 is Better

Advantages of Simple Greedy:

  • O(1) extra space: Only uses a single variable
  • Simpler code: Easier to understand and maintain
  • Same time complexity: O(n) in both cases
  • More efficient: No stack operations overhead

When to use Monotonic Stack:

  • Need to track multiple values, not just maximum
  • Need to answer queries about “next greater/smaller”
  • Problem requires maintaining order of elements

For this problem, Solution 1 (Simple Greedy) is the optimal choice.

Key Takeaways

  1. Right-to-left iteration: Essential for efficiently checking if buildings to the right are shorter

  2. Track maximum only: We only need the maximum height, not all heights

  3. Greedy approach: Local optimal (taller than max) leads to global optimal

  4. Equal heights: Buildings of equal height block each other (use < not <=)

References

Template Reference