All Possible Full Binary Trees
Identifying Problem Isomorphism
“All Possible Full Binary Trees” can be mapped to “Unique Binary Search Trees II”.
Here’s the reasoning:
Both problems deal with the construction of all possible trees given certain conditions. In “All Possible Full Binary Trees”, you need to generate all possible full binary trees given a number ‘N’ where ‘N’ is the total number of nodes and each node is either a leaf or possesses exactly two children. On the other hand, “Unique Binary Search Trees II” asks to generate all structurally unique BST’s (binary search trees) that store values 1 to ’n’.
The problems are not identical as they deal with different kinds of trees (full binary trees vs binary search trees), but they share the same fundamental operation of generating all possible tree structures under given constraints. This operation requires a depthfirst search and recursive approach in both problems.
Both problems have similar complexities, but “Unique Binary Search Trees II” might be considered slightly simpler as it deals with binary search trees (where the left child node is always smaller and the right child node is always larger), while “All Possible Full Binary Trees” involves more generalized full binary trees. The basis of isomorphism lies in their shared requirement to generate all possible trees given specific constraints.


Language Agnostic Coding Drills
 Basic variable assignments: Assigning basic integer values to variables.
 Understanding the data structure: Here, it’s a tree.
 Understanding of a class or object (in this case, TreeNode): How to access its properties and methods.
 Basic arithmetic operations: Here, it’s mainly addition and subtraction.
 Use of math functions: Use of infinite (
inf
) and basic operations with it.  Use of a list or array data structure: Understanding how to initialize, add elements to, and retrieve elements from a list.
 List comprehensions: A concise way to create lists based on existing lists.
 Tuples: Understanding the use of tuples, here it’s used to store pair of two values (
ans
).  Conditional statements: Understanding how
if
conditions work.  Loops: Specifically the use of
while
loops.  Understanding
None
: In Python,None
is often used to represent the absence of a value, here it’s used to check the existence of child nodes.  Use of Queue: Here, it’s used for levelwise traversal of tree.
 Use of
max
function: To get the maximum value.  Functions and methods: Creating functions or methods, how to return a value from them.
 Anonymous functions or lambdas: Used in the
sum
function for getting the sum of node values.  Negation operation: Understanding how the negative (

) operator works.
Arranged in the order of increasing difficulty, these topics cover the concepts used in this code snippet.
Targeted Drills in Python
 Basic variable assignments:


 Understanding of a class or object:


 Basic arithmetic operations:


 Use of math functions:


 Use of a list or array data structure:


 List comprehensions:


 Tuples:


 Conditional statements:


 Loops:


 Understanding
None
:


 Use of
max
function:


 Functions and methods:


 Anonymous functions or lambdas:


 Negation operation:


Each of these drills focuses on a single concept, and can be combined to create more complex programs similar to the provided code snippet.
10 Prerequisite LeetCode Problems
The problem of generating all possible full binary trees involves a lot of recursion and dynamic programming. Here are 10 problems that will help you prepare for this:
LeetCode 100: Same Tree
This is a basic problem for understanding binary tree traversal using recursion.LeetCode 104: Maximum Depth of Binary Tree
This problem helps understand how to use recursion to traverse the tree and calculate the maximum depth.LeetCode 105: Construct Binary Tree from Preorder and Inorder Traversal
A more complicated tree construction problem that will help understand how different traversals can determine a tree’s structure.LeetCode 106: Construct Binary Tree from Inorder and Postorder Traversal
Similar to the previous problem, but uses a different pair of traversals.LeetCode 108: Convert Sorted Array to Binary Search Tree
This problem involves constructing a balanced binary tree from a sorted array.LeetCode 95: Unique Binary Search Trees II
This problem is very similar to the problem in question and involves generating all possible binary search trees.LeetCode 96: Unique Binary Search Trees
A dynamic programming problem that calculates the number of unique BSTs.LeetCode 337: House Robber III
This problem involves dynamic programming on trees.LeetCode 124: Binary Tree Maximum Path Sum
This problem involves recursion and calculating path sums.LeetCode 199: Binary Tree Right Side View
This problem is about viewing the binary tree from a different perspective and is a good exercise in recursion and tree traversal.
These cover a range of operations involving binary trees, recursion and dynamic programming which will be useful in understanding how to approach and solve the given problem.
Clarification Questions
 Is the given number ’n’ always an odd number? A full binary tree always contains an odd number of nodes because each node has either no child (0) or two children.
 How should we represent the tree in the list format? In other words, what are the rules for null placeholders for missing children? For example, should we continue appending nulls for all absent descendants or just for the immediate missing children?
 If there are multiple correct answers, can they be returned in any order?
 What should the function return if ’n’ is equal to 1?
 Will we always have valid inputs, or should we handle cases where ’n’ might be out of the range [1,20] or not an integer?
 Is there a time or space complexity requirement that we should be aiming for?
Problem Analysis and Key Insights
Binary Tree Structure: One of the main insights is the understanding of a full binary tree, where each node has either 0 or 2 children. This particular structure allows us to deduce that a full binary tree always has an odd number of nodes.
Recursive Nature: This problem has a recursive nature. We can generate full binary trees of ’n’ nodes by combining smaller full binary trees.
Odd Nodes: The total number of nodes in a full binary tree is always odd. This is due to the property of each node having either 0 or 2 children.
Enumerating Possibilities: The task is to generate all possible full binary trees, which suggests an enumeration of possibilities. The problem, therefore, may have an exponential time complexity in the worst case.
Output Representation: The binary trees are represented as a list of values in the output, using null to indicate no child. This provides an easy, linear representation of a tree structure.
Key Takeaways:
 Full binary trees can only be built with an odd number of nodes.
 The problem has a recursive structure and solution.
 The output requires a listbased representation of a binary tree.
Problem Boundary
The scope of this problem is around understanding, generating, and working with binary trees, specifically full binary trees. It involves the following concepts:
Understanding the properties of full binary trees: This problem requires a deep understanding of what full binary trees are and how they work. Each node in a full binary tree has exactly 0 or 2 children, meaning each internal node contributes two more nodes to the tree.
Generating binary trees: The problem involves generating all possible full binary trees given a number ’n’ that indicates the total nodes in the tree. This is a complex task that may involve recursion, as smaller trees can be combined to create larger ones.
Representing trees as lists: In order to return the generated trees, the problem requires transforming the binary tree structure into a list format. This is a common method for representing trees in a way that can be easily understood and communicated.
Combinatorial generation: Since the task involves generating all possible full binary trees, it inherently involves the generation of combinations that satisfy a certain property.
Handling constraints: The problem also requires handling of constraints like ’n’ being between 1 and 20 and ’n’ being an odd number (since full binary trees only have odd nodes).
Efficiency considerations: Although not explicitly stated in the problem, efficiency in terms of time and space complexity might be an important consideration, as the problem could involve a large number of recursive calls for larger ’n’.
The boundary of the problem is established based on the following factors:
Input Space: The input is a single integer ’n’ that represents the number of nodes in the binary tree. The problem constraints specify that ’n’ is between 1 and 20.
Output Space: The output is a list of lists, where each list represents one possible full binary tree with ’n’ nodes. Each node in the binary tree is represented by a 0 and missing nodes are represented by ’null'.
Full Binary Trees: The problem specifically deals with full binary trees where each node has either 0 or 2 children. This excludes binary trees where a node can have only one child.
Order of Output: The problem statement mentions that the output can be in any order. This implies that there is no need to sort or order the binary trees in the output list.
No Invalid Inputs: The problem statement does not specify what to do in the case of invalid inputs. For the boundary of this problem, we’ll assume that the input is always valid (i.e., an odd integer within the range [1,20]).
No External Resources: The problem does not involve any external resources or dependencies, such as databases or file systems. The solution is selfcontained and only depends on the given input.
No User Interaction: The problem is solved in a single function call and does not involve multiple steps or user interaction.
No Specified Time or Space Constraints: There is no explicit requirement for the solution to meet a certain time or space complexity. However, due to the nature of the problem, an efficient solution would be preferable, as the number of possible trees can grow rapidly for larger ’n'.
Problem Classification
The problem falls under the binary trees.
What Components:
 Input: An integer ’n’ that signifies the number of nodes in the binary tree.
 Output: A list of all possible full binary trees with ’n’ nodes where each node has Node.val == 0. The structure of each tree is represented as a list where ’null’ indicates no child node.
 Constraints: The number of nodes ’n’ is within the range [1,20]. A full binary tree is a binary tree where each node has either no child (0) or two children.
This problem is a “Combinatorial Generation” problem. The task is to generate all possible combinations that satisfy a certain property, i.e., the formation of full binary trees with a given number of nodes. This typically involves recursive or iterative construction of trees, adhering to the rule that each node has either 0 or 2 children. It’s also worth noting that there’s an element of “Graph Theory” in this problem as binary trees are a specific type of graph.
Distilling the Problem to Its Core Elements
The problem is based on the concept of “Binary Trees” and “Combinatorial Generation”. It leverages the structure of full binary trees, where each node has either 0 or 2 children, to generate all possible configurations.
Think of it like assembling a jigsaw puzzle. You have ’n’ puzzle pieces (where ’n’ is an odd number). These puzzle pieces can be assembled in different ways to form distinct full trees. A full tree is one where every piece (node) either has no connected pieces or is connected to two other pieces. Your task is to find out all the distinct ways these puzzle pieces can be assembled to form full trees.
We are trying to generate all possible full binary trees with ’n’ nodes where each node has the value of 0.
Simplified Problem Statement: Find all possible structures of full binary trees (trees where each node has either 0 or 2 children) given ’n’ nodes.
Key Components:
 Understanding what a full binary tree is.
 Creating a full binary tree.
 Enumerating all possible full binary trees.
Minimal Set of Operations:
 Initialize an empty list to store the possible trees.
 Check if the number of nodes ’n’ is 1. If so, create a new node, add it to the list, and return the list.
 Iterate from ‘i’ = 1 to ’n’  2 with a step of 2. This ‘i’ will represent the number of nodes in the left subtree.
 Recursively generate all possible left subtrees with ‘i’ nodes and right subtrees with ’n’  1  ‘i’ nodes.
 For each pair of left and right subtrees, create a new root node and connect the root to the left and right subtrees. Add the new tree to the list.
 Return the list of all possible full binary trees.
Visual Model of the Problem
Visualizing this problem is about understanding how full binary trees are formed. A full binary tree is a binary tree where each node has exactly 0 or 2 children.
Let’s consider n=3 as a simple case. We have three nodes and need to form all possible full binary trees. The only way to form a full binary tree in this case is:
0
/ \
0 0
This can be represented as [0,0,0] in list format.
Now, let’s consider a slightly complex case where n=7. We can visualize the generation of possible trees as follows:
 Using 5 nodes for left subtree and 1 for the right:
0
/ \
/ \
0 0
/ \ / \
0 0 0 0
/ \
0 0
This can be represented as [0,0,0,null,null,0,0,null,null,0,0] in list format.
 Using 3 nodes for the left subtree and 3 for the right:
0
/ \
/ \
0 0
/ \ / \
0 0 0 0
This can be represented as [0,0,0,null,null,0,0] in list format.
 Using 1 node for the left subtree and 5 for the right:
0
/ \
/ \
0 0
/ \
0 0
/ \
0 0
/ \
0 0
This can be represented as [0,0,0,0,0,null,null,null,null,0,0] in list format.
And so on for other configurations…
This visualization provides a clear idea of how different combinations of left and right subtrees can create various full binary trees for a given number of nodes.
Problem Restatement
We’re given an odd number ’n’. This number represents the total count of nodes we have to work with. Each node has a value of 0. Our task is to create all the distinct full binary trees that can be formed with ’n’ nodes.
A full binary tree is a type of binary tree where each node has either no children or two children. In other words, a node in a full binary tree can’t have just one child.
Once we have created these trees, we need to return them. However, we don’t return them as typical tree data structures. Instead, we represent each tree as a list, where each element of the list is a node’s value, and the position of the element in the list indicates its position in the tree. A ’null’ value signifies a nonexistent child node. We return a list of these tree representations.
Also, the order in which we return these trees doesn’t matter. There are no constraints on time or space complexity, but ’n’ is guaranteed to be between 1 and 20.
So, in essence, we are tasked with creating all possible combinations of full binary trees using ’n’ nodes, representing each tree as a list, and returning all these representations.
Abstract Representation of the Problem
This problem is essentially a combinatorial problem that involves generating and exploring a search space of possibilities, within the structure of a binary tree.
Let’s create an abstract representation:
Let’s represent our problem as function F(n)
where ’n’ is the number of nodes we have to use. The output of this function is a set of all possible binary tree structures that can be formed using ’n’ nodes.
In our context, a binary tree is defined as a structure T
, which is either null (no tree) or a root node with a left subtree T1
and a right subtree T2
. Both T1
and T2
are also binary trees. A binary tree is called “full” if every node in the tree has either 0 or 2 children.
The function F(n)
must generate all the full binary trees with ’n’ nodes. It can do this by recursively combining smaller binary trees. For each i
from 1 to ’n’  2 with a step of 2 (to ensure the total number of nodes remains odd), F(n)
generates all combinations of full binary trees with i
nodes (let’s call this set L
) and ’n’  1  i
nodes (let’s call this set R
).
Each pair of trees (l, r)
where l
is from L
and r
is from R
, forms a new full binary tree where l
is the left subtree, r
is the right subtree, and a new node is added as the root. This process generates all full binary trees with ’n’ nodes.
The abstract problem, therefore, is to implement the function F(n)
that explores this combinatorial space using recursive generation and outputs a set of binary tree structures.
Terminology
Node: In the context of this problem, a node refers to the basic unit of a binary tree. It has a value and can have links to other nodes, generally referred to as child nodes.
Binary Tree: A binary tree is a tree data structure where each node has at most two children, referred to as the left child and the right child.
Full Binary Tree: A full binary tree is a special type of binary tree where each node has either zero or two children. This concept is crucial because the problem requires us to generate full binary trees.
Recursion: Recursion is a programming concept where a function calls itself to solve smaller instances of the same problem. It is essential in this problem because the process of generating all full binary trees can be naturally defined recursively.
Combinatorial Generation: This refers to the process of generating all possible arrangements (combinations) of a set of elements. In this problem, we are generating all combinations of full binary trees with a certain number of nodes.
Null: In this context, ’null’ is used to represent a nonexistent node in the binary tree. In the list representation of binary trees used in this problem, ’null’ signifies a missing child node.
Tree Traversal: Tree traversal is the process of visiting (checking or updating) each node in a tree data structure. In the context of this problem, it is essential to traverse the tree when constructing the list representation of the binary tree.
Root: The root of a tree is the node with no parents. It is the node from where every other node descends. In this problem, every new tree generated begins with a new root node.
Problem Simplification and Explanation
Let’s think about this problem in terms of a game of building blocks.
You are given ’n’ blocks. These blocks come in sets of three: one block serves as a parent, and the other two as its children. You can’t use a single block alone  you either use three together (forming a parentchildren set), or none at all. This is akin to the concept of a full binary tree, where each node has either no children or two children.
Your task is to use all ’n’ blocks to build different structures (binary trees). The rules are:
 Each structure must start with a single block (the root node).
 You can attach two blocks to any existing block (creating a new set of parent and children).
 You can’t attach only one block to an existing block.
 Each block can only be used once across all structures.
The game’s goal is to come up with all possible different structures you can build with the given ’n’ blocks.
In this game, the building process mirrors the concept of recursion in the problem. You start with one block and keep adding more to the existing structure, creating more complex structures step by step. The way you can use three blocks to create a parentchildren set is analogous to the full binary tree concept in the problem.
Moreover, the need to create all possible structures reflects the combinatorial aspect of the problem  generating all possible full binary trees with ’n’ nodes.
The resulting structures, created using your blocks, represent the different binary trees you can create with ’n’ nodes, where each node (block) either has no children or two children.
Constraints
There are a few characteristics of this problem that can be used to our advantage:
Odd number of nodes: The problem statement specifies that ’n’ is an odd number. This aligns perfectly with the nature of full binary trees, where each node has either 0 or 2 children. This means we always have a root node (1 node) and the remaining nodes can be split into two groups, potentially forming left and right subtrees. It makes it easier to iterate from 1 to ’n’  2 with a step of 2 while generating subtrees.
Combinatorial nature: The problem of generating all full binary trees with ’n’ nodes naturally breaks down into generating smaller trees first. This allows for a recursive solution, where larger trees are built by combining smaller trees. This divideandconquer approach simplifies the problem.
Full binary trees: The problem is about generating full binary trees, which are trees where each node has either 0 or 2 children. This constraint eliminates the need to consider trees with nodes having only one child, effectively reducing the search space.
Memoization opportunity: Due to the recursive nature of the problem, there will be cases where we compute the possible trees of a certain size more than once. By storing (memoizing) the result for each ’n’, we can avoid redundant computation, improving the efficiency of our solution.
Limited range of ’n’: The problem statement specifies that ’n’ is between 1 and 20. This relatively small range of ’n’ ensures that our solution doesn’t need to handle very large inputs. It also makes the problem manageable from a computational point of view.
From analyzing the constraints, we can derive the following key insights:
Number of Nodes is Always Odd: Since ’n’ is always an odd number, it confirms our understanding that in a full binary tree, each node has either 0 or 2 children. The root node of the tree is always one of the nodes, leaving an even number of nodes that can be split into two groups to form the left and right subtrees.
Recursion is a Viable Approach: The problem essentially asks for all possible combinations of full binary trees with ’n’ nodes. As this problem has a combinatorial nature and involves the concept of tree structures, a recursive approach is suitable. We will need to explore all the different ways to split the ’n’ nodes into two groups (left and right subtrees) recursively.
Memoization Can Improve Efficiency: Because we’re dealing with a problem that involves recursion over a set of nodes, there will be overlapping subproblems. For example, while generating trees with ’n’ nodes, we might need to generate trees with ’n2’ nodes multiple times. Using memoization to store and retrieve the results of previous computations can significantly reduce the total amount of computation and thus increase efficiency.
No Large Inputs: Given that ’n’ is between 1 and 20, we know that we won’t need to handle large inputs. This constraint makes it feasible to explore all possible combinations of full binary trees with ’n’ nodes.
Output Format: The problem statement specifies that the output is a list of tree root nodes. The trees are represented in a particular way, with each tree expressed as a list. Understanding this output format is crucial for correctly implementing the solution.
Case Analysis
Let’s consider the following test cases:
Minimum Nodes Test (Edge Case): Input: n = 1 Output: [[0]]
Analysis: This case represents the smallest possible full binary tree, consisting of a single node. Since ’n’ equals 1, we only have one node, and it must be a full binary tree by itself.
Small Number of Nodes Test (Typical Case): Input: n = 3 Output: [[0,0,0]]
Analysis: This case represents the smallest nontrivial full binary tree. A full binary tree with three nodes must have a root node with two child nodes, which forms a single possible structure.
Multiple Possibilities Test (Typical Case): Input: n = 5 Output: [[0,0,0,null,null,0,0],[0,0,0,0,0]]
Analysis: This case starts to introduce the concept of multiple possible structures for a given ’n’. With five nodes, we can either make the root node the parent of two 1node trees or make it the parent of a 3node tree and a 1node tree.
Large Number of Nodes Test (Stress Test): Input: n = 7 Output: Multiple possible structures.
Analysis: With a larger number of nodes, the number of possible structures grows. This test is useful to verify the performance of the solution and ensure it can handle larger ’n’ values within the provided constraints.
Maximum Nodes Test (Edge Case): Input: n = 19 (since n can’t be 20 because n is always an odd number in full binary trees) Output: Multiple possible structures.
Analysis: This is an edge case representing the maximum possible ’n’ within the problem constraints. This case is useful for testing the upper limits of the solution’s performance. It also helps verify that the solution correctly handles the case where ’n’ is at its maximum.
The output for each of the test cases is a list of root nodes of all possible full binary trees, represented as nested lists where each list is a tree with nodes represented in levelorder. The actual order of trees in the output list does not matter, as the problem statement allows for any order. The most important aspect is that all unique tree structures are represented.
Let’s visualize the test cases through tree diagrams:
Minimum Nodes Test (Edge Case): For n = 1, we have one node, which stands alone as a full binary tree.
Diagram:
0
Small Number of Nodes Test (Typical Case): For n = 3, we have a root node with two child nodes.
Diagram:
0 / \
0 0
3. **Multiple Possibilities Test (Typical Case)**: For n = 5, we have two possible structures. One where the root node has two child nodes which themselves are roots of 1node trees, and another where the root node has one child that is the root of a 3node tree and one child that is a 1node tree.
Diagrams:
First structure:
0
/
0 0
/ \
0 0
Second structure:
0
/
0 0
/
0 0
4. **Large Number of Nodes Test (Stress Test)**: For n = 7, we have more possible structures. Due to the number of possibilities, providing diagrams for all would be impractical, but here is one example structure:
Diagram (one of many possible structures):
0
/
0 0
/ \
0 0 0
/
0 0
5. **Maximum Nodes Test (Edge Case)**: For n = 19, there are many possible tree structures, so we won't provide specific diagrams here. This case is more about testing the upper limit of the solution.
Each 0 in the diagrams represents a node with Node.val == 0, as per the problem statement. The connections between nodes represent the parentchild relationships in the tree. Remember, the number of unique tree structures grows rapidly as 'n' increases, so for larger 'n' values, visualizing all possible tree structures would be quite complex.
Analyzing different cases can give us several key insights:
1. **Odd Number of Nodes**: From all our test cases, we can observe that an odd number of nodes is essential to form full binary trees. This is because in a full binary tree, each node has either 0 or 2 children. This ensures that every subtree (including the whole tree) always has an odd number of nodes.
2. **Increasing Complexity with 'n'**: As the number of nodes 'n' increases, the number of possible full binary trees also increases. This demonstrates the combinatorial nature of the problem.
3. **Recursive Structure**: Each full binary tree can be split into a root node and two subtrees. This suggests a recursive approach to the problem, where we can construct larger trees by combining smaller trees.
4. **Memoization Opportunity**: Since the same size of subtrees can be used multiple times in different trees, we have an opportunity to use memoization to avoid recalculating the same size subtrees repeatedly.
5. **Stress Test Scenarios**: The test cases with larger 'n' values serve as stress tests. They highlight the need for an efficient solution to handle larger inputs within the given constraints. If we were to attempt a solution that doesn't take advantage of the recursive nature of the problem and the memoization opportunity, it would struggle with these larger test cases.
These insights are crucial in formulating an efficient strategy to solve this problem.
## Identification of Applicable Theoretical Concepts
This problem statement draws on several key algorithmic concepts:
1. **Recursion**: Given the hierarchical nature of a binary tree, recursion is a natural fit for solving this problem. A recursive approach is often used when a problem can be divided into smaller, simpler subproblems of the same type. In this case, the problem of generating all possible full binary trees with 'n' nodes can be divided into subproblems of generating all possible full binary trees with fewer nodes. We construct larger trees by combining smaller trees in all possible ways.
2. **Dynamic Programming**: The problem has an overlapping subproblems property, which is a key trait that suggests the usefulness of dynamic programming. In this case, we're repeatedly solving the same subproblems when we calculate all possible trees for different 'n'. By storing the results of these subproblems in a data structure (a process known as memoization), we can avoid redundant work and improve efficiency.
3. **Combinatorial Generation**: This problem is essentially asking for all possible combinations of full binary trees with 'n' nodes. Generating all possible combinations of a set of elements is a common task in combinatorics, a field of mathematics. A combinatorial approach can be applied to generate all possible trees by iterating over all possible ways to split 'n' nodes into two groups for the left and right subtrees.
By understanding and applying these concepts, we can devise a more effective and efficient solution to the problem. We can simplify the problem by leveraging the properties of recursion, dynamic programming, and combinatorics to systematically generate all possible full binary trees with 'n' nodes.
## Simple Explanation
Let's imagine you're playing with blocks. Each block represents a "node". Now, you have a special rule for how you can stack these blocks. You can either have a single block stand alone, or you must have one block with two other blocks stacked directly underneath it, forming a sort of pyramid. You can't have a block with only one other block stacked directly beneath it.
Now, suppose I give you an odd number of blocks (say, 5 or 7) and ask you to stack all of them following these rules. You might find out that there are multiple ways to do it.
For example, if I give you 3 blocks, you can only stack them in one way, forming a small pyramid. But if I give you 5 blocks, you can make a larger pyramid with a single block at the top, two in the middle, and two at the bottom, or you could make a small pyramid with two single blocks standing separately.
The problem is essentially asking you to find all the ways you can stack a given number of blocks following the rules. In technical terms, each block represents a node in a binary tree, and the problem is asking for all possible full binary trees with 'n' nodes. The stacking rule represents the condition that each node in a full binary tree must have either 0 or 2 children.
The complexity comes in when you have a larger number of blocks, and you need to find an efficient way to discover all possible stacking configurations.
## Problem Breakdown and Solution Methodology
Let's start by breaking down our approach to solving this problem. Here's a simple plan:
1. **Understand the Problem**: The first step to any problemsolving approach is to understand the problem. Here, we want to generate all possible full binary trees with a given number of nodes.
2. **Check for Base Case**: If the number of nodes 'n' is 1, we know that we can have only one tree  a single node. If 'n' is not an odd number, we know that we cannot create a full binary tree, as each node either stands alone or has two children.
3. **Divide and Conquer Approach**: Now, we adopt a recursive "Divide and Conquer" approach. This means we will solve the problem by breaking it down into smaller subproblems. We do this by iterating from 1 to 'n', assuming the current number 'i' is the root. We then solve the subproblem for 'i1' nodes on the left subtree and 'ni' nodes for the right subtree.
4. **Combine the Results**: The next step is to combine the results from the subproblems to construct the possible trees. For every combination of left and right subtrees, create a new root and attach the left and right subtrees to it.
5. **Store the Results**: Once we've calculated all possible trees for a particular 'n', we store these trees. This is a technique called "memoization", and it helps us avoid recomputing these trees when the same number of nodes 'n' comes up in a different part of the problem.
6. **Return the Result**: Finally, we return the list of all possible trees.
This approach is like creating various family trees where every parent (root of a tree) either has no child (a single node tree) or has two children (two subtrees). The idea is to try out every possible combination of families, given a certain number of family members (nodes).
Now, let's consider how changes in the problem's parameters would affect the solution:
 **Increase in 'n'**: As 'n' increases, the number of possible full binary trees also increases. The problem becomes more complex due to a larger number of combinations. However, our approach efficiently handles this increase by storing the results of subproblems, thus avoiding redundant calculations.
Let's look at an example to illustrate how the approach works. Suppose 'n' is 3.
 There's only one way to create a full binary tree: one root with two children nodes. So we return this single possibility.
Now, let's consider 'n' as 5.
 We start by considering the root to be the first node. That leaves us with '51=4' nodes. As we can't form a full binary tree with an even number of nodes, we move to the next possibility.
 We then consider the root to be the second node. That leaves us with '52=3' nodes. We can form a full binary tree with 3 nodes, and we already have that from our previous calculation when 'n' was 3. We form two trees, one with the 3 nodes on the left and the other with the 3 nodes on the right.
 We continue this process until we've tried every possible position for the root.
We repeat this process for every 'n', always reusing the trees we've calculated for smaller 'n'. This makes our solution more efficient and allows us to handle larger inputs effectively.
## Inference of ProblemSolving Approach from the Problem Statement
Here are the key terms and concepts, and how they inform our approach:
1. **Integer 'n'**: The problem is asking for full binary trees with 'n' nodes. This directly determines our problem space. Depending on the value of 'n', we need to find all possible full binary trees.
2. **Binary Tree**: A binary tree is a treetype nonlinear data structure with a maximum of two children for each parent. Understanding this structure is crucial to the problem. Full binary trees have the property that every node has either 0 or 2 children, which guides how we create these trees.
3. **Recursion**: Recursion is a method where the solution to a problem depends on solutions to smaller instances of the same problem. This problem has a natural recursive structure because we can construct larger trees by combining smaller trees.
4. **Dynamic Programming**: This approach is used for optimization problems and involves solving subproblems, storing those solutions, and using them to solve larger problems. We use dynamic programming here to avoid recomputing the trees for a certain 'n'.
To visualize these concepts, consider drawing a table that lists the number of nodes in one column and the corresponding possible full binary trees in the next column. For instance:
No of Nodes  Possible Full Binary Trees
1  [0] 3  [0,0,0] 5  [0,0,0,null,null,0,0], [0,0,0,0,0]
Also, visualizing the binary tree structure will help. For example, for 'n' equals 5, we can have:
0 0
/ \ / \
0 0 0 0
/ \ / / \
0 0 0 0 0 0
This visual representation can assist you in understanding the various ways of forming full binary trees and how the recursive construction works.
The problem statement gives a hint that dynamic programming might be a good approach when it asks for "all possible" full binary trees with 'n' nodes. Here's why:
1. **Overlapping Subproblems**: In many combinatorial problems where you're asked to find all possible configurations, those configurations can be built up from smaller configurations. For this problem, larger binary trees are composed of smaller binary trees. This gives us a hint that we can solve the problem for smaller 'n' first and use those results to construct solutions for larger 'n'. This is a hallmark of dynamic programming.
2. **Optimal Substructure**: The problem can be broken down into subproblems in such a way that an optimal solution for the larger problem can be constructed efficiently from the solutions to the subproblems. In this case, all possible trees of size 'n' can be constructed from trees of smaller sizes. This property of the problem is another clue that dynamic programming might be an effective strategy.
Remember that dynamic programming is a technique for efficient computation that trades off space complexity to reduce time complexity. It uses memoization to save results of expensive function calls and reuse them when the same inputs occur again. In this problem, storing the results of all possible trees for each 'n' from 1 to 'n' helps to avoid recomputation and thus, makes the solution more efficient.
So, the nature of the problem and these characteristics hint that a dynamic programming approach might be an effective solution method.
## Simple Explanation of the Proof
Let's break down the proof of this algorithm into easytounderstand chunks. To reiterate, our task is to create all possible full binary trees with 'n' nodes. Here's a stepbystep breakdown of the algorithm and proof:
1. **Base Case**: If 'n' is even, we cannot create a full binary tree because every full binary tree has an odd number of nodes (every internal node contributes 3 nodes including itself, and adding nodes means adding two more children to an existing node). So, we return an empty list for even 'n'. If 'n' is 1, we can only create one tree with a single node. So, we return a list with one singlenode tree.
2. **Recursive Case**: For 'n' greater than 1, we initialize an empty list to store all possible full binary trees with 'n' nodes. Then, we iterate 'i' from 1 to 'n', incrementing by 2 each time. Here, 'i' represents the number of nodes in the left subtree. Therefore, 'ni1' will represent the number of nodes in the right subtree (we subtract 1 because one node becomes the root).
3. **Combining Subtrees**: For each 'i', we get all possible left subtrees by making a recursive call with 'i' and all possible right subtrees by making a recursive call with 'ni1'. Then, we create trees for each pair of left and right subtrees by assigning a new node as the root and each pair of left and right subtrees as the children of the root. We add each such tree to our list.
4. **Return Result**: After considering all 'i', we will have generated all possible full binary trees with 'n' nodes. So, we return our list.
Now, why does this work?
 It correctly generates all trees because we consider all possibilities for the number of nodes in the left subtree and for each such possibility, we consider all configurations of left and right subtrees.
 It doesn't generate invalid trees because we only generate trees where every node has 0 or 2 children (which is the definition of full binary trees).
 It doesn't miss any valid trees because we are exhaustively considering all possibilities for the left subtree and for each such possibility, considering all configurations of left and right subtrees.
This is how the algorithm works and why it is correct. The concept of recursion and the properties of binary trees are key to understanding this proof.
## Stepwise Refinement
1. **Stepwise Refinement of Approach**:
 **Step 1**: Initialize a list or similar data structure to hold the output trees.
 **Step 2**: Handle the base cases: If 'n' is even, return an empty list, as full binary trees always have an odd number of nodes. If 'n' equals 1, return a list with a singlenode tree.
 **Step 3**: For odd 'n' greater than 1, iterate over 'i' from 1 to 'n', incrementing by 2 each time. For each 'i', make a recursive call to generate all possible full binary trees with 'i' nodes (the left subtrees), and another recursive call to generate all possible full binary trees with 'ni1' nodes (the right subtrees).
 **Step 4**: For each pair of left and right subtrees, create a new tree with a new node as root and each pair of left and right subtrees as its children. Add each such tree to the list.
 **Step 5**: Return the list of all full binary trees with 'n' nodes.
2. **Granular Steps**: The most granular, actionable steps would be the operations inside the forloop (Steps 3 and 4), which include the recursive calls and the creation of new trees. This is where the actual tree generation happens.
3. **Independently Solvable Parts**: The generation of all left subtrees and right subtrees can be done independently for each 'i'. Once you have these, you can combine them independently to create the final trees. So the algorithm has a divideandconquer nature.
4. **Repeatable Patterns**: The repeatable pattern in our solution is the recursive generation of subtrees and their combination. For every 'i', we generate all possible left and right subtrees, then combine them to form new trees. This pattern repeats until we've tried all possible numbers of nodes for the left subtrees.
## Solution Approach and Analysis
Let's break down the solution to this problem:
1. **Understanding the Problem**: We need to generate all possible full binary trees with 'n' nodes. A full binary tree is a tree where each node has exactly 0 or 2 children. Imagine a series of interconnected buckets. Each bucket (node) either holds two other buckets (children nodes) or it doesn't hold any.
2. **Handling Base Cases**: If 'n' is even, we cannot make a full binary tree because every full binary tree must have an odd number of nodes. So, we return an empty list. If 'n' is 1, the only possible full binary tree is a tree with a single node. So, we return a list containing this singlenode tree.
3. **Recursive Approach**: If 'n' is greater than 1 and odd, we consider every possibility for the number of nodes in the left subtree, which we denote as 'i'. For each 'i', we recursively find all possible left subtrees with 'i' nodes and all possible right subtrees with 'ni1' nodes. Imagine that you have a bunch of Lego blocks, and you're trying to see all the different ways you can attach a certain number of blocks to the left of a central block and the remaining blocks to the right.
4. **Combining Subtrees**: Now, for each pair of left and right subtrees, we create a new tree by assigning a new node as the root and each pair of left and right subtrees as the children of the root. We add each such tree to our list. This is like joining two Lego constructions at a central block to create a bigger construction.
5. **Return the Result**: We return our list, which now contains all possible full binary trees with 'n' nodes.
6. **Effect of Changing Parameters**: Increasing 'n' means we will generate more trees, and the trees will be larger. If 'n' becomes even, we return an empty list. So, the operation's complexity increases with 'n'.
Let's see an example: n=3
 When n=3, we have only one possible value of 'i' which is 1. So we generate all full binary trees with 1 node, which is just a singlenode tree.
 Then we create new trees with a new node as the root, and each pair of left and right subtrees (which are the same in this case) as the children. So, we get one tree, and we return a list containing this tree.
 This tree is represented as [0,0,0] in the problem's format.
## Identify Invariant
An invariant is a condition that can be relied upon to be true during the execution of a program or during some segment of it. In the case of this problem, the invariant is that "All trees generated at any point are full binary trees".
This invariant holds because the algorithm ensures that every node has either 0 or 2 children, which is the definition of a full binary tree. Therefore, no matter what stage we're at or what subtree we're building, we can trust that we're only dealing with full binary trees.
Another important invariant is "All trees generated have a total number of nodes equal to the 'n' value that we're currently considering". This holds true because the algorithm is designed to create trees with a specific total number of nodes at each step, and the combination of left and right subtrees always results in a total node count of 'n'.
## Identify Recursion Invariant
In this problem, the recursion invariant is a condition that holds before and after each recursive call. Specifically, "For a given number of nodes 'n', the function returns all possible full binary trees with 'n' nodes."
This invariant is maintained through each recursive call in the following manner:
1. Before recursion: When we make a recursive call for 'i' nodes (for the left subtree) and 'ni1' nodes (for the right subtree), we have the invariant that our function will return all possible full binary trees with 'i' and 'ni1' nodes respectively.
2. During recursion: Inside the recursive call, the function builds all possible full binary trees with 'i' and 'ni1' nodes, maintaining the invariant.
3. After recursion: After the recursive call, we have a list of all full binary trees with 'i' nodes and 'ni1' nodes. We then combine these left and right subtrees to generate all possible full binary trees with 'n' nodes. This ensures that our invariant, "For a given number of nodes 'n', the function returns all possible full binary trees with 'n' nodes" is still holding true.
So, the recursion invariant ensures the correctness of our solution throughout the recursion process.
## Unit of Work
The unit of work in every recursive call for this problem is the creation of all possible full binary trees for a specific number of nodes. This is achieved by the following steps:
1. **Iterating Over Possible Left Subtrees**: For a specific number of nodes 'i' (ranging from 1 to 'n' skipping one number each time), we consider it as the number of nodes for the left subtree and recursively generate all possible full binary trees with 'i' nodes.
2. **Generating Right Subtrees**: Simultaneously, we generate all possible right subtrees with 'ni1' nodes.
3. **Creating New Trees**: For every pair of left and right subtrees, we create a new tree with a new node as the root, the left subtree as the left child, and the right subtree as the right child.
4. **Appending New Trees to the List**: We add this newly created tree to our result list, which will contain all possible full binary trees with 'n' nodes.
This is the primary work performed in every recursive call. This unit of work is repeated for every odd number less than 'n', generating all possible trees and leading to the solution.
## Thought Process
Let's start from the problem statement.
1. **Problem Understanding**: We need to generate all possible full binary trees with a specific number of nodes 'n'. Each tree's node has a value of 0. A full binary tree is one where each node has exactly 0 or 2 children.
2. **Insights and Approach**: This problem statement gives a few clues. The requirement to generate "all possible" trees hints at the need for an exhaustive search, which suggests using recursion or backtracking. The nature of binary trees also points towards recursion, as trees are naturally recursive data structures with selfsimilar substructures (subtrees).
3. **Applying Dynamic Programming**: We note that the trees for a specific number of nodes do not depend on the trees with a larger number of nodes. This implies that we can build up the solution from smaller numbers to larger ones  a bottomup dynamic programming approach. Also, the problem has overlapping subproblems: the same number of nodes can be split in different ways for different parent nodes. Dynamic programming can be helpful in storing and reusing these results.
4. **Formulating Recurrence Relation**: We can start with a single node tree. Then, for each odd number up to 'n', we can generate all possible left and right subtrees with a smaller number of nodes and combine them.
Here's the Python code that implements this approach:
```python
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
memo = {0: [], 1: [TreeNode(0)]}
def allPossibleFBT(self, n: int) > List[Optional[TreeNode]]:
if n not in Solution.memo:
ans = []
for x in range(1, n, 2):
for left in self.allPossibleFBT(x):
for right in self.allPossibleFBT(nx1):
node = TreeNode(0)
node.left = left
node.right = right
ans.append(node)
Solution.memo[n] = ans
return Solution.memo[n]
This Python code uses memoization to store and reuse the results of subproblems (calculating all possible full binary trees for a smaller number of nodes) in order to solve the overall problem. This is an example of a dynamic programming approach.
For every odd number less than ’n’, it generates all possible left and right subtrees and combines them under a new node. The results are stored in a memoization dictionary Solution.memo to avoid recomputation for the same ’n’.
The function allPossibleFBT takes the number of nodes ’n’ as input and returns a list of all possible full binary trees with ’n’ nodes.
Establishing Preconditions and Postconditions
Parameters:
 The method
allPossibleFBT
takes one input parameter:n
.  This parameter is of type integer.
 The parameter
n
represents the total number of nodes in the full binary trees.
 The method
Preconditions:
 Before this method is called, there are no strict requirements about the state of the program.
 The input parameter,
n
, should be an integer between 1 and 20 (inclusive). As per the problem constraints,n
must be an odd integer for a full binary tree to exist.  The method assumes that the class
TreeNode
is defined and that an instance ofTreeNode
represents a node in the binary tree.
Method Functionality:
 This method is expected to generate all possible full binary trees with
n
nodes.  It uses the input
n
and the current state of thememo
dictionary to generate the trees.
 This method is expected to generate all possible full binary trees with
Postconditions:
 After the method has been called, the
memo
dictionary is updated with the full binary trees for the inputn
.  The return value is a list of root nodes of all possible full binary trees with
n
nodes. Each node in the tree has a value of 0 and either no children or exactly two children.  As a side effect, the
memo
dictionary is updated to include the result for the inputn
, which can be used in future method calls.
 After the method has been called, the
Error Handling:
 The method does not explicitly handle errors. If the preconditions are not met (for example, if
n
is not an odd integer or is out of the allowed range), it would lead to unexpected results or an error.  The method does not throw an exception or return a special value. Any exceptions would be thrown by the Python interpreter itself, such as a
TypeError
if the input is not an integer.
 The method does not explicitly handle errors. If the preconditions are not met (for example, if
Problem Decomposition
Problem Understanding:
 In this problem, we are given a single integer input
n
. The task is to generate all possible full binary trees withn
nodes, each node having a value of 0 and having either no children or exactly two children.
 In this problem, we are given a single integer input
Initial Breakdown:
 This problem can be broken down into the following subproblems:
 Recognize that full binary trees can only exist if
n
is odd.  Create the trees recursively by dividing the nodes between the left and right subtrees.
 Recognize that full binary trees can only exist if
 This problem can be broken down into the following subproblems:
Subproblem Refinement:
 The second subproblem can be further broken down:
 For all possible divisions of nodes between the left and right subtrees, generate the left and right subtrees recursively.
 Combine the generated left and right subtrees in all possible ways to create the trees for the current
n
.
 The second subproblem can be further broken down:
Task Identification:
 The repeated task in this problem is generating the trees given a certain number of nodes. This is done recursively for all possible divisions of nodes between the left and right subtrees.
Task Abstraction:
 The abstracted task is: Given
n
nodes, generate all full binary trees withn
nodes by combining the trees generated for all possible divisions ofn1
nodes between the left and right subtrees.
 The abstracted task is: Given
Method Naming:
 This task could be named
generate_trees
or similar, which takes an integern
and returns a list of all possible full binary trees withn
nodes.
 This task could be named
Subproblem Interactions:
 The tasks need to be performed in a recursive manner, starting from the smallest possible tree (one node) and building up to the required number of nodes (
n
). The trees generated for smaller numbers of nodes are used to generate the trees for larger numbers of nodes.
 The tasks need to be performed in a recursive manner, starting from the smallest possible tree (one node) and building up to the required number of nodes (
From Brute Force to Optimal Solution
Let’s start by discussing a brute force solution and then we’ll explore how to optimize it.
Brute Force Solution:
A brute force approach would be to generate all possible binary trees and then discard the ones that are not full binary trees. For n
nodes, there are Catalan(n
) possible binary trees. We would then traverse each tree to check if it is a full binary tree (each node has either 0 or 2 children). This solution would be extremely inefficient, with a time complexity that would be much worse than exponential, and it is not feasible for even moderately large inputs.
Inefficiencies:
The main inefficiencies in the brute force solution are:
 Generating all binary trees, not just full binary trees. This creates a lot of unnecessary trees.
 Checking each generated tree to see if it is a full binary tree. This requires traversing each node of each tree, which adds significant time complexity.
Optimized Approach:
To optimize our solution, we can make use of the properties of full binary trees.
Observation: Full binary trees can only have an odd number of nodes. Therefore, if
n
is even, we return an empty list. This allows us to eliminate a large portion of the input space.Dynamic Programming: Instead of generating all binary trees, we can generate only full binary trees. We can start by noting that a full binary tree with 1 node is just a single node. Then, for any odd
n > 1
, we can generate full binary trees by combining smaller full binary trees. If we haven
nodes, we allocate 1 node for the root, and the remainingn1
nodes are split between the left and right subtrees. Since the total number of nodes must be odd, the number of nodes in the subtrees must also be odd. Therefore, we can iteratei
from 1 ton1
with a step of 2, allocatingi
nodes to the left subtree andn1i
nodes to the right subtree. We then combine the trees generated for the left and right subtrees in all possible ways to generate the trees forn
.
This solution has a time complexity of O(4^n / n^1.5) due to the nth Catalan number and a space complexity of O(4^n / n^1.5), which are much more manageable than the brute force approach.
Conclusion:
By making use of the properties of full binary trees and using a dynamic programming approach, we are able to greatly optimize our solution, reducing the time and space complexity to a much more manageable level.
Code Explanation and Design Decisions
Initial Parameters: The initial parameter is an integer
n
, representing the number of nodes in a full binary tree. Full binary trees are binary trees where every node has either 0 or 2 children.n
is significant because it determines the number of unique full binary trees that we can create.Primary Iteration: The primary iteration is over the range of numbers from 1 to
n
, with a step of 2. Each iteration of this loop represents a unique way to divide then
nodes into a root, left subtree, and right subtree. The left subtree getsi
nodes and the right subtree getsn1i
nodes. The root takes 1 node.Conditions and Branches: There aren’t specific conditions or branches within the main loop, but there is a nested loop which iterates over the cartesian product of left and right subtrees. This represents all the ways the left and right subtrees can be combined with the root to form a full binary tree with
n
nodes.Updates or Modifications: In each iteration, we create new full binary trees by combining a root with left and right subtrees. These new trees are then added to our list of full binary trees for
n
nodes.Invariant: The invariant in this problem is the list of full binary trees for a given number of nodes. At the end of each main loop iteration, this list contains all full binary trees with
i
nodes. This invariant is important because it ensures we generate all possible full binary trees forn
nodes.Final Output: The final output is a list of root nodes of all full binary trees with
n
nodes. Each root node represents a unique full binary tree. This output satisfies the problem’s requirements by generating all possible full binary trees withn
nodes.
The focus of the code is to leverage the properties of full binary trees and the principle of dynamic programming to efficiently generate all possible full binary trees for a given number of nodes. The result provides the roots of these trees, serving as a comprehensive list of possible full binary tree configurations.
Coding Constructs
HighLevel Strategies: This code is utilizing a divideandconquer strategy coupled with dynamic programming. Divideandconquer is used to split the problem of generating a full binary tree into smaller subproblems of generating left and right subtrees. Dynamic programming is then used to store and reuse the solutions to these subproblems, thereby improving efficiency.
Explaining to a NonProgrammer: Think of this code like a tree nursery that grows all possible unique shapes of full binary trees, given a specific number of nodes. A full binary tree is one where each “parent” node has two “children” nodes or none at all. The code is like a gardener, carefully deciding how to best split the available nodes between left and right branches at every level to grow all possible trees.
Logical Elements: The key logical elements in this code include iteration (repeatedly looping over a sequence of numbers), branching (performing different actions based on different conditions), and recursion (solving a problem by breaking it down into smaller versions of the same problem).
Algorithmic Approach in Plain English: Start with a certain number of nodes. For each possible split of nodes between a left and right subtree (keeping in mind that each parent node needs one node), calculate all possible full binary trees for the left subtree and the right subtree. Combine these subtrees in all possible ways to create all full binary trees with the given number of nodes.
Key Steps or Operations: The main operation this code is performing is the construction of full binary trees by combining a root node with every possible pair of left and right subtrees. This operation is performed for each valid way of splitting the available nodes between a left and right subtree.
Algorithmic Patterns: The algorithmic pattern here is dynamic programming. We are solving the problem for smaller numbers of nodes first, and storing those solutions. Then, for larger numbers of nodes, we reuse the previously calculated solutions to avoid redundant computations, thereby saving time and computational resources.
Language Agnostic Coding Drills
Concept Identification:
Here are some fundamental concepts embedded in the code:
Variables: Variables are a fundamental concept where you create symbolic names to represent different values or data. Variables can be used to hold different data types, including integers, strings, objects, etc.
Loops: Loops are a way to repeatedly execute a block of code.
Conditional Statements: Conditionals are used to decide which block of code to execute based on whether a certain condition is true or false.
Functions: Functions are reusable blocks of code that perform a specific task.
Recursion: Recursion is a concept where a function calls itself to solve a smaller version of the same problem.
List Comprehensions: This is a Pythonspecific concept, but similar ideas exist in other languages too. It is a concise way to create lists based on existing lists.
Dynamic Programming: Dynamic programming is a technique used to solve complex problems by breaking them down into simpler subproblems, solving each of those just once, and storing their solutions.
Increasing Difficulty:
Variables: Easy. Variables are one of the first concepts taught in programming.
Conditional Statements: Easy. This comes just after variables in learning difficulty.
Loops: EasyModerate. Loops can get a bit complex when you deal with nested loops and controlling the flow of iteration.
Functions: Moderate. Writing good functions requires a decent understanding of how to structure code.
List Comprehensions: Moderate. This concept could be tricky for beginners due to its syntax.
Recursion: ModerateHard. Recursion can be a challenging concept to understand, especially for complex problems.
Dynamic Programming: Hard. This technique involves a higher level of problemsolving and often requires a solid understanding of recursion.
ProblemSolving Approach:
Understand the problem: Read the problem carefully and understand what it is asking.
Identify the problem type: Notice that the problem is asking for “all possible” full binary trees, which suggests a combinatorial problem that may involve recursion or dynamic programming.
Develop a recursive relationship: A full binary tree with
N
nodes can be broken down into a root node, and two subtrees with a total ofN1
nodes. Further, theseN1
nodes can be divided in all possible ways between the two subtrees. Formulate this relationship in your mind.Translate the recursive relationship into code: Now you know how to create all possible full binary trees, you can write a function that does this. This function should take the number of nodes
N
as input, and output a list of all possible full binary trees.Optimize with dynamic programming: You realize that while creating all full binary trees, you are doing repeated work. For example, you might be calculating all full binary trees with 3 nodes multiple times. To avoid this, you can store the result the first time you calculate it, and simply reuse it later. This is the concept of dynamic programming.
Test your solution: Once your code is written, test it on various test cases to make sure it’s working as expected. Be sure to include edge cases to test the robustness of your solution.
Remember, the goal here is to translate your thought process into code, stepbystep. Each of these concepts or “drills” contributes a different aspect to the final solution. Understanding these individual components will help you grasp the overall solution more effectively.
Targeted Drills in Python
Variables
This is a simple example of defining and using a variable in Python.
1 2
greeting = "Hello, World!" print(greeting)
Conditional Statements
This piece of code uses an
ifelse
statement to print different messages based on the value of a variable.1 2 3 4 5
number = 10 if number > 0: print("Positive number") else: print("Nonpositive number")
Loops
This is an example of a
for
loop that iterates over a list of numbers and prints each number.1 2 3
numbers = [1, 2, 3, 4, 5] for num in numbers: print(num)
Functions
This code defines a simple function that takes two numbers and returns their sum.
1 2 3
def add_numbers(num1, num2): return num1 + num2 print(add_numbers(3, 4))
List Comprehensions
This drill shows how to create a new list where each element is double the corresponding element in the original list.
1 2 3
numbers = [1, 2, 3, 4, 5] doubled = [num * 2 for num in numbers] print(doubled)
Recursion
This is a recursive function that calculates the factorial of a number.
1 2 3 4 5 6
def factorial(n): if n == 0: return 1 else: return n * factorial(n1) print(factorial(5))
Dynamic Programming
This drill shows how to calculate Fibonacci numbers using dynamic programming to avoid repeated calculations.
1 2 3 4 5 6 7 8 9 10
def fibonacci(n, memo = {}): if n in memo: return memo[n] elif n <= 2: result = 1 else: result = fibonacci(n1, memo) + fibonacci(n2, memo) memo[n] = result return result print(fibonacci(10))
To solve the original problem of creating all possible full binary trees, you would need to integrate these drills together:
 Define the problem and inputs using variables.
 Write a recursive function that creates all possible full binary trees for a given number of nodes. This would require using an
if
statement to handle the base case (when the number of nodes is 1), and afor
loop to divide the remaining nodes between the left and right subtrees.  Use a list comprehension to create all combinations of left and right subtrees.
 Optimize your solution using dynamic programming, by storing the result for each number of nodes in a dictionary and reusing it to avoid repeated calculations.
 Finally, test your solution using a variety of test cases to ensure it works as expected.
Q&A
Similar Problems
Here are 10 problems that use similar underlying concepts:
LeetCode 96. Unique Binary Search Trees: This problem requires counting the number of unique binary search trees that can be created with
n
nodes, which is similar to our problem of creating all possible full binary trees.LeetCode 1130. Minimum Cost Tree From Leaf Values: This problem involves constructing binary trees, and requires using a similar approach to our problem, with additional calculations to minimize the cost.
LeetCode 95. Unique Binary Search Trees II: This problem is about generating all structurally unique BST’s that store values 1 to n. It requires recursion and dynamic programming.
LeetCode 241. Different Ways to Add Parentheses: This problem is similar because it involves recursively solving subproblems and combining their solutions, just like we did when we split the total nodes into left and right subtrees.
LeetCode 337. House Robber III: This problem requires a dynamic programming solution on a binary tree, much like our original problem.
LeetCode 124. Binary Tree Maximum Path Sum: This problem involves finding the maximum path sum in a binary tree. It requires a similar recursive traversal and dynamic programming strategy as our problem.
LeetCode 1320. Minimum Distance to Type a Word Using Two Fingers: This problem requires the use of dynamic programming to minimize a cost (in this case, the distance to type a word) and also uses a recursive approach to divide the problem into subproblems.
LeetCode 312. Burst Balloons: This problem is about maximizing the number of points obtained by bursting balloons. It requires a similar divideandconquer approach, and also benefits from dynamic programming optimization to avoid redundant computations.
LeetCode 140. Word Break II: This problem involves finding all possible ways to split a string into a sequence of words. It requires a similar approach of solving subproblems recursively and combining their results.
LeetCode 329. Longest Increasing Path in a Matrix: This problem involves finding the longest increasing path in a matrix. The problemsolving strategy involves dynamic programming and recursion which are similar to our original problem.
Remember, while these problems are related, they might involve additional or slightly different concepts, so be sure to carefully read and understand each problem statement.