Shortest Subarray with Sum at Least K

We can use a deque to keep track of the indexes of a non-decreasing sequence of prefix sums. This way, you’ll be able to efficiently find the shortest subarray with a sum of at least ( k ).

Here’s the code:

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from collections import deque

class Solution:
    def shortestSubarray(self, nums: List[int], k: int) -> int:
        n = len(nums)
        prefix_sums = [0] * (n + 1)
        for i in range(n):
            prefix_sums[i + 1] = prefix_sums[i] + nums[i]

        # Deque to store indexes of prefix sums
        q = deque()
        min_length = float('inf')

        for i in range(n + 1):
            # Check if the current prefix sum minus the prefix sum at the front of the deque is at least k
            while q and prefix_sums[i] - prefix_sums[q[0]] >= k:
                min_length = min(min_length, i - q.popleft())

            # Remove indexes from the back while the prefix sum is less than the current prefix sum
            while q and prefix_sums[i] <= prefix_sums[q[-1]]:
                q.pop()

            q.append(i)

        return min_length if min_length < float('inf') else -1

Key Takeaways:

The solution uses a deque to maintain a non-decreasing sequence of prefix sums. By doing this, you can find the shortest subarray with a sum of at least ( k ) efficiently.
The prefix sums help in calculating the sum of any subarray quickly.
The solution has a time complexity of ( O(n) ) and a space complexity of ( O(n) ), where ( n ) is the length of the input array nums.

Identifying Problem Isomorphism

“Shortest Subarray with Sum at Least K” can be mapped to “Minimum Size Subarray Sum”.

Reasoning:

Both involve finding a subarray within an array with a sum that meets certain criteria. In “Shortest Subarray with Sum at Least K”, the task is to find the shortest length of a subarray with a sum at least K. In “Minimum Size Subarray Sum”, the task is to find the minimal length of a subarray with a sum greater than or equal to a given value.

“Minimum Size Subarray Sum” is a simpler version of “Shortest Subarray with Sum at Least K”. The former can be solved with a two-pointer or sliding window approach, while the latter adds a level of complexity as it involves a deque to manage the window size in order to handle negative numbers and achieve a more efficient solution.

10 Prerequisite LeetCode Problems

“Shortest Subarray with Sum at Least K” involves arrays and sliding window concept. Here are 10 problems to prepare for it:

“Subarray Sum Equals K” (LeetCode Problem #560): This problem is a great starting point as it requires you to find a subarray that adds up to a certain number, K.
“Two Sum” (LeetCode Problem #1): This problem helps to understand the concept of finding two numbers that add up to a target.
“Minimum Size Subarray Sum” (LeetCode Problem #209): This problem introduces the idea of finding a subarray of minimal size that meets a certain condition.
“Continuous Subarray Sum” (LeetCode Problem #523): This problem will help you understand how to find a subarray that adds up to a multiple of a given number.
“Maximum Size Subarray Sum Equals k” (LeetCode Problem #325): This is a good practice problem for handling sums within arrays.
“Sliding Window Maximum” (LeetCode Problem #239): This problem introduces the concept of a sliding window which is a vital technique for this category of problems.
“Sliding Window Median” (LeetCode Problem #480): This problem continues to reinforce the concept of a sliding window.
“Maximum Subarray” (LeetCode Problem #53): This problem is a classic one that deals with finding the maximum possible sum in an array.
“Subarray Product Less Than K” (LeetCode Problem #713): This problem involves a product instead of a sum, but it still helps in understanding the approach for this category of problems.
“Longest Substring Without Repeating Characters” (LeetCode Problem #3): This problem uses the sliding window technique, helping to understand the approach to problems of similar nature.

Problem Classification

Problem Statement: Given an integer array nums and an integer k, return the length of the shortest non-empty subarray of nums with a sum of at least k. If there is no such subarray, return -1. A subarray is a contiguous part of an array.

Example 1:

Input: nums = [1], k = 1 Output: 1 Example 2:

Input: nums = [1,2], k = 4 Output: -1 Example 3:

Input: nums = [2,-1,2], k = 3 Output: 3

Constraints:

1 <= nums.length <= 105 -105 <= nums[i] <= 105 1 <= k <= 109

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?

Alternatively, if you’re working on a specific problem, you might ask something like:

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.

CLAUDE CANNOT CODE THE SOLUTION.

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.