# Posts tagged as “grid”

In a gold mine grid of size m * n, each cell in this mine has an integer representing the amount of gold in that cell, 0 if it is empty.

Return the maximum amount of gold you can collect under the conditions:

• Every time you are located in a cell you will collect all the gold in that cell.
• From your position you can walk one step to the left, right, up or down.
• You can’t visit the same cell more than once.
• Never visit a cell with 0 gold.
• You can start and stop collecting gold from any position in the grid that has some gold.

Example 1:

Input: grid = [[0,6,0],[5,8,7],[0,9,0]]
Output: 24
Explanation:
[[0,6,0],
[5,8,7],
[0,9,0]]
Path to get the maximum gold, 9 -> 8 -> 7.


Example 2:

Input: grid = [[1,0,7],[2,0,6],[3,4,5],[0,3,0],[9,0,20]]
Output: 28
Explanation:
[[1,0,7],
[2,0,6],
[3,4,5],
[0,3,0],
[9,0,20]]
Path to get the maximum gold, 1 -> 2 -> 3 -> 4 -> 5 -> 6 -> 7.


Constraints:

• 1 <= grid.length, grid[i].length <= 15
• 0 <= grid[i][j] <= 100
• There are at most 25 cells containing gold.

## Solution: DFS

Time compleixty: O(4^25) ???
Space complexity: O(25)

## C++

Given a 2D grid of 0s and 1s, return the number of elements in the largest square subgrid that has all 1s on its border, or 0 if such a subgrid doesn’t exist in the grid.

Example 1:

Input: grid = [[1,1,1],[1,0,1],[1,1,1]]
Output: 9


Example 2:

Input: grid = [[1,1,0,0]]
Output: 1


Constraints:

• 1 <= grid.length <= 100
• 1 <= grid[0].length <= 100
• grid[i][j] is 0 or 1

## Solution: DP

Compute the sums of all rectangles that has left-top corner at (0, 0) in O(m*n) time.
For each square and check whether its borders are all ones in O(1) time.

Time complexity: O(m*n*min(m,n))
Space complexity: O(m*n)

## C++

In a given grid, each cell can have one of three values:

• the value 0 representing an empty cell;
• the value 1 representing a fresh orange;
• the value 2 representing a rotten orange.

Every minute, any fresh orange that is adjacent (4-directionally) to a rotten orange becomes rotten.

Return the minimum number of minutes that must elapse until no cell has a fresh orange.  If this is impossible, return -1instead.

Example 1:

Input: [[2,1,1],[1,1,0],[0,1,1]]
Output: 4


Example 2:

Input: [[2,1,1],[0,1,1],[1,0,1]]
Output: -1
Explanation:  The orange in the bottom left corner (row 2, column 0) is never rotten, because rotting only happens 4-directionally.


Example 3:

Input: [[0,2]]
Output: 0
Explanation:  Since there are already no fresh oranges at minute 0, the answer is just 0.


Note:

1. 1 <= grid.length <= 10
2. 1 <= grid[0].length <= 10
3. grid[i][j] is only 01, or 2.

## Solution: BFS

Time complexity: O(mn)
Space complexity: O(mn)

## C++

Given a 2D binary matrix filled with 0’s and 1’s, find the largest rectangle containing only 1’s and return its area.

Example:

Input:
[
["1","0","1","0","0"],
["1","0","1","1","1"],
["1","1","1","1","1"],
["1","0","0","1","0"]
]
Output: 6

## Solution: DP

Time complexity: O(m^2*n)
Space complexity: O(mn)

dp[i][j] := max length of all 1 sequence ends with col j, at the i-th row.
transition:
dp[i][j] = 0 if matrix[i][j] == ‘0’
= dp[i][j-1] + 1 if matrix[i][j] == ‘1’

# Problem

You are given a map in form of a two-dimensional integer grid where 1 represents land and 0 represents water. Grid cells are connected horizontally/vertically (not diagonally). The grid is completely surrounded by water, and there is exactly one island (i.e., one or more connected land cells). The island doesn’t have “lakes” (water inside that isn’t connected to the water around the island). One cell is a square with side length 1. The grid is rectangular, width and height don’t exceed 100. Determine the perimeter of the island.

Example:

[[0,1,0,0],
[1,1,1,0],
[0,1,0,0],
[1,1,0,0]]

Explanation: The perimeter is the 16 yellow stripes in the image below:



# Solution: Counting

If a land has 0 neighbour, it contributes 4 to the perimeter

If a land has 1 neighbour, it contributes 3 to the perimeter

If a land has 2 neighbours, it contributes 2 to the perimeter

If a land has 3 neighbours, it contributes 1 to the perimeter

If a land has 4 neighbours, it contributes 0 to the perimeter

perimeter = area * 4 – total_neighbours

Time complexity: O(mn)

Space complexity: O(1)

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