Min Cost to Connect All Points
This problem can be approached using Kruskal’s algorithm or Prim’s algorithm to construct a Minimum Spanning Tree (MST). Since all points must be connected with the minimum cost, a MST approach fits this scenario.
We’ll use Prim’s algorithm:
 Create an array
cost
of size equal to the number of points and initialize it tofloat('inf')
except for the starting point which is 0. This array represents the cost to connect each point to the current MST.  Create a set
visited
to keep track of the points that have been added to the MST.  For each point:
 Pick the point
u
with the minimum cost that’s not visited.  Add
u
to the visited set.  For each unvisited point
v
, update its cost if the distance betweenu
andv
is smaller than its current cost.
 Pick the point
 Return the sum of the cost array.


This code uses a simple version of Prim’s algorithm to find and return the cost of the MST, effectively solving the problem.
10 Prerequisite LeetCode Problems
For this, the following are a good preparation:
“1202. Smallest String With Swaps”  This problem introduces the concept of using UnionFind data structure to solve problems that involve connecting elements in a graph.
“1135. Connecting Cities With Minimum Cost”  This problem is quite similar to the “1584. Min Cost to Connect All Points” in terms of finding the minimum cost to connect all points or cities, but it uses Kruskal’s algorithm to solve the problem.
“1168. Optimize Water Distribution in a Village”  It’s a more challenging problem that also uses the concept of minimum spanning tree to connect all points.
“261. Graph Valid Tree”  This problem introduces the concept of using UnionFind data structure to determine whether a graph is a valid tree.
“200. Number of Islands”  This problem is helpful in understanding how to traverse in a grid or matrix which will be useful when trying to calculate the Manhattan distance between points.
“785. Is Graph Bipartite?”  This problem introduces the concept of graph coloring to determine whether a graph is bipartite.
“743. Network Delay Time”  This problem involves finding the maximum network delay, which can be helpful in understanding the concept of distances in a graph.
“787. Cheapest Flights Within K Stops”  This problem deals with finding the minimum cost path with at most k stops which can be useful for understanding the concept of costs in a graph.
“721. Accounts Merge”  It involves the unionfind concept which will help understand the underlying concepts of “Min Cost to Connect All Points”.
“1631. Path With Minimum Effort”  This problem is about finding a path with the minimum total effort across a terrain which helps understand the concept of minimizing effort or cost.
These cover graph theory, UnionFind data structure, and minimum spanning tree concepts, which are critical in solving “1584. Min Cost to Connect All Points”.
Problem Classification
This is a graph theory problem in the domain of algorithms and optimization. The key ‘What’ components are:
 Points on a plane  Vertices in a graph
 Connecting points  Edges between vertices
 Costs between points  Weights on edges
 Minimum total cost  Optimization objective
 Require unique path  Constraint on solution
Based on these aspects, we can further classify this problem as:
 Graph modeling  Mapping points to graph representation
 Complete subgraph  Finding a spanning structure
 Weighted shortest paths  Optimizing total edge weights
 Constraint satisfaction  Enforcing unique path requirement
 Combinatorial optimization  Finding minimum cost combination
The core challenge is constructing an optimal graph connecting all points using weighted edges such that there is a unique path between every two vertices. This requires skills in graph algorithms, optimization, and constraint satisfaction.
Clarification Questions
Here are some clarification questions that could be asked about this minimum cost points connection problem:
Are the points always guaranteed to be distinct?
Can points be directly on top of each other at the same coordinates?
Does the path between points have to be a straight line or can it be arbitrary?
Can the path between two points involve multiple edges/hops?
Is the graph required to be undirected? Or can directed edges be used?
Is the objective to minimize total cost or just ensure connectivity?
Do edges need to have positive weights or can they be zero or negative?
Is it acceptable for numerical errors to slightly affect total cost calculation?
Are there guarantees on maximum coordinates or number of points?
Does solution need to minimize number of edges or just total cost?
Asking clarifying questions helps validate assumptions, exposes edge cases, and provides insights into intended behavior, constraints, and objectives. This additional context improves and focuses the problem analysis.
Problem Analysis and Key Insights
Here are some key insights gained from analyzing the problem statement:
The points can be modeled as vertices in a graph, with edges between them representing connections. This maps the problem to graph theory.
The cost function provides weighted edges based on manhattan distance between point pairs.
The goal is to find a minimum cost subgraph that connects all vertices. This suggests finding a minimum spanning tree or Steiner tree.
The unique path constraint adds complexity not typical to standard minimum spanning tree problems.
The problem resembles the traveling salesman problem but imposes the unique path requirement.
No indication if graph should be directed or undirected based on problem description.
No obvious constraints on the number of points or maximum coordinates.
Since total cost is being optimized, numeric precision errors may need to be handled.
The key insights are modeling the problem as a weighted graph and leveraging spanning tree algorithms, while incorporating the unique path constraint that differentiates this from standard graph problems.
Problem Boundary
Based on analyzing the problem statement, here is how I would summarize the scope:
Inputs:
 Set of 2D integer coordinate points
Outputs:
 Minimum total cost to connect points such that there is a unique path between every pair
Objective:
 Model points as graph vertices and connections as weighted edges
 Find the spanning subgraph with minimum total edge cost
 Subject to constraint of unique paths between every two vertices
Focus Areas:
 Graph construction and modeling
 Optimizing edge weights under constraints
 Reasoning about uniqueness constraint
Out of Scope:
 Actual path construction
 Directed vs undirected graph variations
 Memory/space complexity constraints
 Exact coordinate bounds
So in essence, the scope focuses on finding an optimal weight graph spanning all points that satisfies the unique path constraint. Implementation variations and output formatting are not constrained.
Here are some ways we can establish clear boundaries for the scope of this minimum cost points connection problem:
The only input is the set of 2D coordinate points. No other inputs.
The only output is the minimum total connection cost. We don’t need to return the actual graph edges or paths.
We only need to optimize the total cost, not necessarily the number of edges or other metrics.
The unique path constraint is defined between every pair of vertices. It is not limited to just consecutive edges.
The type of graph (directed/undirected) and implementation can vary, as long as it satisfies the constraints.
There are no set limits on maximum coordinate values or number of points.
Small floating point precision errors are acceptable as long as minimal.
The solution does not need to actually construct the minimum cost graph explicitly. Only the total cost matters.
Analysis should focus only on aspects directly related to minimizing cost under the unique path constraint.
Keeping the scope narrowly focused on just the core optimization criteria will prevent getting distracted by unnecessary details outside the core objective and constraints.
Distilling the Problem to Its Core Elements
Here are my thoughts on analyzing this point connection problem:
Fundamental concept:
 This problem boils down to optimally constructing a graph with a uniqueness constraint. The core techniques are weighted graph algorithms and constraint reasoning.
Simple explanation:
 We want to connect points on a plane using straight lines in the cheapest way possible, so that there is only one path between any two points.
Core problem:
 Finding the minimum cost connections between points such that there are unique paths between every pair. We can simplify it to minimizing cost under a uniqueness constraint.
Key components:
 Points as graph vertices
 Connections as weighted edges
 Total cost to optimize
 Unique path requirement
Minimal operations:
 Model points and connections as a graph
 Calculate edge weights as costs
 Find the minimum cost subgraph with unique paths
 Output total cost
So in essence, this problem involves applying weighted graph algorithms to find an optimal subgraph satisfying a global uniqueness constraint.
Visual Model of the Problem
Here are some ways we could visualize this point connection problem to provide more intuition:
Graph  Plot points as vertices, possible connections as edges with weights labeled.
Grid  Represent 2D plane with points positioned. Connections form graph.
Network  Show points and connections abstractly as a network diagram rather than spatial representation.
Animated connections  Animate different possible connections being drawn between points, with cost updating.
Interactive visualization  Allow plotting points and interactively making connections to understand cost impacts.
Local vs global  Show valid local connections that violate global unique path constraint.
Constraint graph  Model unique path requirements as edges/dependencies.
Representing the relationships visually can provide insight into how points can be connected optimally. Interactive visuals could further bring the problem to life.
Problem Restatement
Here’s how I would paraphrase the problem statement in my own words:
We are given a set of points identified by 2D coordinates.
We want to connect these points using straight line paths such that there is a unique path between any two points.
Each path incurs a cost equal to the Manhattan distance between the two points it connects.
Our goal is to find connections between all the points such that the total cost is minimized.
The key outputs are:
 Total minimum cost of connecting the points with unique paths
The key constraints are:
 The paths must form a connected subgraph containing all points
 There must be exactly one path between any two points
We don’t necessarily need to construct the actual pointtopoint paths  just determine the minimum total cost.
Essentially, we aim to model the points and possible connections as a graph and find the optimal subgraph that satisfies the unique path constraints. The focus is on minimizing total edge cost.
Abstract Representation of the Problem
Here is one way we could formulate an abstract representation of this point connection problem:
We are given a set of elements S and a distance function f that gives pairwise distances between elements.
The goal is to select connections between elements in S to form a unified structure such that:
 Every element is connected through some path to every other element
 There is exactly one path between every pair of elements
 The total cost of all connections, calculated using f, is minimized
More formally:
Inputs:
 Set S
 Distance function f: S x S > R
Output:
 Scalar total cost C
Objective:
 Select connections between elements in S, forming a unified structure
 Minimize total connection cost C
 Subject to:
 Path exists between every pair of elements
 Exactly one path between every pair of elements
By framing the problem in terms of abstract elements and connections, we can focus on the optimization goal and constraints separate from specifics like 2D points and grids. This helps relate the problem to general graph theory and combinatorial optimization.
Terminology
Here are some key concepts that are relevant to understanding this minimum spanning tree problem:
Graph: The points can be modeled as a graph, where each point is a vertex and edges connect the vertices. Finding the minimum cost to connect all points involves finding a minimum spanning tree in this graph.
Minimum spanning tree (MST): A spanning tree that connects all vertices in a graph at the minimum total cost. Finding the MST will give the optimal way to connect all the points at the lowest total cost.
Kruskal’s algorithm: An algorithm for finding the MST of a graph. It works by starting with an empty MST, then repeatedly adding the lowest cost edge that doesn’t form a cycle, until all vertices are included.
Greedy algorithm: Kruskal’s algorithm is a greedy algorithm, meaning it builds up the optimal solution (MST) by greedily choosing the best local option (lowest cost edge) at each step.
Disjoint sets: Kruskal’s uses disjoint sets to efficiently track connectivity and detect cycles. Each vertex is in its own disjoint set initially. Unioning sets when adding edges.
Manhattan distance: The distance metric used to calculate edge costs. It’s the sum of absolute differences in x and y coordinates between two points.
The key is modeling the points as a graph and using algorithms like Kruskal’s to find the MST, which gives the optimal connectivity solution. The Manhattan distance metric determines the edge costs.
Problem Simplification and Explanation
Here’s a simplified breakdown of this minimum spanning tree problem:
Imagine the points are houses in a city (analogy). We want to build roads connecting all the houses together. However, we want to do this as cheaply as possible by minimizing the total length of roads built.
The key concepts are:
 Houses = points
 Roads = edges connecting points
 Road distance between houses = Manhattan distance between points
 Connected houses by roads = connected points by edges
 Total length of all roads = total cost of all edges
 Minimum total road length to connect all houses = minimum total edge cost to connect all points
So it’s like connecting neighborhoods in a city with roads in the most efficient way possible.
To solve this, we can model the houses and potential roads as a graph. Then use Kruskal’s algorithm to determine the best roads to build in a greedy fashion to minimize total cost, while making sure we don’t create cycles.
The end result will be a minimum spanning tree, which gives us the optimal way to connect the houses while minimizing road length, similar to how the algorithm gives us the optimal connections between points to minimize total Manhattan distance.
Constraints
Here are some characteristics and conditions of this problem that we can leverage to find an efficient solution:
The number of points is relatively small (at most 1000). This means we can use a simpler O(n^2) algorithm instead of more complex graph algorithms.
The points are distinct. So no two points share the same x and y coordinates. This guarantees that distances between any two points will be greater than 0.
The x and y coordinates are bounded within a reasonable range of 106 to 106. We don’t have to worry about overflow errors when calculating distances.
Manhattan distance is easy to calculate as the sum of absolute differences. We can write a simple function to compute this. No need for more complex distance metrics.
We only care about minimizing the total cost, not the actual construction order. This allows a greedy approach like Kruskal’s to work well.
Disjoint sets with union by rank and path compression can achieve very fast connectivity queries. Crucial for Kruskal’s.
Overall, the small discrete input space, distinct points, bounded coordinates, simple distance metric and greedy optimality criteria allow us to design an efficient unionfind based Kruskal’s algorithm or even a simpler nearest neighbor connection heuristic.
Here are some key insights from analyzing the constraints of this problem:
The small number of points (at most 1000) means we can use simpler, less optimized algorithms instead of very complex graph algorithms. This reduces implementation complexity.
The distinct points and bounded coordinates allow us to precisely calculate distances between any two points without ambiguity or overflow. This makes distance calculations clean and welldefined.
Using the Manhattan distance metric keeps distance calculations simple and fast. We avoid slow computations associated with more complex distance formulas.
The problem only cares about total cost, not construction order. This opens up greedy approaches like Kruskal’s algorithm that progressively build up the minimum spanning tree by choosing local minimums.
The distinct points, small input size, and greedy optimality criteria means we can likely get away with a nearest neighbor heuristic that connects each point to its closest neighbor until all points are connected. This is simpler than Kruskal’s algorithm.
For Kruskal’s algorithm specifically, the use of disjoint sets with union by rank and path compression will make querying connectivity very fast. This is key to an efficient implementation.
Overall, the insights allow us to focus on simpler and faster algorithms and data structures that exploit the structure and constraints of the problem to achieve an efficient solution. We can avoid overengineering by matching the algorithm design to the characteristics of the input.
Case Analysis
Here are some additional test cases that cover a wider range of inputs:
Basic Case
Input: points = [[1,1],[2,2],[3,3]]
Output: 2
Reasoning: Connecting the 3 points in order gives the minimum total cost of 2.
** Repeated Points **
Input: points = [[1,1],[2,2],[2,2]]
Output: 0
Reasoning: With repeated points, the minimum cost path is 0 since no connections needed.
** Large Coordinates **
Input: points = [[100000,200000],[300000,400000],[500000,600000]]
Output: 1200000
Reasoning: Handles large coordinate values without overflow.
** Single Point **
Input: points = [[0,0]]
Output: 0
Reasoning: Minimum cost for single point is 0.
** Nonconvex Shape **
Input: points = [[0,0],[1,1],[2,0],[3,1],[4,0]]
Output: 4
Reasoning: Handles nonconvex shapes, not just convex hull.
** Suboptimal Local Choice **
Input: points = [[0,0],[3,2],[1,1],[2,3]]
Output: 5
Reasoning: Greedy choice may not be globally optimal.
** Large Number of Points **
Input: points = [[0,0],[1,1],...,[999,999]] // 1000 points
Output: 499500
Reasoning: Handles large input size near upper limit.
Boundary Cases:
 0 points
 1 point
 2 points
 Points in straight line
 All points identical
Edge Cases:
 Points with max/min x or y coordinates
 Repeated points
 Disconnected clusters of points
 Adjacent or overlapping points with 0 distance
Analyzing these cases helps validate the solution works for a wide range of inputs and avoids pitfalls.
Here are some ways to visualize these test cases:
Basic Case
Draw 3 points in a triangle formation and connect them sequentially.
Repeated Points
Draw 3 overlapping points on top of each other.
Large Coordinates
Use a coordinate plane with labeled axes ranging from 500k to 500k. Plot 3 points with large x and y values.
Single Point
Draw a single point.
NonConvex Shape
Draw 5 points in an Lshape formation, not along the convex hull. Connect optimally.
Suboptimal Local Choice
Draw 4 points in a square formation but connected vertically/horizontally suboptimally instead of diagonally.
Large Number of Points
Use a dense coordinate plane plot with many points (at least 2030).
Boundary Cases:
 Nothing drawn for 0 points
 Single point plot for 1 point case
 Two points plotted and connected
 Points in a straight line, connected
 Overlapping points
Edge Cases:
 Plot points on x and y axis limits
 Overlapping points
 Separate clusters of points
 Points plotted very close together or on top of each other
Visualizing these cases through simple plots and diagrams can help better understand the problem space and different scenarios that need to be handled. We can visually verify the optimality of the connections as well. Plots also allow issues to be identified visually.
Here are some key insights gained from analyzing the different test cases:
The solution needs to handle repeated and overlapping points gracefully by not creating duplicate connections.
Large coordinate values must work properly without overflow errors.
Single point and zero point cases should be handled as special cases optimally.
Convex hull is not sufficient, the solution must work for any general point configuration.
Greedy approaches may produce suboptimal local choices, so global optimality needs consideration.
Performance needs to scale well as number of points increases towards the upper limit.
Boundary cases like lines help test minimal required connections.
Edge cases like max coordinates, disconnected clusters, adjacent points etc stress test robustness.
Visualizing cases provides intuition and allows geometrical verification of optimality.
A wide variety of test cases validates correctness across the problem’s entire input space.
Key insights are the need to handle degenerate cases properly, ensure global optimal connections, scale efficiently for large inputs, and test robustness via diverse edge cases. Visualizing test cases also provides significant intuitive understanding. Overall, exhaustive and diverse test data improves confidence in the solution quality.
Identification of Applicable Theoretical Concepts
Here are some mathematical and algorithmic concepts that can help simplify and solve this minimum spanning tree problem more efficiently:
Graph theory  Model the points as vertices and connections as edges in a graph. Then finding the minimum spanning tree is a wellstudied graph theory problem.
Greedy algorithms  Kruskal’s algorithm provides an efficient greedy approach to find the MST of a graph by adding lowest cost edges first.
Unionfind data structure  Using disjoint sets with unionbyrank and path compression allows efficient connectivity queries needed for Kruskal’s.
Heuristic algorithms  A nearest neighbor heuristic that progressively connects each point to its closest neighbor is simpler and may work well in practice.
Computational geometry  Applying concepts like convex hulls, bounding boxes, spatial indexing (quadtrees, kd trees) can help optimize nearest neighbor searches.
Metric properties  Manhattan distance obeys metric properties like triangle inequality. This can help prune infeasible connections.
Dynamic programming  For a fixed number of points, an optimal solution can be calculated bottomup by considering all possible connections.
Relaxation  Allowing fractional edges and solving the mincost fractional spanning tree first provides a lower bound.
Overall, viewing this problem through the lens of graph theory and known algorithms like Kruskal’s provides a very effective solution methodology.
Simple Explanation
Here’s how I would explain this problem in simple, nontechnical terms:
This problem is like planning the best way to build roads to connect a bunch of neighborhoods in a city. Each neighborhood is at a certain location on a map. We want to build roads between the neighborhoods so that every neighborhood is connected to every other neighborhood.
But we don’t want to build more roads than necessary, because each road costs money to build. So we want to find the minimum number of roads needed to connect all the neighborhoods, while making sure we don’t create cycles or dead ends.
For example, if there are 3 neighborhoods  A, B and C  we would build just 2 roads  one between A and B, and one between B and C. We don’t need a road directly between A and C, because you can already get from A to C through B. This is the cheapest way to connect all 3.
In programming terms, the neighborhoods are called “points”, the location of each neighborhood is its (x, y) coordinates, and the cost to build a road between two neighborhoods is the Manhattan distance between their coordinates.
The goal is to connect all the points together while minimizing the total Manhattan distance of the roads built. This ensures we don’t overspend on roads and connect the neighborhoods efficiently.
Does this help explain the problem in a simpler, realworld way? Let me know if you need any clarification or have additional questions!
Problem Breakdown and Solution Methodology
Here is a stepbystep explanation of how I would approach solving this minimum spanning tree problem:
Model the points as vertices and potential connections as edges in a graph. Each vertex represents a point, and each edge between two vertices has a cost equal to the Manhattan distance between those two points.
Apply Kruskal’s algorithm to find the minimum spanning tree (MST) of this graph. Kruskal’s works by starting with an empty MST, then greedily adding the lowest cost edge that doesn’t create a cycle, repeating this process until all vertices are connected.
For example, with points A, B, C:
 Start with empty MST
 Add lowest cost edge AB
 Add next lowest cost edge BC
 MST done. Total cost = cost of AB edge + cost of BC edge
Use a disjoint set data structure to efficiently check connectivity and detect cycles. This allows us to determine if adding an edge would connect two previously unconnected vertices or create a cycle.
Iterate through the edges in order of increasing cost. For each edge:
 Check if the two vertices are already connected using the disjoint set.
 If not, add that edge to the MST and union the sets containing those two vertices.
 Once all vertices are connected, the MST is complete. Return the total cost.
This greedily builds up the optimal MST by choosing the lowest cost edges first while maintaining connectivity info with the disjoint set.
If the number of points increased, the runtime would increase since we’d have more edges to consider. But Kruskal’s algorithm is efficient, with runtime O(E log V) for E edges and V vertices.
Let’s walk through an example with points [(0,0),(1,1),(2,2)]:
 Add edge (0,0)(1,1) to MST
 Add edge (1,1)(2,2) to MST
 Total cost = 2
Inference of ProblemSolving Approach from the Problem Statement
Here are the key terms and how they guide my approach:
Points  Represented as vertices in a graph. Need to connect them.
Connect  Indicates edges between vertices. Goal is connecting all vertices.
Minimum cost  Optimize cost, suggest finding minimum spanning tree.
Simple path  Cycles not allowed, MST forms a simple graph.
Manhattan distance  Used to calculate edge weights in the graph.
Graph  Connectivity structure informs using graph algorithms.
Minimum spanning tree (MST)  Wellstudied graph concept, guides using Kruskal’s.
Kruskal’s algorithm  Greedy approach constructs MST by adding lowest cost edges first.
Disjoint sets  Efficient connectivity checking needed for Kruskal’s via disjoint sets.
Greedy algorithm  Kruskal’s greedily optimizes the MST in each step.
The core modeling using graphs and the problem goal of minimizing connectivity cost points towards finding the MST of the graph via Kruskal’s algorithm. Disjoint sets provide efficient cycle checking. Greedy optimization one edge at a time constructs the optimal MST.
So the key terms clearly motivate using a graph representation along with the appropriate algorithms and data structures like Kruskal’s and disjoint sets to efficiently construct the minimum cost connections.
We can visualize and recognize key properties of this problem using diagrams:
Points as Graph Vertices
Draw points as labeled circles. Connect two points with an edge labeled with distance. This represents modeling points as a graph.
Minimum Spanning Tree
Illustrate the construction of the MST starting from an empty graph then greedily adding lowest cost edges first until all vertices are connected. Show how total edge cost is calculated.
Disjoint Sets
Show disjoint sets as separate collections/groupings of vertices. When adding an edge, show the sets being unioned together.
Kruskal’s Algorithm
Stepbystep diagram showing edges considered in order of cost, merging disjoint sets, final MST formation.
Manhattan Distance
Mini grid diagram showing two points and the Lshaped path between them to illustrate Manhattan distance calculation.
Counterexample Cycles
Draw a small graph with a low cost cycle that Kruskal’s algorithm correctly avoids.
These visual representations of the vertices, edges, distances, disjoint sets, and algorithm execution help build intuition for the concepts and properties needed to solve the problem. Diagrams provide an understandable and recognizable view of the abstraction.
How did you infer from the problem statement that this problem can be solved using ?
Simple Explanation of the Proof
I’m having trouble understanding the proof of this algorithm. Could you explain it in a way that’s easy to understand?
Stepwise Refinement
Here is a stepwise refinement for solving this minimum spanning tree problem:
Highlevel approach: Use Kruskal’s algorithm to find the minimum spanning tree
Break down into actionable steps:
Represent points as vertices and connections as edges in a graph
Define a function to calculate Manhattan distance between two points
Initialize a disjoint set forest where each vertex is in its own set
Sort the edges by ascending cost (Manhattan distance)
Iterate through edges from lowest cost to highest:
Check if the two vertices of the edge belong to the same disjoint set
If not, add edge to MST, union the two sets
Repeat until all vertices connected in MST
Return total cost of MST
 Independent subproblems:
Calculating Manhattan distance between points
Disjoint set operations (make set, union, find)
Sorting edges by cost
 Repeatable patterns:
Main loop iterating over sorted edges
Calculating distance cost for each edge
Querying and updating disjoint sets
Breaking the problem down into modular steps allows parallelization of subtasks like distance calculations and sorting. The main loop has a repetitive pattern of considering edges based on cost and updating connectivity. This refinement decomposes the problem into its constituent components.
Solution Approach and Analysis
Here is a detailed stepbystep approach to solving this minimum spanning tree problem:
Model the points as vertices and potential connections as edges in a graph. Represent them using adjacency list or matrix. This allows us to leverage graph algorithms.
Define a function to calculate the Manhattan distance between two points. This will give us the edge weights. For example, distance between (0,0) and (3,4) is 03 + 04 = 7.
Initialize a disjoint set forest, where each vertex is in its own disjoint set. We will use this data structure to efficiently track connections and detect cycles.
Sort the edges by ascending order of their cost, which is the Manhattan distance between the two vertices. This allows us to consider the lowest cost edges first.
Iterate through the sorted edges:
 Check if the two vertices of the current edge belong to the same disjoint set using the find() operation.
 If not, it means connecting them won’t form a cycle. So add the edge to the minimum spanning tree, and union the two sets.
Repeat step 5 until all vertices are connected into one single disjoint set. At this point the minimum spanning tree is constructed.
Calculate the total cost by summing the costs of all edges in the MST. Return this value.
If the number of points increases, the asymptotic runtime increases as O(ElogV) but the algorithm still remains efficient due to the sorting and disjoint set optimization.
Let’s walk through an example with points (0,0), (1,1), (2,2) and the edges between them:
 Add edge (0,0)(1,1) to MST, cost = 1
 Add edge (1,1)(2,2) to MST, cost = 1
 Total cost = 2
This stepbystep breakdown illustrates how we apply graph concepts and optimized algorithms like Kruskal’s to efficiently construct the minimum spanning tree.
Identify Invariant
The key invariant in this minimum spanning tree problem is:
At each step of the algorithm, the set of edges chosen thus far forms a valid partial minimum spanning tree.
This means the edges selected and added to the minimum spanning tree (MST) at each iteration maintain the following invariant properties:
 The edges form one or more disjoint subtrees that connect the vertices
 There are no cycles created in the MST so far
 The edges chosen produce the minimum total cost out of all possible combinations considered until that point
We can maintain this invariant by:
 Considering edges in ascending order of cost
 Using a disjoint set data structure to detect cycles
 Only adding an edge if it connects two previously disconnected subtrees
As long as these conditions are met, the edges in the MST will be the optimal lowest cost connections between the vertices processed so far.
The final MST produced after all edges are considered will be the optimal solution with minimum total cost out of all possible spanning trees.
This invariant allows us to incrementally construct the MST in a greedy fashion while ensuring global optimality by maintaining the key property that the edges chosen produce the minimum cost partial spanning tree at every iteration.
Identify Loop Invariant
The loop invariant in Kruskal’s algorithm for finding the minimum spanning tree is:
At the start of each iteration of the main loop, the edges chosen so far form a forest (a set of trees) that connects some or all of the vertices such that:
The edges form a valid partial minimum spanning tree on the vertices spanning the trees in the forest.
The total weight of all edges in the partial MST is the minimum over all possible partial MSTs connecting those vertices.
There are no cycles in the partial MST.
To maintain this loop invariant:
 Edges are considered in ascending weight order.
 A disjoint set data structure is used to detect cycles.
 An edge is only added if it connects two disjoint sets.
At the end of the loop, the edges will form the minimum spanning tree over the entire graph.
The key is that the invariant maintains that at every step, the edges chosen produce the minimum weight partial MST on the vertices processed so far. Following greedy selection by weight and using unionfind allows this to be true throughout.
The invariant and loop invariant are essentially the same concept for Kruskal’s algorithm in this minimum spanning tree problem.
The invariant refers to the key property that must be maintained at each step for the algorithm to work correctly.
The loop invariant is specifically the invariant property that needs to hold true at the start and end of each iteration of the main loop of Kruskal’s algorithm.
For Kruskal’s algorithm, the invariant is:
“The edges selected so far form a valid partial minimum spanning tree on the processed vertices.”
This must be true before entering the loop, after each iteration, and upon final completion.
So for Kruskal’s algorithm, the invariant and loop invariant are identical  they refer to the same crucial property that the edges chosen up to any given point form the optimal partial minimum spanning tree on the vertices processed so far.
In summary:
Invariant = Edges chosen so far form the partial MST
Loop Invariant = The invariant that must hold at the start and end of each loop iteration
So in this case, the invariant and loop invariant are equivalent and interchangeable terms. They both refer to the same key property that enables Kruskal’s algorithm to function correctly.
Thought Process
Here is the thought process and steps to solve this minimum spanning tree problem:
The problem statement cues:
 Points connected by simple paths
 Minimum total cost
 Manhattan distance
This suggests modeling the problem as a graph and using a minimum spanning tree (MST) algorithm.
Thought process:
Represent points as graph vertices and potential connections as edges
Define function to calculate Manhattan distance between two points for edge weights
Use Kruskal’s algorithm to find the MST
 Sort edges by weight
 Add lowest weight edge that doesn’t form cycle
 Repeat until all vertices connected
 Total cost is sum of all edge weights in MST
Python code:


This shows the stepbystep thought process to model the problem and apply MST algorithms to arrive at an optimal solution.
Establishing Preconditions and Postconditions
Here are the details for the minCostConnectPoints() method:
 Parameters:
 points: List[List[int]]  The input points specified by x,y coordinates
 Preconditions:
 points contains at least 2 elements
 All point coordinates are valid integers
 Method Functionality:
 Models points as graph and applies Kruskal’s algorithm to find the minimum spanning tree
 Calculates Manhattan distance to determine edge weights
 Returns the total sum of edge weights in the minimum spanning tree
 Postconditions:
 The graph is transformed into a minimum spanning tree
 The total cost of the MST is calculated and returned
 Error Handling:
 If less than 2 points, raises ValueError
 If invalid coordinates, raises TypeError
 On any other input error, raises RuntimeError
So in summary, this method takes in the point coordinates, builds a graph, runs Kruskal’s algorithm, and returns the computed minimum cost of connections while validating input and handling potential errors.
Problem Decomposition
Here is how I would break down this minimum spanning tree problem:
Problem Understanding: Find the minimum total cost to connect a set of points such that there is a single path between any two points. Cost is Manhattan distance.
Initial Breakdown:
 Model points and connections as a graph
 Determine costs between points using Manhattan distance
 Find the minimum spanning tree of the graph
 Calculate total cost of MST
 Subproblem Refinement:
 Construct graph from points
 Define Manhattan distance function
 Run Kruskal’s algorithm to find MST
 Sort edges by cost
 Use disjoint sets for connectivity
 Sum edge costs in MST
 Task Identification:
 calculate_manhattan_distance()
 build_graph()
 sort_edges()
 find()
 union()
 add_edge_to_mst()
Task Abstraction: Good as is
Method Naming: Good as is
Subproblem Interactions:
 Build graph
 Calculate distances
 Sort edges
 Run Kruskal’s:
 Repeat:
 Check connectivity
 Add edge to MST
 Update connectivity
 Repeat:
 Calculate total MST cost
This breaks the problem down into modular subtasks, identifies reusable units, and determines order of operations and dependencies.
From Brute Force to Optimal Solution
Here is a brute force approach and how we can optimize it:
Brute force:
 Try every possible set of connections between the points
 Check if each forms a valid spanning tree
 Calculate total cost for each
 Return the minimum cost spanning tree
This evaluates an exponential number of combinations.
Optimizations:
 Model as a graph:
 Represent points as vertices, connections as edges
 Allows using graph algorithms
 Use a greedy algorithm:
 Kruskal’s algorithm adds lowest cost edge at each step
 Greedily optimizes and avoids checking all combinations
 Sort edges by cost:
 Consider lowest cost edges first in Kruskal’s
 More likely to be in MST
 Improves efficiency
 Use disjoint set for connectivity:
 Avoid recursion to check connections
 Makes cycle checking O(1) time
 Stop when all vertices connected:
 Don’t need to process remaining edges
 Reduces work
These optimizations improve the time complexity from O(n!) for brute force to O(E log E) for Kruskal’s, where E is the number of edges.
Space complexity improves from O(n^2) to store all spanning trees, to O(V) for disjoint set storage.
In summary, modeling as a graph, applying greedy/Kruskal’s algorithm, sorting edges, efficient connectivity with disjoint sets, and early stopping together optimize both time and space complexity significantly compared to brute force.
Code Explanation and Design Decisions
Here is an analysis of the logic and reasoning behind the Kruskal’s minimum spanning tree algorithm code:
The input parameters are the graph (vertices, edges) and parent array for disjoint sets. These represent the problem domain.
The main loop iterates over all edges sorted by weight. Each iteration considers the next lowest cost edge to potentially add to the MST.
The if condition checks if adding the edge would form a cycle using the disjoint sets. This upholds the tree structure.
The parent array is updated via union() when adding an edge. This maintains connectivity information.
The invariant is that edges chosen so far form a partial MST. Using sorted edges and unionfind maintains this.
The final MST contains optimal low cost edges so total cost is minimized. This achieves the minimum spanning tree objective.
Overall, the logic flow reinforces adding sorted edges to build up the MST in a greedy fashion using unionfind for efficiency and maintaining key invariants. The code directly maps to Kruskal’s algorithm to achieve the goals of the problem.
Coding Constructs
Here is an analysis of the highlevel logic of this minimum spanning tree code:
It uses a greedy graph algorithm approach by incrementally building up the MST.
I would explain that it carefully selects the shortest connections between points in order to connect all of them together as cheaply as possible.
It uses core logic like loops, conditional checks, data structures, and functions/methods.
The algorithm sorts all possible connections from cheapest to most expensive. It then walks through these connections sequentially, adding them if they don’t duplicate existing connections, until all points are connected.
The key steps are modeling the points and connections as a graph, sorting the graph edges by weight, iteratively adding the lowest weight edges while tracking connectivity, and finally calculating the total cost. This builds up the optimal MST efficiently.
The core algorithmic strategy is Kruskal’s algorithm for minimum spanning trees. It employs a greedy approach by connecting points incrementally using the cheapest option at each step. It uses a disjoint set to efficiently track connectivity.
Overall, this analysis extracts the highlevel problem solving approach, semantics, logic patterns, algorithmic techniques, and data transformations being performed, without referring to language specifics.
Language Agnostic Coding Drills
Here is a decomposition of the key coding concepts in this problem:
Easy
 Variable assignment  Initialize vertices, edges, MST
 Array/List management  Add/access graph edges
 Math operations  Calculate Manhattan distance
 Looping  Iterate over edges
Medium
 Sorting  Sort edges by weight
 Methods/Functions  Encapsulate distance calculation
 Data structures  Use disjoint set for tracking connectivity
 Conditional checks  Avoid cycles when adding edges
Advanced
 Graph modeling  Represent points/connections as vertices/edges
 Algorithm design  Kruskal’s MST using greedy approach
 Abstraction  Modularize distance function, unionfind methods
 Optimization  Use unionrank heuristic, path compression
The overall problem solving approach is:
 Model points as graph vertices and connections as edges
 Write distance calculation function
 Initialize disjoint set
 Sort edges by weight
 Loop over edges in order:
 Check connectivity
 Add edge to MST
 Update connectivity
 Return total MST weight
Each coding concept builds on the previous ones, enabling the overall algorithm. Mastering these fundamental concepts equips one to implement graph algorithms like Kruskal’s.
Targeted Drills in Python
Here are Python coding drills for each concept:
Basic
Variable assignment:


Array manipulation:


Math operations:


Looping:


Intermediate
Sorting:


Functions:


Data structures:


Conditionals:


Advanced
Graph modeling:


Kruskal’s algorithm:


Abstraction:


Optimization:


These can be integrated in order to build up the full MST algorithm.
Q&A
Similar Problems
Here are 10 related problems that use similar concepts:
Number of Islands (200)  Models grid as graph, uses DFS/disjoint sets to find connected components like Kruskal’s algorithm.
Course Schedule (207)  Detects cycles in graph using topology sorting similar to cycle detection in Kruskal’s.
Cheapest Flights Within K Stops (787)  Graph shortest paths problem with greedy optimization like Kruskal’s.
Minimum Cost to Connect Sticks (1167)  Greedy MSTlike solution, combines smallest cost sticks each step.
Minimum Cost to Connect Points (1584)  Very similar MST problem, uses Kruskal’s algorithm.
Optimize Water Distribution in a Village (1168)  Another MSTbased problem, models houses and pipes like graph.
Couples Holding Hands (765)  Models seating as graph, finds MST to minimize cost metric.
Spell Checker (2047)  Graph modeling of word connections, uses BFS which is like Kruskal’s traversal.
Most Stones Removed with Same Row or Column (947)  Models stones as graph, connectivitybased using disjoint sets.
Regions Cut By Slashes (959)  Converts input to graph, uses DFS/unionfind for connectivity like Kruskal’s.
The shared concepts are graph modeling, traversing connectivity, optimization approaches like MST, and use of unionfind/DFS for efficient connectivity queries.