Minimum Time to Build Blocks

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class Solution:
    def minBuildTime(self, blocks: List[int], split: int) -> int:
        heapq.heapify(blocks)
        while len(blocks) > 1:
            block_1 = heapq.heappop(blocks)
            block_2 = heapq.heappop(blocks)
            new_block = max(block_1, block_2) + split
            heapq.heappush(blocks, new_block)
        return blocks[0]

10 Prerequisite LeetCode Problems

“Minimum Time to Build Blocks” (#1199) is a dynamic programming problem that involves a heap. Here are 10 problems to prepare:

“Assign Cookies” (LeetCode Problem #455): This problem involves sorting and greedy algorithm, which are helpful concepts for the main problem.
“Meeting Rooms II” (LeetCode Problem #253): This problem can help you practice with heap operations, which is essential for the main problem.
“Merge k Sorted Lists” (LeetCode Problem #23): This problem also involves using heap, similar to the main problem.
“House Robber” (LeetCode Problem #198): This dynamic programming problem is simpler and can help you prepare for more complex dynamic programming problems.
“Climbing Stairs” (LeetCode Problem #70): Another basic dynamic programming problem that can help you understand the concept.
“Coin Change” (LeetCode Problem #322): This problem involves dynamic programming and greedy algorithm, similar to the main problem.
“Top K Frequent Elements” (LeetCode Problem #347): This problem involves using a heap to solve a problem, which can be a good practice for the main problem.
“Binary Tree Maximum Path Sum” (LeetCode Problem #124): This problem involves a form of dynamic programming on trees, which is a good way to practice dynamic programming.
“Kth Largest Element in an Array” (LeetCode Problem #215): This problem involves using a heap to solve a problem, which can be a good practice for the main problem.
“Best Time to Buy and Sell Stock with Cooldown” (LeetCode Problem #309): This problem involves dynamic programming and is a bit more complex, but it can provide a good challenge before tackling the main problem.

The concepts of heap and dynamic programming are key to solving this problem.

Problem Classification

The problem falls under the domain of Combinatorial Optimization and Scheduling.

‘What’

A list of blocks with each block having a time requirement (t) to build.
A worker that can either build a block or split into two workers.
A time cost (split) for splitting one worker into two.
Initial condition: Only one worker is available.
Objective: Minimize the time needed to build all blocks.

The problem can be classified as a Dynamic Programming problem with elements of Greedy Algorithms for local decisions. It can also be seen as a Resource Allocation problem where the resource is the worker’s time and the objective is to allocate it optimally to minimize the total time taken to build all blocks.

The problem requires optimizing the sequence and timing of actions (split or build) that workers should take in order to minimize the total time required to build all blocks. This involves making decisions at multiple stages based on the current state, making it well-suited for a Dynamic Programming approach.

Visual Model of the Problem

Visualizing this problem can help clarify the dependencies and the dynamics of the choices involved. Here are a few ways to visualize it:

Timeline: Draw a horizontal timeline, breaking it into units. Mark when a worker splits and when blocks are being built. This helps you understand the sequence of actions and their cost in time.
Tree Diagram: Imagine a tree where each node represents a state in the decision-making process. The tree can have two types of branches: one for splitting a worker and another for building a block. Label each edge with the time cost of the corresponding action.
Grid: You can also use a 2D grid to represent the state of the problem. One axis could represent the number of workers, and the other could represent the remaining blocks. Each cell in the grid would then represent a state with a specific number of workers and remaining blocks. You could fill in each cell with the minimum time needed to reach that state.
Gantt Chart: You can use a Gantt chart to show the allocation of workers to blocks over time. Each row could represent a worker and blocks of time on that row represent that worker’s tasks (either splitting or building a block).
Stack of Blocks: Imagine the blocks as actual physical blocks. You can visualize stacking them up in the order in which they’ll be built, which might help you see a more efficient way to allocate workers to blocks.

These visualizations can help you get a grip on the complexities and nuances of the problem, making it easier to devise an optimal strategy for solving it.

Problem Restatement

You start with one worker and a list of blocks that need to be built. Each block requires a certain amount of time to be completed. A worker has two choices: either to build a block or to split into two workers. Splitting also takes some time. Your goal is to find the least amount of time required to build all the blocks. You can assume the following:

You initially have just one worker.
Each block takes a fixed, known amount of time to build.
Splitting a worker into two also takes a fixed, known amount of time.
Multiple splits can happen at the same time, costing you only the time for a single split.

The constraints specify that you’ll have up to 1000 blocks, each taking up to 100,000 units of time to build, and the split time will be up to 100 units.

Abstract Representation of the Problem

To abstract this problem, consider:

An array ( B ), where ( B[i] ) is the time needed to construct the ( i^{th} ) item.
A single integer ( S ) as the cost to double your available workforce.
Initially, a single worker, denoted as ( W = 1 ).

Your task is to find the minimum total time ( T ) to build all items in ( B ), using the initial worker, who can either build or duplicate into two workers at a time cost of ( S ).

So the abstract problem becomes: “Minimize ( T ) such that all items in ( B ) are constructed using ( W ) workers, where ( W ) can increment by duplication at a time cost ( S ).”

Constraints:

( 1 \leq |B| \leq 1000 )
( 1 \leq B[i] \leq 10^5 )
( 1 \leq S \leq 100 )

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.

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?

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

How did you infer from the problem statement that this problem can be solved using ?

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

Thought Process

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.

From Brute Force to Optimal Solution

shitgpt

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.