What if you could instantly find the cheapest way through a maze of streets without guessing?
Why Minimum Path Sum in Grid in DSA C++?
Imagine you are walking through a city grid from the top-left corner to the bottom-right corner. Each block has a cost to cross, like traffic or tolls. You want to find the cheapest path, but you try to check every possible route by hand.
Checking every path manually is slow and confusing because the number of routes grows very fast. You might miss cheaper paths or waste time repeating the same calculations over and over.
The Minimum Path Sum algorithm uses a smart way to remember the cheapest cost to reach each block. It builds the solution step-by-step, so you don't repeat work and quickly find the cheapest path.
int minPathSum(vector<vector<int>>& grid) {
// Try all paths manually (impractical)
// No code here because it's too complex
}int minPathSum(vector<vector<int>>& grid) {
int rows = grid.size();
int cols = grid[0].size();
vector<vector<int>> cost(rows, vector<int>(cols));
cost[0][0] = grid[0][0];
for (int i = 1; i < rows; i++)
cost[i][0] = cost[i-1][0] + grid[i][0];
for (int j = 1; j < cols; j++)
cost[0][j] = cost[0][j-1] + grid[0][j];
for (int i = 1; i < rows; i++) {
for (int j = 1; j < cols; j++) {
cost[i][j] = std::min(cost[i-1][j], cost[i][j-1]) + grid[i][j];
}
}
return cost[rows-1][cols-1];
}This lets you quickly find the cheapest path through any grid, saving time and avoiding mistakes.
Delivery drivers use this to find routes that minimize fuel costs and time when navigating city streets.
Manual path checking is slow and error-prone.
Dynamic programming remembers costs to avoid repeated work.
Minimum Path Sum finds the cheapest route efficiently.
Practice
Solution
Step 1: Understand the problem goal
The problem asks for the minimum sum of values collected when moving from the top-left corner to the bottom-right corner of a grid, moving only right or down.Step 2: Clarify movement constraints
Only moves to the right or down are allowed, so the path is restricted and the sum depends on chosen cells.Final Answer:
The smallest sum of numbers along a path from top-left to bottom-right, moving only right or down. -> Option DQuick Check:
Minimum path sum = smallest sum path [OK]
- Thinking diagonal moves are allowed
- Looking for maximum sum instead of minimum
- Counting number of paths instead of sum
grid with 3 rows and 4 columns filled with zeros?Solution
Step 1: Understand 2D vector initialization syntax
To create a 2D vector with 3 rows and 4 columns filled with zeros, we use vector of vectors: outer vector size 3, inner vector size 4, each element 0.Step 2: Check each option
std::vector grid(3, std::vector<int>(4, 0)); correctly uses std::vector grid(3, std::vector<int>(4, 0)); which means 3 rows, each row is a vector of 4 zeros.Final Answer:
std::vector grid(3, std::vector<int>(4, 0)); -> Option AQuick Check:
Correct 2D vector init = std::vector grid(3, std::vector<int>(4, 0)); [OK]
- Using wrong vector type for 2D
- Swapping rows and columns sizes
- Trying to pass three arguments to vector constructor
std::vector grid = {{1, 3}, {1, 5}};
int m = grid.size();
int n = grid[0].size();
for (int i = 1; i < m; i++) grid[i][0] += grid[i-1][0];
for (int j = 1; j < n; j++) grid[0][j] += grid[0][j-1];
for (int i = 1; i < m; i++) {
for (int j = 1; j < n; j++) {
grid[i][j] += std::min(grid[i-1][j], grid[i][j-1]);
}
}
std::cout << grid[m-1][n-1] << std::endl;Solution
Step 1: Calculate first column cumulative sums
grid[1][0] = grid[1][0] + grid[0][0] = 1 + 1 = 2Step 2: Calculate first row cumulative sums
grid[0][1] = grid[0][1] + grid[0][0] = 3 + 1 = 4Step 3: Calculate inner cell minimum path sums
grid[1][1] = grid[1][1] + min(grid[0][1], grid[1][0]) = 5 + min(4, 2) = 5 + 2 = 7Final Answer:
7 -> Option AQuick Check:
Minimum path sum = 7 [OK]
- Not updating first row and column before inner cells
- Adding max instead of min
- Mixing indices i and j
std::vector grid = {{1, 2, 3}, {4, 5, 6}};
int m = grid.size();
int n = grid[0].size();
for (int i = 1; i < m; i++) grid[i][0] += grid[i-1][0];
for (int j = 1; j < n; j++) grid[0][j] += grid[0][j-1];
for (int i = 1; i < m; i++) {
for (int j = 1; j < n; j++) {
grid[i][j] += std::max(grid[i-1][j], grid[i][j-1]);
}
}
std::cout << grid[m-1][n-1] << std::endl;Solution
Step 1: Check the inner loop operation
The code uses std::max to update grid[i][j], but the problem requires minimum path sum, so std::min should be used.Step 2: Verify other parts
First row and column are correctly initialized, loops have correct ranges, and grid initialization is valid.Final Answer:
Using std::max instead of std::min in inner loop -> Option CQuick Check:
Use min for minimum path sum [OK]
- Confusing max and min functions
- Skipping first row/column initialization
- Off-by-one errors in loops
grid = {
{1, 3, 1, 2},
{1, 5, 1, 1},
{4, 2, 1, 3}
};
You can only move right or down. Calculate the minimum sum.Solution
Step 1: Compute first row and first column cumulative sums
First row: [1, 4, 5, 7] (1, 1+3=4, 4+1=5, 5+2=7) First column: [1, 2, 6] (1, 1+1=2, 2+4=6)Step 2: Calculate inner cells minimum path sums
Row 1, col 1: 5 + min(4,2) = 5 + 2 = 7 Row 1, col 2: 1 + min(5,7) = 1 + 5 = 6 Row 1, col 3: 1 + min(7,6) = 1 + 6 = 7 Row 2, col 1: 2 + min(7,6) = 2 + 6 = 8 Row 2, col 2: 1 + min(6,8) = 1 + 6 = 7 Row 2, col 3: 3 + min(7,7) = 3 + 7 = 10Final Answer:
10 -> Option BQuick Check:
Minimum path sum = 10 [OK]
- Adding max instead of min
- Incorrect cumulative sums for first row/column
- Misindexing grid cells
Ready to prove it?
Pass the test and this pattern moves unknown
