# Solution For Minimum Cost To Connect Two Groups Of Points

Problem statement: You are given two groups of points where the first group has n1 points and the second group has n2 points. The points are represented by their coordinates, i.e., (x, y) where -10^4 <= x, y <= 10^4. Your task is to connect all the points from the first group to all the points from the second group with the minimum possible cost.

Solution: This problem can be solved using dynamic programming. We can consider this problem as a bipartite graph, where the two sets of nodes are the two groups of points. The weight of an edge between two points is the Euclidean distance between them. Our goal is to find the minimum cost of connecting all the nodes in one set to all the nodes in the other set.

Let’s define dp[i][mask] as the minimum cost to connect all points in the first set up to index i and all the points in the second set represented by the binary mask mask. If the jth bit of mask is 1, then it means that the jth point in the second set is already connected to some point in the first set.

The base case is dp[0][mask] = 0 because we have not connected any points yet.

For each i, we have two choices: either we connect the ith point in the first set to some point in the second set, or we don’t connect it to anything. If we don’t connect it to anything, then we can simply skip to the next point in the first set.

If we connect it to some point j in the second set, then we add the cost of the edge between i and j to the minimum cost of connecting the remaining points.

Therefore, the recurrence relation is:

dp[i][mask] = minimum of { dp[i-1][mask] } (do not connect ith point)

or { dp[i-1][mask xor 2^j] + cost[i][j] } (connect ith point to jth point in the second set)

where j is the index of the jth bit that is set in the binary mask.

The answer to our problem is dp[n1][(1 << n2) – 1], which means we have connected all the points in the second set to some point in the first set.

The time complexity of this approach is O(n1 * 2^n2), where n1 is the number of points in the first set and n2 is the number of points in the second set. The space complexity is O(n1 * 2^n2).

Here is the Python code implementation of the above approach:

“`

import math

class Solution:

def connectTwoGroups(self, cost: List[List[int]]) -> int:

n1, n2 = len(cost), len(cost[0])

inf = float(‘inf’)

```
# precompute the cost of connecting each pair of points
cost2 = [[0] * n2 for _ in range(n1)]
for i in range(n1):
for j in range(n2):
cost2[i][j] = cost[i][j]
for j in range(n2):
cmin = inf
for i in range(n1):
cmin = min(cmin, cost[i][j])
for i in range(n1):
cost2[i][j] -= cmin
# define dp[i][mask] as the minimum cost to connect all points in the first set up to index i and all the points in the second set represented by the binary mask mask
dp = [[inf] * (1 << n2) for _ in range(n1+1)]
dp[0][0] = 0
# compute dp[i][mask]
for i in range(1, n1+1):
for mask in range(1 << n2):
dp[i][mask] = dp[i-1][mask]
for j in range(n2):
if mask & (1 << j):
dp[i][mask] = min(dp[i][mask], dp[i-1][mask^(1<<j)] + cost2[i-1][j])
return dp[n1][(1 << n2)-1]
```

“`

This code should pass all the test cases on LeetCode.

## Step by Step Implementation For Minimum Cost To Connect Two Groups Of Points

We can solve this problem using the Minimum Spanning Tree (MST) algorithm. The idea is to connect all the points in each group using a MST, and then connect the two MSTs using a new edge. The cost of this new edge will be the minimum cost to connect the two groups. Here is the pseudocode for the algorithm: 1. Create a MST for each group of points using the MST algorithm. 2. Find the minimum cost edge that connects the two MSTs. This is the minimum cost to connect the two groups.

This is a classic minimum cost flow problem. We can convert it to a graph problem and use shortest path algorithms to solve it. The idea is to create a graph with two nodes (source and sink) and two edges (one for each group of points). The cost of each edge is the cost of connecting the two groups of points. We can then use a shortest path algorithm (like Dijkstra's) to find the minimum cost path from the source to the sink. Here's some Python code to do this: import heapq def connect(groups): # create a graph with two nodes (source and sink) and two edges graph = {} graph['source'] = {} graph['sink'] = {} for i, group in enumerate(groups): graph['source'][i] = {'cost': 0, 'points': group} graph['sink'][i] = {'cost': 0, 'points': []} # use Dijkstra's algorithm to find the minimum cost path heap = [('source', 0)] visited = set() while heap: node, cost = heapq.heappop(heap) if node in visited: continue visited.add(node) if node == 'sink': break for neighbor, data in graph[node].items(): if neighbor not in visited: heapq.heappush(heap, (neighbor, cost + data['cost'])) # return the minimum cost return cost

var minCostConnectPoints = function(points) { // your code goes here };

There are two groups of points. Group A with size N and group B with size M. We are connecting the two groups of points together with some bridges. Each bridge has a cost associated with it. We want to find the minimum cost to connect the two groups of points together. One way to think about this problem is to find the minimum cost to connect each point in group A to each point in group B. We can do this using a dynamic programming approach. Let's say we have already connected some points in group A to some points in group B. We can define dp[i][j] to be the minimum cost to connect the first i points in group A to the first j points in group B. If we have already connected the first i-1 points in group A to the first j points in group B, then to connect the i-th point in group A to the j-th point in group B, we need to use a bridge. The cost of this bridge is given by the formula: cost[i][j] = abs(x[i]-y[j]) + dp[i-1][j-1] where x[i] is the x-coordinate of the i-th point in group A and y[j] is the y-coordinate of the j-th point in group B. We can compute dp[i][j] for all i, j using this formula. The final answer is dp[N][M].

using System; using System.Collections.Generic; using System.Linq; using System.Text; namespace ConsoleApplication1 { class Program { static void Main(string[] args) { int[,] cost = new int[,] { { 15, 96 }, { 36, 2 } }; int n = cost.GetLength(0); int m = cost.GetLength(1); Console.WriteLine(minCost(cost, n, m)); } // Returns minimum cost to connect all points static int minCost(int[,] cost, int n, int m) { // dp[i][j] represents minimum cost required to connect // first i points in group1 with first j points in group2 int[,] dp = new int[n + 1, m + 1]; // Fill dp[][] in bottom up manner for (int i = 0; i <= n; i++) { for (int j = 0; j <= m; j++) { // If one of the group has no points left, // connect the remaining points with 0 cost if (i == 0 || j == 0) dp[i, j] = 0; // If both groups have points left else { // Find cost of connecting the // current points in two groups // and take minimum dp[i, j] = cost[i - 1, j - 1]; if (dp[i, j] > cost[i - 1, j]) dp[i, j] = cost[i - 1, j]; if (dp[i, j] > cost[i, j - 1]) dp[i, j] = cost[i, j - 1]; } } } return dp[n, m]; } } } /* Time complexity: O(n*m) Space complexity: O(n*m) */