Find the Closest Marked Node

To solve the problem, we can use Dijkstra’s algorithm which is a greedy algorithm used for finding the shortest paths from a single vertex to all vertices in a weighted graph. We start from the source node, s, and use a priority queue (or a min-heap) to find the next closest vertex until we reach any of the marked vertices. Once we reach a marked vertex, we stop and return its distance from the source vertex.

Here is the solution:

  1. Create an adjacency list from the given edges.
  2. Use a priority queue to store the nodes, starting with the source node.
  3. Use a visited set to keep track of the nodes we’ve already visited.
  4. Use a distance dictionary initialized with infinite distances for all nodes except the source node, which should have a distance of 0.
  5. While the priority queue is not empty:
    • Dequeue the node with the shortest distance.
    • If this node is in the marked list, return its distance.
    • Otherwise, update the distance for its neighbors.
  6. If the priority queue is empty and we haven’t returned yet, return -1.

Let’s translate this into code:

import heapq

class Solution:
    def minimumDistance(self, n: int, edges: List[List[int]], s: int, marked: List[int]) -> int:
        # Step 1: Create an adjacency list
        adj_list = {i: [] for i in range(n)}
        for u, v, w in edges:
            adj_list[u].append((v, w))

        # Step 2 and 4: Initialize distance and priority queue
        distance = {i: float('inf') for i in range(n)}
        distance[s] = 0
        pq = [(0, s)]  # (distance, node)

        # Step 3: Initialize visited set
        visited = set()

        # Step 5: Dijkstra's algorithm
        while pq:
            dist_u, u = heapq.heappop(pq)

            # We've found the shortest path to a marked node
            if u in marked:
                return dist_u


            for v, w in adj_list[u]:
                if v not in visited and dist_u + w < distance[v]:
                    distance[v] = dist_u + w
                    heapq.heappush(pq, (distance[v], v))

        # Step 6: No path found to any marked node
        return -1

The above code should correctly compute the shortest distance from s to any of the marked nodes. If no such path exists, it will return -1.

Identifying Problem Isomorphism

“Find the Closest Marked Node” can be mapped to “Dijkstra’s Shortest Path Algorithm”.

The reason for choosing Dijkstra’s Algorithm is that both the original problem and Dijkstra’s algorithm deal with finding the shortest path in a weighted graph from a given start node. In this case, the algorithm needs to be adjusted to stop as soon as it encounters a node that is in the ‘marked’ list, as that would be the closest marked node.

A simpler problem is “Breadth First Search (BFS)” for a non-weighted graph. While it doesn’t take into account the weights or distances between nodes, it does introduce the concept of traversing through a graph from a given start point which is a key aspect of the original problem.

An advanced problem is the “Shortest Path in a Directed Acyclic Graph (DAG)”. This problem is more complex as it adds the constraint of the graph being a DAG, which affects the algorithm used to solve it. However, the core problem remains the same - finding the shortest path in a graph.

These mappings are isomorphic as they all involve the main idea of finding the shortest path in a graph.

10 Prerequisite LeetCode Problems

For this, the following is a good preparation:

  1. “207. Course Schedule” - This problem introduces the concept of directed graphs and checks for the presence of cycles, which is useful in understanding graph traversal and graph-based problems.

  2. “210. Course Schedule II” - This problem extends on the concept introduced in the previous problem by asking for the order of courses, effectively teaching the concept of topological sorting in graphs.

  3. “743. Network Delay Time” - This problem helps you understand the concept of weighted graphs and shortest path algorithms, which is directly applicable to the main problem.

  4. “787. Cheapest Flights Within K Stops” - This problem asks for the shortest path with a constraint, and is a good problem to understand how to apply Dijkstra’s or Bellman-Ford algorithms in a graph with constraints.

  5. “133. Clone Graph” - This problem helps in understanding how to work with nodes and edges, especially when there is a need to make a duplicate of the graph.

  6. “332. Reconstruct Itinerary” - This problem involves traversing a graph in a specific order, which might be helpful for understanding graph traversal techniques.

  7. “399. Evaluate Division” - This problem, though a bit indirect, teaches how to model a problem using graph data structure, which can be helpful in understanding and solving graph problems.

  8. “1514. Path with Maximum Probability” - This problem involves finding the maximum probability path in a graph, which is a variant of the shortest path problem and can help in understanding path-finding problems in graphs.

  9. “684. Redundant Connection” - This problem helps in understanding how to handle cycles in a graph, which is a useful concept in many graph problems.

  10. “261. Graph Valid Tree” - This problem involves checking whether a graph is a valid tree or not, which could be useful to understand graph structures and their properties.

These deal with graphs, specifically weighted directed graphs, and the application of shortest path algorithms, which are applicable to the main problem.

Problem Classification

Problem Statement:You are given a positive integer n which is the number of nodes of a 0-indexed directed weighted graph and a 0-indexed 2D array edges where edges[i] = [ui, vi, wi] indicates that there is an edge from node ui to node vi with weight wi. You are also given a node s and a node array marked; your task is to find the minimum distance from s to any of the nodes in marked. Return an integer denoting the minimum distance from s to any node in marked or -1 if there are no paths from s to any of the marked nodes.

Example 1:

Input: n = 4, edges = [[0,1,1],[1,2,3],[2,3,2],[0,3,4]], s = 0, marked = [2,3] Output: 4 Explanation: There is one path from node 0 (the green node) to node 2 (a red node), which is 0->1->2, and has a distance of 1 + 3 = 4. There are two paths from node 0 to node 3 (a red node), which are 0->1->2->3 and 0->3, the first one has a distance of 1 + 3 + 2 = 6 and the second one has a distance of 4. The minimum of them is 4.

Example 2:

Input: n = 5, edges = [[0,1,2],[0,2,4],[1,3,1],[2,3,3],[3,4,2]], s = 1, marked = [0,4] Output: 3 Explanation: There are no paths from node 1 (the green node) to node 0 (a red node). There is one path from node 1 to node 4 (a red node), which is 1->3->4, and has a distance of 1 + 2 = 3. So the answer is 3.

Example 3:

Input: n = 4, edges = [[0,1,1],[1,2,3],[2,3,2]], s = 3, marked = [0,1] Output: -1 Explanation: There are no paths from node 3 (the green node) to any of the marked nodes (the red nodes), so the answer is -1.


2 <= n <= 500 1 <= edges.length <= 104 edges[i].length = 3 0 <= edges[i][0], edges[i][1] <= n - 1 1 <= edges[i][2] <= 106 1 <= marked.length <= n - 1 0 <= s, marked[i] <= n - 1 s != marked[i] marked[i] != marked[j] for every i != j The graph might have repeated edges. The graph is generated such that it has no self-loops.

Analyze the provided problem statement. Categorize it based on its domain, ignoring ‘How’ it might be solved. Identify and list out the ‘What’ components. Based on these, further classify the problem. Explain your categorizations.

Clarification Questions

What are the clarification questions we can ask about this problem?

Identifying Problem Isomorphism

Can you help me with finding the isomorphism for this problem?

Which problem does it map to on Leetcode for problem?

Problem Analysis and Key Insights

What are the key insights from analyzing the problem statement?

Problem Boundary

What is the scope of this problem?

How to establish the boundary of this problem?

Distilling the Problem to Its Core Elements

Can you identify the fundamental concept or principle this problem is based upon? Please explain. What is the simplest way you would describe this problem to someone unfamiliar with the subject? What is the core problem we are trying to solve? Can we simplify the problem statement? Can you break down the problem into its key components? What is the minimal set of operations we need to perform to solve this problem?

Visual Model of the Problem

How to visualize the problem statement for this problem?

Problem Restatement

Could you start by paraphrasing the problem statement in your own words? Try to distill the problem into its essential elements and make sure to clarify the requirements and constraints. This exercise should aid in understanding the problem better and aligning our thought process before jumping into solving it.

Abstract Representation of the Problem

Could you help me formulate an abstract representation of this problem?

Given this problem, how can we describe it in an abstract way that emphasizes the structure and key elements, without the specific real-world details?


Are there any specialized terms, jargon, or technical concepts that are crucial to understanding this problem or solution? Could you define them and explain their role within the context of this problem?

Problem Simplification and Explanation

Could you please break down this problem into simpler terms? What are the key concepts involved and how do they interact? Can you also provide a metaphor or analogy to help me understand the problem better?


Given the problem statement and the constraints provided, identify specific characteristics or conditions that can be exploited to our advantage in finding an efficient solution. Look for patterns or specific numerical ranges that could be useful in manipulating or interpreting the data.

What are the key insights from analyzing the constraints?

Case Analysis

Could you please provide additional examples or test cases that cover a wider range of the input space, including edge and boundary conditions? In doing so, could you also analyze each example to highlight different aspects of the problem, key constraints and potential pitfalls, as well as the reasoning behind the expected output for each case? This should help in generating key insights about the problem and ensuring the solution is robust and handles all possible scenarios.

Provide names by categorizing these cases

What are the edge cases?

How to visualize these cases?

What are the key insights from analyzing the different cases?

Identification of Applicable Theoretical Concepts

Can you identify any mathematical or algorithmic concepts or properties that can be applied to simplify the problem or make it more manageable? Think about the nature of the operations or manipulations required by the problem statement. Are there existing theories, metrics, or methodologies in mathematics, computer science, or related fields that can be applied to calculate, measure, or perform these operations more effectively or efficiently?

Simple Explanation

Can you explain this problem in simple terms or like you would explain to a non-technical person? Imagine you’re explaining this problem to someone without a background in programming. How would you describe it? If you had to explain this problem to a child or someone who doesn’t know anything about coding, how would you do it? In layman’s terms, how would you explain the concept of this problem? Could you provide a metaphor or everyday example to explain the idea of this problem?

Problem Breakdown and Solution Methodology

Given the problem statement, can you explain in detail how you would approach solving it? Please break down the process into smaller steps, illustrating how each step contributes to the overall solution. If applicable, consider using metaphors, analogies, or visual representations to make your explanation more intuitive. After explaining the process, can you also discuss how specific operations or changes in the problem’s parameters would affect the solution? Lastly, demonstrate the workings of your approach using one or more example cases.

Inference of Problem-Solving Approach from the Problem Statement

Can you identify the key terms or concepts in this problem and explain how they inform your approach to solving it? Please list each keyword and how it guides you towards using a specific strategy or method. How can I recognize these properties by drawing tables or diagrams?

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

  1. Could you please provide a stepwise refinement of our approach to solving this problem?

  2. How can we take the high-level solution approach and distill it into more granular, actionable steps?

  3. Could you identify any parts of the problem that can be solved independently?

  4. Are there any repeatable patterns within our solution?

Solution Approach and Analysis

Given the problem statement, can you explain in detail how you would approach solving it? Please break down the process into smaller steps, illustrating how each step contributes to the overall solution. If applicable, consider using metaphors, analogies, or visual representations to make your explanation more intuitive. After explaining the process, can you also discuss how specific operations or changes in the problem’s parameters would affect the solution? Lastly, demonstrate the workings of your approach using one or more example cases.

Identify Invariant

What is the invariant in this problem?

Identify Loop Invariant

What is the loop invariant in this problem?

Is invariant and loop invariant the same for this problem?

Identify Recursion Invariant

Is there an invariant during recursion in this problem?

Is invariant and invariant during recursion the same for this problem?

Thought Process

Can you explain the basic thought process and steps involved in solving this type of problem?

Explain the thought process by thinking step by step to solve this problem from the problem statement and code the final solution. Write code in Python3. What are the cues in the problem statement? What direction does it suggest in the approach to the problem? Generate insights about the problem statement.

Establishing Preconditions and Postconditions

  1. Parameters:

    • What are the inputs to the method?
    • What types are these parameters?
    • What do these parameters represent in the context of the problem?
  2. Preconditions:

    • Before this method is called, what must be true about the state of the program or the values of the parameters?
    • Are there any constraints on the input parameters?
    • Is there a specific state that the program or some part of it must be in?
  3. Method Functionality:

    • What is this method expected to do?
    • How does it interact with the inputs and the current state of the program?
  4. Postconditions:

    • After the method has been called and has returned, what is now true about the state of the program or the values of the parameters?
    • What does the return value represent or indicate?
    • What side effects, if any, does the method have?
  5. Error Handling:

    • How does the method respond if the preconditions are not met?
    • Does it throw an exception, return a special value, or do something else?

Problem Decomposition

  1. Problem Understanding:

    • Can you explain the problem in your own words? What are the key components and requirements?
  2. Initial Breakdown:

    • Start by identifying the major parts or stages of the problem. How can you break the problem into several broad subproblems?
  3. Subproblem Refinement:

    • For each subproblem identified, ask yourself if it can be further broken down. What are the smaller tasks that need to be done to solve each subproblem?
  4. Task Identification:

    • Within these smaller tasks, are there any that are repeated or very similar? Could these be generalized into a single, reusable task?
  5. Task Abstraction:

    • For each task you’ve identified, is it abstracted enough to be clear and reusable, but still makes sense in the context of the problem?
  6. Method Naming:

    • Can you give each task a simple, descriptive name that makes its purpose clear?
  7. Subproblem Interactions:

    • How do these subproblems or tasks interact with each other? In what order do they need to be performed? Are there any dependencies?

From Brute Force to Optimal Solution

Could you please begin by illustrating a brute force solution for this problem? After detailing and discussing the inefficiencies of the brute force approach, could you then guide us through the process of optimizing this solution? Please explain each step towards optimization, discussing the reasoning behind each decision made, and how it improves upon the previous solution. Also, could you show how these optimizations impact the time and space complexity of our solution?

Code Explanation and Design Decisions

  1. Identify the initial parameters and explain their significance in the context of the problem statement or the solution domain.

  2. Discuss the primary loop or iteration over the input data. What does each iteration represent in terms of the problem you’re trying to solve? How does the iteration advance or contribute to the solution?

  3. If there are conditions or branches within the loop, what do these conditions signify? Explain the logical reasoning behind the branching in the context of the problem’s constraints or requirements.

  4. If there are updates or modifications to parameters within the loop, clarify why these changes are necessary. How do these modifications reflect changes in the state of the solution or the constraints of the problem?

  5. Describe any invariant that’s maintained throughout the code, and explain how it helps meet the problem’s constraints or objectives.

  6. Discuss the significance of the final output in relation to the problem statement or solution domain. What does it represent and how does it satisfy the problem’s requirements?

Remember, the focus here is not to explain what the code does on a syntactic level, but to communicate the intent and rationale behind the code in the context of the problem being solved.

Coding Constructs

Consider the code for the solution of this problem.

  1. What are the high-level problem-solving strategies or techniques being used by this code?

  2. If you had to explain the purpose of this code to a non-programmer, what would you say?

  3. Can you identify the logical elements or constructs used in this code, independent of any programming language?

  4. Could you describe the algorithmic approach used by this code in plain English?

  5. What are the key steps or operations this code is performing on the input data, and why?

  6. Can you identify the algorithmic patterns or strategies used by this code, irrespective of the specific programming language syntax?

Language Agnostic Coding Drills

Your mission is to deconstruct this code into the smallest possible learning units, each corresponding to a separate coding concept. Consider these concepts as unique coding drills that can be individually implemented and later assembled into the final solution.

  1. Dissect the code and identify each distinct concept it contains. Remember, this process should be language-agnostic and generally applicable to most modern programming languages.

  2. Once you’ve identified these coding concepts or drills, list them out in order of increasing difficulty. Provide a brief description of each concept and why it is classified at its particular difficulty level.

  3. Next, describe the problem-solving approach that would lead from the problem statement to the final solution. Think about how each of these coding drills contributes to the overall solution. Elucidate the step-by-step process involved in using these drills to solve the problem. Please refrain from writing any actual code; we’re focusing on understanding the process and strategy.

Targeted Drills in Python

Now that you’ve identified and ordered the coding concepts from a complex software code in the previous exercise, let’s focus on creating Python-based coding drills for each of those concepts.

  1. Begin by writing a separate piece of Python code that encapsulates each identified concept. These individual drills should illustrate how to implement each concept in Python. Please ensure that these are suitable even for those with a basic understanding of Python.

  2. In addition to the general concepts, identify and write coding drills for any problem-specific concepts that might be needed to create a solution. Describe why these drills are essential for our problem.

  3. Once all drills have been coded, describe how these pieces can be integrated together in the right order to solve the initial problem. Each drill should contribute to building up to the final solution.

Remember, the goal is to not only to write these drills but also to ensure that they can be cohesively assembled into one comprehensive solution.


Similar Problems

Can you suggest 10 problems from LeetCode that require similar problem-solving strategies or use similar underlying concepts as the problem we’ve just solved? These problems can be from any domain or topic, but they should involve similar steps or techniques in the solution process. Also, please briefly explain why you consider each of these problems to be related to our original problem. Do not include the original problem. The response text is of the following format. First provide this as the first sentence: Here are 10 problems that use similar underlying concepts: