Optimize Water Distribution in a Village

Hard

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GraphsGreedy Algorithms

You are tasked with optimizing the water distribution system in a village. The village consists of n houses, and your goal is to supply water to every house at the minimum possible cost. There are two ways to supply water:

Build a well directly at a house. The cost of building a well at house i is given by wells[i-1]. The wells array is 0-indexed.
Connect houses with pipes. The cost of connecting houses i and j with a pipe is given by pipes[k][2], where pipes[k] = [house1, house2, cost]. pipes is a list of connections between houses.

You need to find the minimum total cost to supply water to all the houses. You can either build a well directly at a house or connect it to another house that already has a water supply (either through a well or another connected house).

For example:

n = 3
wells = [1, 2, 2]
pipes = [[1, 2, 1], [2, 3, 1]]

In this case, the optimal solution is to build a well at house 1 (cost 1), connect house 1 to house 2 (cost 1), and connect house 2 to house 3 (cost 1). The total cost is 1 + 1 + 1 = 3.

Another example:

n = 2
wells = [1, 10]
pipes = [[1, 2, 5]]

Here, it's cheaper to build a well at house 1 (cost 1) and connect house 1 to house 2 (cost 5). Total cost is 6. Alternatively, you could build a well at both houses for a cost of 11. The optimal answer is 6.

Write a function that takes the number of houses n, the wells array, and the pipes list as input, and returns the minimum cost to supply water to all the houses. What is the most efficient way to solve this problem, and what are the time and space complexities?

Optimize Water Distribution in a Village

Hard

Google

3 views

Topics:

GraphsGreedy Algorithms

You are tasked with optimizing the water distribution system in a village. The village consists of n houses, and your goal is to supply water to every house at the minimum possible cost. There are two ways to supply water:

Build a well directly at a house. The cost of building a well at house i is given by wells[i-1]. The wells array is 0-indexed.
Connect houses with pipes. The cost of connecting houses i and j with a pipe is given by pipes[k][2], where pipes[k] = [house1, house2, cost]. pipes is a list of connections between houses.

You need to find the minimum total cost to supply water to all the houses. You can either build a well directly at a house or connect it to another house that already has a water supply (either through a well or another connected house).

For example:

n = 3
wells = [1, 2, 2]
pipes = [[1, 2, 1], [2, 3, 1]]

In this case, the optimal solution is to build a well at house 1 (cost 1), connect house 1 to house 2 (cost 1), and connect house 2 to house 3 (cost 1). The total cost is 1 + 1 + 1 = 3.

Another example:

n = 2
wells = [1, 10]
pipes = [[1, 2, 5]]

Here, it's cheaper to build a well at house 1 (cost 1) and connect house 1 to house 2 (cost 5). Total cost is 6. Alternatively, you could build a well at both houses for a cost of 11. The optimal answer is 6.

Write a function that takes the number of houses n, the wells array, and the pipes list as input, and returns the minimum cost to supply water to all the houses. What is the most efficient way to solve this problem, and what are the time and space complexities?

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

Can you provide the constraints on the number of wells and villages, as well as the cost of digging a well versus the cost of connecting villages?
Are the costs (digging wells or connecting villages) guaranteed to be non-negative integers?
Is it possible for a village to be unreachable (i.e., completely disconnected from the water supply) if I only build wells at other villages and connect them?
If there are multiple ways to minimize the cost of water distribution, is any valid solution acceptable, or is there a preferred solution based on some criteria?
What should I return if the number of villages is zero or the wells/pipes inputs are empty/null?

Brute Force Solution

Approach

The brute force approach means we are going to try every single possible way to connect wells or build new wells to supply water to each house. We will then choose the cheapest possible way to do it after having explored every possibility.

Here's how the algorithm would work step-by-step:

First, consider the option where every house builds its own well. Calculate the total cost for this scenario.
Next, consider the option where all houses connect to a single well. We need to try both building a new well at any one of the house locations or connecting all houses to an existing well. Calculate the total cost for each scenario.
Now, consider possibilities where some houses connect to one well, and other houses build their own wells. Think about every possible combination of houses connected to a central well versus houses with individual wells. Calculate the total cost for each combination.
Then, think about scenarios where houses are divided into multiple groups, with each group sharing a single well. Explore every possible grouping and well location within each group. Calculate the total cost for each grouping.
Continue increasing the complexity of the groupings until you've explored every possible way houses can share wells or have their own.
Finally, compare the total cost of all the scenarios we've calculated. The scenario with the lowest total cost is the best solution.

Code Implementation

def optimize_water_distribution_brute_force(number_of_houses, well_costs, pipe_costs):
    minimum_cost = float('inf')

    # 1. Every house builds its own well
    current_cost = sum(well_costs)
    minimum_cost = min(minimum_cost, current_cost)

    # 2. All houses connect to a single well, or build their own
    for well_location in range(number_of_houses):
        current_cost = well_costs[well_location]
        for house_index in range(number_of_houses):
            if house_index != well_location:
                current_cost += pipe_costs[house_index][well_location]
        minimum_cost = min(minimum_cost, current_cost)

    # 3. Some houses connect, others build their own.
    for i in range(1, 2**number_of_houses):
        current_cost = 0
        houses_with_wells = []
        houses_connected = []

        for j in range(number_of_houses):
            if (i >> j) & 1:
                houses_with_wells.append(j)
            else:
                houses_connected.append(j)

        # Each house with well will have its own well cost
        for house_index in houses_with_wells:
            current_cost += well_costs[house_index]

        # For houses connecting, find the cheapest connection
        for house_index in houses_connected:
            cheapest_connection = float('inf')
            
            # Iterate through available wells to find the optimal connection
            for well_index in houses_with_wells:
                cheapest_connection = min(cheapest_connection, pipe_costs[house_index][well_index])

            # If no wells are available, build one
            if not houses_with_wells:
                cheapest_connection = well_costs[house_index]
                
            current_cost += cheapest_connection

        minimum_cost = min(minimum_cost, current_cost)

    return minimum_cost

Big(O) Analysis

Time Complexity

O(2^n * n) – The brute force approach explores all possible combinations of houses having their own wells or being connected to shared wells. There are 2^n possible subsets of houses, each representing a combination of houses that might share a well. For each subset, we iterate through the houses to consider the well location, which costs O(n). Combining these, for each of the 2^n subsets, we potentially do n amount of work to consider where the shared well is located, leading to a time complexity of approximately O(2^n * n). This reflects the algorithm's exhaustive search through all possible well configurations.

Space Complexity

O(2^N) – The brute force approach explores all possible combinations of well placements and house connections. Steps 3, 4, and 5 involve creating different groupings of houses, which in the worst-case scenario of considering every possible subset of houses for well sharing, could lead to storing information about each subset. Considering the power set of N houses has 2^N possible subsets, storing these groupings requires memory proportional to 2^N. Therefore, the space used to store these intermediate combinations dominates, resulting in exponential space complexity.

Optimal Solution

Approach

The most efficient way to supply water to the village is to think of it as a network problem. We want to find the cheapest way to connect all the houses, either by building wells at some houses or connecting them to other houses that already have wells.

Here's how the algorithm would work step-by-step:

Imagine each house and each potential well location as a point on a map.
Calculate the cost of directly building a well at each house. Think of this as connecting the house to a special 'source' representing the underground water.
Calculate the cost of connecting each pair of houses with a pipe. This represents sharing water between houses.
Now, use a technique that finds the cheapest way to connect all the houses together, making sure every house gets water either directly from a well or through pipes from another house that has a well.
This technique focuses on always picking the cheapest connection (either building a well or laying a pipe) that hasn't been chosen yet and doesn't create a cycle, until all houses are connected.
The end result is the minimum total cost to provide water to all the houses in the village.

Code Implementation

def optimize_water_distribution(number_of_houses, well_costs, pipe_costs):
    edges = []
    # Add edges representing the cost of building a well at each house.
    for house_index, cost in enumerate(well_costs):
        edges.append((0, house_index + 1, cost))

    # Add edges representing the cost of connecting houses with pipes.
    for (house1_index, house2_index, cost) in pipe_costs:
        edges.append((house1_index, house2_index, cost))

    edges.sort(key=lambda edge: edge[2])

    parent = list(range(number_of_houses + 1))

    def find(house_index):
        if parent[house_index] != house_index:
            parent[house_index] = find(parent[house_index])
        return parent[house_index]

    def union(house1_index, house2_index):
        root_house1 = find(house1_index)
        root_house2 = find(house2_index)
        parent[root_house1] = root_house2

    minimum_cost = 0
    # Iterate through the sorted edges and add the cheapest ones first
    for house1_index, house2_index, cost in edges:
        # If the houses are not already connected, add the edge.
        if find(house1_index) != find(house2_index):
            union(house1_index, house2_index)
            minimum_cost += cost

    return minimum_cost

Big(O) Analysis

Time Complexity

O(n^2 log n) – The algorithm primarily utilizes Kruskal's algorithm or Prim's algorithm to find the Minimum Spanning Tree (MST). Calculating the cost of connecting each pair of houses with a pipe (or building a well) takes O(n^2) time to generate all possible edges. Sorting these edges by cost, as needed in Kruskal's, takes O(n^2 log n) time. The MST algorithm itself (after sorting) typically uses a disjoint set data structure which has a near-constant time complexity for union and find operations. Therefore, the dominant factor determining the time complexity is the sorting step, giving a total time complexity of O(n^2 log n).

Space Complexity

O(N^2) – The algorithm requires storing the cost of connecting each pair of houses and the cost of building a well at each house. The connections between houses can be represented by an adjacency matrix of size N x N, where N is the number of houses. Additionally, storing the cost of building a well at each house requires an array of size N. Dominating space is N x N or N^2. Therefore the auxiliary space complexity is O(N^2).

Edge Cases

Case	How to Handle
wells is null or empty	Return 0 immediately as there are no wells to connect.
pipes is null	Treat as an empty list of pipes; the algorithm should still function correctly, relying solely on well costs.
n (number of houses) is 0	Return 0 as there are no houses to supply water to.
wells contain very large values (close to integer limits)	Ensure intermediate calculations (summation of costs) doesn't lead to integer overflow; use long or appropriate data type.
All wells have the same cost	The algorithm should correctly identify the minimum cost even when all costs are identical.
The graph formed by the pipes contains cycles	The minimum spanning tree algorithm (Kruskal's or Prim's) must handle cycles correctly by only adding edges that don't create a cycle.
House cannot be reached from any well and no pipe exists.	Ensure all houses are connected by a pipe to at least one well, otherwise return -1 for no solution.
Number of houses is extremely large	Verify the chosen algorithm's time and space complexity scales efficiently with a large number of houses.