Maximum Height by Stacking Cuboids

This problem can be solved by sorting and dynamic programming. Here are the steps to solve the problem:

Sorting: For each cuboid, sort its dimensions in non-decreasing order. Then, sort the cuboids by their dimensions. This ensures that we have a sorted order and can easily apply dynamic programming to find the solution.
Dynamic Programming: Let dp[i] be the maximum height that can be achieved using the first i cuboids. Then, we iterate through the cuboids and use the sorted order to find the best stacking.

Here’s the code:

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class Solution:
    def maxHeight(self, cuboids: List[List[int]]) -> int:
        for cuboid in cuboids:
            cuboid.sort()
        cuboids.sort()

        dp = [0] * len(cuboids)
        for i in range(len(cuboids)):
            dp[i] = cuboids[i][2]  # Initialize with the current cuboid's height
            for j in range(i):
                if cuboids[i][0] >= cuboids[j][0] and cuboids[i][1] >= cuboids[j][1] and cuboids[i][2] >= cuboids[j][2]:
                    dp[i] = max(dp[i], dp[j] + cuboids[i][2])

        return max(dp)

Explanation:

For each cuboid, we sort its dimensions.
Then we sort the cuboids themselves.
Using dynamic programming, we calculate the maximum height that can be achieved by considering the dimensions’ constraints (width, length, height).
Finally, we return the maximum height found in the dp array.

This solution is efficient and should work within the given constraints.

Identifying Problem Isomorphism

“Maximum Height by Stacking Cuboids” can be mapped approximately to “Russian Doll Envelopes”.

Reasoning:

In “Maximum Height by Stacking Cuboids”, you are given a set of cuboids with dimensions and your task is to stack them to maximize the height. You can rotate the cuboids and the cuboid that is placed on top of another cuboid must be strictly smaller in all dimensions.

In “Russian Doll Envelopes”, you are given a set of envelopes with two dimensions, and your task is to stack them in a way that maximizes the number of envelopes in the stack. An envelope can only fit into another envelope if it’s strictly smaller in both dimensions. You can also sort and rearrange the envelopes before stacking them, similar to how you can rotate the cuboids in the other problem.

Both are about ordering a list of multi-dimensional objects (cuboids/envelopes) to optimize a certain objective (height/number of envelopes), and in both cases, an object can only be placed on top of another object if it’s strictly smaller in all dimensions. Thus, the strategy in both problems involves sorting the objects according to some criteria, then using dynamic programming to find the best order of stacking.

Both require multi-dimensional sorting and dynamic programming. “Russian Doll Envelopes” is simpler because it involves two dimensions while the “Maximum Height by Stacking Cuboids” involves three dimensions. The principle behind solving both problems is very similar. There’s no exact mapping due to the distinct problem context and additional rotation possibility in “Maximum Height by Stacking Cuboids”.

10 Prerequisite LeetCode Problems

This involves sorting, dynamic programming and rotation of the cuboids to fit them on top of each other. Here are 10 problems to prepare for this one:

354. Russian Doll Envelopes: This problem requires finding the longest increasing subsequence in two dimensions, which is similar to finding the tallest stack of cuboids.
300. Longest Increasing Subsequence: Involves finding the longest increasing subsequence in an array, a technique that can be extended to two or three dimensions.
435. Non-overlapping Intervals: This problem teaches techniques for dealing with overlapping intervals, similar to dealing with overlapping cuboids.
646. Maximum Length of Pair Chain: Involves finding the longest chain of pairs that can be formed, which is a similar concept to stacking cuboids.
322. Coin Change: A fundamental dynamic programming problem that could provide insights into creating the dp states for the cuboid problem.
377. Combination Sum IV: This problem also requires dynamic programming, and it might provide insights into how to keep track of the best solutions as you progress.
31. Next Permutation: Teaches about rearranging elements in an array to create a certain order, which is applicable when you need to rearrange the dimensions of the cuboids.
56. Merge Intervals: Useful for understanding how to deal with overlapping ranges, which could help in figuring out how to stack the cuboids.
179. Largest Number: Although not directly related, this problem involves a custom sorting order which could be helpful to understand how to sort the cuboids.
673. Number of Longest Increasing Subsequence: A variation of the Longest Increasing Subsequence problem, where instead of just length, we are also interested in the count.

Problem Analysis and Key Insights

What are the key insights from analyzing the problem statement?

Problem Boundary

What is the scope of this problem?

How to establish the boundary of this problem?

Problem Classification

Problem Statement:Given n cuboids where the dimensions of the ith cuboid is cuboids[i] = [widthi, lengthi, heighti] (0-indexed). Choose a subset of cuboids and place them on each other.

You can place cuboid i on cuboid j if widthi <= widthj and lengthi <= lengthj and heighti <= heightj. You can rearrange any cuboid’s dimensions by rotating it to put it on another cuboid.

Return the maximum height of the stacked cuboids.

Example 1:

Input: cuboids = [[50,45,20],[95,37,53],[45,23,12]] Output: 190 Explanation: Cuboid 1 is placed on the bottom with the 53x37 side facing down with height 95. Cuboid 0 is placed next with the 45x20 side facing down with height 50. Cuboid 2 is placed next with the 23x12 side facing down with height 45. The total height is 95 + 50 + 45 = 190.

Example 2:

Input: cuboids = [[38,25,45],[76,35,3]] Output: 76 Explanation: You can’t place any of the cuboids on the other. We choose cuboid 1 and rotate it so that the 35x3 side is facing down and its height is 76.

Example 3:

Input: cuboids = [[7,11,17],[7,17,11],[11,7,17],[11,17,7],[17,7,11],[17,11,7]] Output: 102 Explanation: After rearranging the cuboids, you can see that all cuboids have the same dimension. You can place the 11x7 side down on all cuboids so their heights are 17. The maximum height of stacked cuboids is 6 * 17 = 102.

Constraints:

n == cuboids.length 1 <= n <= 100 1 <= widthi, lengthi, heighti <= 100

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

Distilling the Problem to Its Core Elements

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

Visual Model of the Problem

How to visualize the problem statement for this problem?

Problem Restatement

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

Abstract Representation of the Problem

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

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

Terminology

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

Problem Simplification and Explanation

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

Constraints

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

What are the key insights from analyzing the constraints?

Case Analysis

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

Provide names by categorizing these cases

What are the edge cases?

What are the key insights from analyzing the different cases?

Identification of Applicable Theoretical Concepts

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

Simple Explanation

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

Problem Breakdown and Solution Methodology

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

Inference of Problem-Solving Approach from the Problem Statement

Can you identify the key terms or concepts in this problem and explain how they inform your approach to solving it? Please list each keyword and how it guides you towards using a specific strategy or method.

How 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?

Could you please provide a stepwise refinement of our approach to solving this problem?
How can we take the high-level solution approach and distill it into more granular, actionable steps?
Could you identify any parts of the problem that can be solved independently?
Are there any repeatable patterns within our solution?

Solution Approach and Analysis

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

Parameters:
- What are the inputs to the method?
- What types are these parameters?
- What do these parameters represent in the context of the problem?
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?
Method Functionality:
- What is this method expected to do?
- How does it interact with the inputs and the current state of the program?
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?
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

Problem Understanding:
- Can you explain the problem in your own words? What are the key components and requirements?
Initial Breakdown:
- Start by identifying the major parts or stages of the problem. How can you break the problem into several broad subproblems?
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?
Task Identification:
- Within these smaller tasks, are there any that are repeated or very similar? Could these be generalized into a single, reusable task?
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?
Method Naming:
- Can you give each task a simple, descriptive name that makes its purpose clear?
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

Identify the initial parameters and explain their significance in the context of the problem statement or the solution domain.
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?
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.
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?
Describe any invariant that’s maintained throughout the code, and explain how it helps meet the problem’s constraints or objectives.
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.

What are the high-level problem-solving strategies or techniques being used by this code?
If you had to explain the purpose of this code to a non-programmer, what would you say?
Can you identify the logical elements or constructs used in this code, independent of any programming language?
Could you describe the algorithmic approach used by this code in plain English?
What are the key steps or operations this code is performing on the input data, and why?
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.

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.
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.
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.

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.
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.
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.