Flatten a Multilevel Doubly Linked List

The problem is flattening a multilevel doubly linked list. In this list, apart from the normal next and previous pointers, there’s also a child pointer, which may or may not point to another doubly linked list. The goal is to “flatten” all these child lists into the main list.

Here’s an approach to solving this problem:

  1. Start at the head of the main list.
  2. Traverse through the list, and for each node:
    • If it has a child, recursively flatten the child list and then connect it to the current list.
    • Continue the process for the next node.
  3. Return the head of the flattened list.

Here’s the code:

class Solution:
    def flatten(self, head: 'Optional[Node]') -> 'Optional[Node]':
        if not head:
            return head

        dummy = Node(0, None, head, None)
        prev = dummy

        stack = [head]

        # Use a stack to process nodes in Depth First manner
        while stack:
            curr = stack.pop()
            prev.next = curr
            curr.prev = prev

            # If there is a next node, push it to stack
            if curr.next:

            # If there is a child node, push it to stack and detach it
            if curr.child:
                curr.child = None

            prev = curr

        # Detach the dummy head from the flattened list
        dummy.next.prev = None
        return dummy.next

The code uses a stack to implement a Depth-First Search (DFS) through the linked list. When it encounters a node with a child, it recursively flattens the child list and then attaches it to the main list. When it’s done, it returns the head of the newly flattened list.

10 Prerequisite LeetCode Problems

“Flatten a Multilevel Doubly Linked List” involves linked list traversal and manipulation, especially dealing with nested linked lists. Below are some problems to understand how to manipulate linked lists and deal with nested data structures:

  1. 206. Reverse Linked List: This is a basic problem that will familiarize you with traversing and manipulating singly linked lists.

  2. 328. Odd Even Linked List: This problem involves manipulating links in a linked list.

  3. 138. Copy List with Random Pointer: This problem is about creating a deep copy of a linked list where each node contains an extra pointer that can point to any node in the list.

  4. 234. Palindrome Linked List: This problem requires you to understand the structure of linked lists deeply.

  5. 141. Linked List Cycle: This problem involves detecting a cycle in a linked list and can help you understand the concept of traversing through a linked list.

  6. 142. Linked List Cycle II: This problem extends the previous problem by asking for the node where the cycle begins.

  7. 24. Swap Nodes in Pairs: This problem requires you to understand how to manipulate links in a linked list.

  8. 25. Reverse Nodes in k-Group: This problem is a more complex linked list manipulation problem that requires you to reverse segments of a linked list.

  9. 19. Remove Nth Node From End of List: This problem requires you to navigate through a linked list and remove a specific node.

  10. 148. Sort List: This problem asks you to sort a linked list in O(n log n) time, which might require you to implement a more complex algorithm on a linked list.

Solving these problems should give you a good foundation for understanding how to traverse and manipulate linked lists, which is a key part of solving the problem “Flatten a Multilevel Doubly Linked List”.

Problem Analysis and Key Insights

Here are some key insights gained from analyzing the problem statement:

  • Need to traverse recursively through child pointers to cover full multilevel structure. Can’t just traverse next pointers at one level.

  • Maintaining previous/next connections important for preserving doubly linked list structure when flattening.

  • Requires in-place updates. Can’t recreate new list nodes. Need to carefully relink nodes.

  • Child pointers must be cleaned up and set to null in flattened form.

  • Structure shares similarities with tree traversal, except nodes have double links.

  • Constraint of not exceeding 1000 nodes indicates brute force traversal is likely sufficient.

  • Description provides clear example structure and serialization format of input/output.

  • No obvious constraints on space complexity, so additional storage allowed if needed.

The key insights are around maintaining previous/next linkage integrity during recursive traversal and flattening via in-place pointer adjustments. The constraints allow brute force traversal.

Problem Boundary

Based on analyzing the problem statement, here is how I would summarize the scope:


  • Head node of multilevel doubly linked list


  • Head node of flattened single level linked list


  • Flatten multilevel doubly linked list into standard single level linked list in-place

Focus Areas:

  • Recursively traversing hierarchy via child pointers
  • Manipulating node connections
  • Cleaning up child pointers
  • Maintaining previous/next integrity

Out of Scope:

  • Creating new nodes
  • Changing node values
  • Preserving intermediate hierarchy
  • Optimizing space complexity

So in essence, the scope focuses just on the structural transformation of flattening the shape of the linked list in-place. The key challenges are safely traversing the multilevel structure recursively and updating the node connections appropriately.

Here are some ways we can establish clear boundaries for the scope of this linked list flattening problem:

  • The only input is the head node of the original multilevel list. No other inputs.

  • The only output is the head of the flattened list. We don’t need to return or yield anything else.

  • We only need to flatten the structure, not change node values or duplicate/create nodes.

  • Flattening must be done in-place by changing pointers. A new list shouldn’t be created.

  • The flattened result should be a standard doubly linked list. Intermediate hierarchy not needed.

  • Child pointers should be set to null once flattened.

  • No constraints specified on time/space complexity, so can focus efforts on correct flattening.

  • The structure is recursive but bounded, so brute force recursion likely sufficient.

  • Only forward traversal needed. Back links just need rewiring, not traversal.

Keeping the scope narrowly focused on the key structural transformation of flattening avoids getting distracted by unnecessary operations or embellishments outside core requirements.

Problem Classification

This is a linked list problem in the domain of data structures. The key ‘What’ components are:

  • Doubly linked list - Contains node connections in both directions
  • Multilevel - Linked lists nested inside child pointers
  • Flatten - Transform into single level list
  • Child pointers - Link between different levels

Based on these aspects, we can further classify this problem as:

  • Linked list manipulation - Modifying structure of linked list
  • Recursive data - Processing nested recursive structure
  • Tree traversal - Visiting all nodes across hierarchy
  • In-place update - Modifying input list without copying

The core challenge is traversing the multilevel linked list recursively while flattening it into a single level structure in-place by manipulating the node connections appropriately.

This requires skills in recursive data structures, linked list manipulation, tree traversal, and in-place updates to transform the shape of the structure. The key is maintaining connections while flattening.

Distilling the Problem to Its Core Elements

Here are my thoughts on analyzing this multilevel linked list problem:

Fundamental concept:

  • This problem involves recursively processing a nested structure and transforming its shape without losing connections. The core technique is tree traversal and pointer manipulation.

Simple explanation:

  • We have a linked list structure where each node can point to another nested linked list. We want to flatten it into one level.

Core problem:

  • Flatten a multilevel linked list structure into a single level by manipulating pointers. We can simplify it to collapsing a recursive structure.

Key components:

  • Multilevel linked list
  • Child pointers to nested levels
  • Pointer manipulation to reshape structure
  • Maintaining previous/next connections

Minimal operations:

  • Recursively traverse nodes via child pointers
  • Rewire previous/next pointers to flatten
  • Set child pointers to null
  • Ensure single level list remains connected

So in essence, this problem involves recursively traversing a multilevel shape and carefully rewiring the connections to flatten it in-place.

Visual Model of the Problem

Here are some ways we could visualize this multilevel linked list flattening problem to provide more intuition:

  • Nested boxes - Show nested boxes representing each level, then open to flatten

  • Step diagram - Illustrate pointer manipulation at each step as levels are merged

  • Tree drawing - Represent multilevel structure as a tree and flatten branches

  • Animation - Animate traversal through layers with pointers relinking

  • Layer cake - Show stack of cakes representing levels, then flatten layers

  • Russian nesting dolls - Visualize opening nesting dolls, representing flattening

  • Folding paper - Demonstrate recursively folding and unfolding paper, like flattening layers

  • Painter’s tape - Show tape with nested branches, then pull to flatten out structure

Representing the multilevel structure using nesting metaphors helps build intuition for how to traverse the levels recursively and flatten into one layer by updating pointers sequentially. Animations could further reinforce this process.

Problem Restatement

Here’s how I would paraphrase the problem statement in my own words:

We are given a linked list where each node contains pointers to the next and previous nodes as well as a child pointer. This child pointer can link to another linked list one level deeper. The result is a multilevel nested linked list structure.

Our goal is to take this multilevel linked list and flatten it into a regular singly linked list structure where all nodes exist at the top level and no intermediate layers remain.

The flattened list should maintain the previous/next connections between nodes in the original order. Any child pointers should be set to null after flattening.

The key constraints are that we can’t create any new nodes, just manipulate existing ones, and the total number of nodes will not exceed 1000.

Essentially we need to traverse the multilevel structure recursively via child pointers, bringing each layer up to the top level one node at a time by rearranging the node connections appropriately. We have to do this in-place by updating pointers only.

Abstract Representation of the Problem

Here is one way we could formulate an abstract representation of this linked list flattening problem:

We are given a tree T with nodes that contain bidirectional links to parent and child nodes.

Our goal is to transform T into a singly linked list S by manipulating only the pointer structures.

Each node in S should be connected from the original nodes and order of T. The child pointers should be null.

More formally:


  • T, a recursively defined tree with bidirectional links


  • S, a flattened singly linked list


  • Traverse T recursively via child pointers
  • Manipulate T’s pointers to reshape structure into S
  • Null out child pointers
  • Maintain connections/order of nodes

By representing the multilevel structure as a recursive tree and the goal as transforming it into a flattened linked list, we can focus on the core pointer manipulation required while abstracting away the specifics of the linked list nodes. This framing relates it to fundamental tree traversal and shaping algorithms.


Here are some key terms and concepts that are important for this multilevel linked list problem:

Doubly linked list - Linked structure with next and previous pointers allowing bidirectional traversal.

Child pointer - Pointer to another nested linked list structure at next level.

Multilevel - Nested hierarchical structure via child pointers.

Flatten - Transform multilevel structure into single level by manipulating pointers.

In-place - Modifying existing object rather than making copy. Requires careful updates.

Tree traversal - Visiting all nodes in hierarchical tree-like structure. Needed for flattening.

Recursion - Applying logic recursively via child pointers to traverse multilevel shape.

Pointer manipulation - Rewiring next/previous pointers when restructuring nodes.

The core challenge is traversing the multilevel shape recursively via child pointers, and carefully manipulating the next/previous pointers in-place to flatten the structure while maintaining connections. Understanding concepts like tree traversal, recursion and pointer manipulation is key.

Problem Simplification and Explanation

Here’s one way I could break down this linked list flattening problem into simpler concepts using a metaphor:

Let’s compare the multilevel structure to a corporate organization chart.

The nodes are like employees. Next/previous pointers are peers on the same level.

Child pointers represent reporting structure - an employee’s manager sits one level above.

Flattening is like restructuring to remove middle management layers and have all employees report directly to the CEO.

We traverse the org chart following reporting lines recursively, moving employees to the top layer by redirecting their child pointer to the CEO node.

The key concepts are:

  • Nodes = Employees
  • Next/Prev = Peer connections
  • Child pointers = Reporting structure
  • Flattening = Restructuring org chart

This company hierarchy analogy provides an everyday way to understand the core idea of collapsing a multilevel recursive structure by redirecting child pointers.


Here are some characteristics and constraints from the problem statement that could help optimize finding an efficient solution:

  • Fixed upper limit of 1000 nodes - Abrute force approach is likely sufficient at this scale.

  • Doubly linked structure - Previous pointers already provide back links, avoiding manual tracking.

  • In-place updates - No need to copy nodes. Can directly manipulate existing nodes.

  • Child pointers given - Built-in links between levels that enable traversal.

  • Multilevel input - Recursion or stack likely needed to traverse hierarchy.

  • Serialization format known - Examples provide clear illustration of input/output structure.

  • Output as flat list - Simpler flattened structure than maintaining intermediate layers.

  • Node values not used - We can focus just on pointer manipulation.

  • Nodes not created - Avoiding node creation simplifies logic.

The fixed size limit, in-place updates, provided child pointers, detailed examples, and simpler flattened output all make the problem more tractable. The characteristics suggest recursion and direct manipulation are feasible.

Here are some key insights gained from analyzing the constraints:

  • Small fixed node limit allows brute force traversal rather than complex optimizations.

  • In-place updates mean we can directly manipulate pointers without copying nodes.

  • Child pointers built-in removes need to manually track layers.

  • Detailed examples illustrate edge cases and input/output structure.

  • Output as flat single level simplifies transformations needed.

  • Unchanging node values allow focusing just on pointer logic.

  • No new node creation avoids complex node splicing logic.

  • Doubly linked list provides back links for free.

  • Upper limit on nodes ensures recursive traversal will be finite.

The constraints allow a simple recursive traversal approach since optimizations like pruning aren’t needed at this scale. In-place updates, child pointers, and a simpler flattened output simplify the implementation.

Here are some key insights gained from analyzing the constraints:

  • Small fixed node limit allows brute force traversal rather than complex optimizations.

  • In-place updates mean we can directly manipulate pointers without copying nodes.

  • Child pointers built-in removes need to manually track layers.

  • Detailed examples illustrate edge cases and input/output structure.

  • Output as flat single level simplifies transformations needed.

  • Unchanging node values allow focusing just on pointer logic.

  • No new node creation avoids complex node splicing logic.

  • Doubly linked list provides back links for free.

  • Upper limit on nodes ensures recursive traversal will be finite.

The constraints allow a simple recursive traversal approach since optimizations like pruning aren’t needed at this scale. In-place updates, child pointers, and a simpler flattened output simplify the implementation.

Case Analysis

Here are some additional test cases covering edge cases and different scenarios:

Base Cases:

  • Empty list
  • Single node list
  • Two level list

Structure Variety:

  • Linear list - No child pointers
  • Full binary tree
  • Unbalanced - Linked lists of different lengths
  • Cyclic - Nodes linked in loop


  • Maximum nodes
  • Maximum depth

Edge Cases:

  • Null head node
  • Circular child pointers
  • Node with two child pointers

The key edge cases are empty list, null head, circular pointers, and nodes with multiple child pointers.

The other cases validate handling a variety of structures, constraints, imbalanced hierarchies, and cyclic connections. This provides broad test coverage.

Here are some key insights gained by analyzing the different test cases:

  • Empty and single node cases reveal need for defensive coding for edge cases.

  • Linear list validates flattening already flattened structure.

  • Maximum depth checks recursion can reach bottom.

  • Imbalanced hierarchy validates handling uneven sublists.

  • Cyclic connections test breaking loops.

  • Multiple child pointers validate properly wiring multiple branches.

  • Null heads test handling errors gracefully.

  • Circular pointers highlight need for cycle detection logic.

  • Maximum nodes validate scalability for large inputs.

Overall, the cases validate handling all shapes of hierarchies, edge cases, constraints, and tricky malformed inputs. Defensive coding is highlighted as essential. The cases help build confidence in a robust solution.

Identification of Applicable Theoretical Concepts

Here are some mathematical and algorithmic concepts that could help simplify and solve this multilevel linked list problem:

  • Tree traversal algorithms - Structure shares similarities with traversing tree hierarchies.

  • Recursive depth-first search - Recursively traversing nested child pointers is naturally recursive.

  • Pointer manipulation - Adjusting next/previous pointers when restructuring is fundamental concept.

  • In-place algorithms - Manipulating data structures safely without copies.

  • Linked list operations - Fundamental techniques like splicing, merging apply.

  • Graph theory - Could model nodes and connections as graphs to flatten.

  • Dynamic programming - Build up flattened list from sublists bottom up.

  • Divide and conquer - Flatten left and right subtrees then combine.

In particular, tree traversal techniques, recursion, in-place pointer manipulation, and linked list operations seem like potentially useful techniques that align with the problem constraints and structure. Graph algorithms could also model the connections and hierarchy.

Here is how I would explain and approach solving this multilevel linked list problem:

Simple Explanation

Let’s imagine a scavenger hunt where you follow clues leading to other clues. Some clues contain pointers directing you to openings in the ground leading to tunnels with more clues.

The starting clue is like the head node of the linked list. Following next pointers is like moving between clues on the surface. Going down tunnels from child pointers is like traversing levels.

We want to flatten the structure so all clues are on the surface. This is like collapsing all the tunnels so the clues can be followed without going underground.

Problem Breakdown

  1. Start at the head node and follow next pointers to traverse the top level

  2. When reach a node with child pointer, recursively follow it to traverse nested levels

  3. Keep track of nodes just before and after the child node

  4. Reposition the child nodes between the before and after nodes

  5. Update the before and after pointers to link around child nodes

  6. Repeat recursively until all nodes flattened

For example, given nodes A->B->C->D where B has child X->Y:

  1. Start at A, follow to B

  2. Child pointer detected at B, follow to traverse X->Y

  3. Track nodes before and after B: A and C

  4. Reposition X->Y between A and C

  5. Update pointers: A->X->Y->C

This incrementally flattens the nested structure by bringing child nodes to the top level and carefully rewiring connections.

Inference of Problem-Solving Approach from the Problem Statement

Here are the key terms and how they guide the solution approach:

Linked list - Chain of nodes with pointer connections guides traversing sequentially.

Child pointer - Indicates nested structure, informs recursive traversal.

Multilevel - Nested layers imply recursive traversal depth-first.

Flatten - Restructuring shape informs need to manipulate pointers between nodes.

In-place - Modifying original structure itself guides directly updating pointers.

Doubly linked - Bidirectional links help maintain connectivity when rearranging nodes.

Tree traversal - Hierarchical structure suggests recursion and tree traversal techniques.

Pointer manipulation - Need to rearrange next/previous pointers when moving nodes.

The core terms like child pointers, multilevel, flatten, in-place, and bidirectional linkage indicate traversing the nested shape recursively while directly manipulating pointers to reshape the structure. Tree traversal and pointer operations are key to safely rewiring connections.

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?

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 following piece of complex software code.

  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.