Minimum Initial Energy to Finish Tasks

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class Solution:
    def minimumEffort(self, tasks: List[List[int]]) -> int:
        tasks.sort(key=lambda x: x[0] - x[1])
        total = 0
        current = 0
        for cost, min_energy_required in tasks:
            if min_energy_required > current:
                total += (min_energy_required - current)
                current = min_energy_required
            current -= cost

        return total

This involves sorting and greedy algorithms. Here are 10 problems to prepare for this:

LeetCode 455. Assign Cookies
- This problem introduces you to the greedy algorithm. It requires you to efficiently distribute resources to get maximum results.
LeetCode 435. Non-overlapping Intervals
- This problem helps you understand the concept of interval scheduling, which can be solved with a greedy approach.
LeetCode 605. Can Place Flowers
- A simpler problem where you need to make optimal decisions in each step, another good practice for greedy algorithms.
LeetCode 122. Best Time to Buy and Sell Stock II
- This problem teaches you to make a series of decisions that globally lead to the maximum profit, reinforcing your understanding of the greedy algorithm.
LeetCode 406. Queue Reconstruction by Height
- This problem is about reconstructing a queue based on certain rules. It helps you understand how to apply sorting and greedy techniques to solve complex problems.
LeetCode 1046. Last Stone Weight
- This problem demonstrates the use of priority queues in the context of a greedy problem, where the objective is to minimize or maximize a certain parameter.
LeetCode 134. Gas Station
- This problem involves understanding and applying a greedy approach in a circular array, which might be useful in the main problem.
LeetCode 1029. Two City Scheduling
- This problem involves a sorting approach and a greedy technique to minimize cost.
LeetCode 392. Is Subsequence
- It can help you understand the concept of the subsequence and greedy approach.
LeetCode 121. Best Time to Buy and Sell Stock

This problem is a simpler version of the stock problems and involves dynamic programming and will help you understand the concept of maintaining a dynamic ‘maximum’ or ‘minimum’ value during traversal.

Problem Classification

Problem Statement:You are given an array tasks where tasks[i] = [actuali, minimumi]:

actuali is the actual amount of energy you spend to finish the ith task. minimumi is the minimum amount of energy you require to begin the ith task. For example, if the task is [10, 12] and your current energy is 11, you cannot start this task. However, if your current energy is 13, you can complete this task, and your energy will be 3 after finishing it.

You can finish the tasks in any order you like.

Return the minimum initial amount of energy you will need to finish all the tasks.

Example 1:

Input: tasks = [[1,2],[2,4],[4,8]] Output: 8 Explanation: Starting with 8 energy, we finish the tasks in the following order: - 3rd task. Now energy = 8 - 4 = 4. - 2nd task. Now energy = 4 - 2 = 2. - 1st task. Now energy = 2 - 1 = 1. Notice that even though we have leftover energy, starting with 7 energy does not work because we cannot do the 3rd task.

Example 2:

Input: tasks = [[1,3],[2,4],[10,11],[10,12],[8,9]] Output: 32 Explanation: Starting with 32 energy, we finish the tasks in the following order: - 1st task. Now energy = 32 - 1 = 31. - 2nd task. Now energy = 31 - 2 = 29. - 3rd task. Now energy = 29 - 10 = 19. - 4th task. Now energy = 19 - 10 = 9. - 5th task. Now energy = 9 - 8 = 1.

Example 3:

Input: tasks = [[1,7],[2,8],[3,9],[4,10],[5,11],[6,12]] Output: 27 Explanation: Starting with 27 energy, we finish the tasks in the following order: - 5th task. Now energy = 27 - 5 = 22. - 2nd task. Now energy = 22 - 2 = 20. - 3rd task. Now energy = 20 - 3 = 17. - 1st task. Now energy = 17 - 1 = 16. - 4th task. Now energy = 16 - 4 = 12. - 6th task. Now energy = 12 - 6 = 6.

Constraints:

1 <= tasks.length <= 105 1 <= actuali <= minimumi <= 104

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.

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.

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

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?

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.