What is the time complexity of Maximum Ascending Subarray Sum?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Maximum Ascending Subarray Sum?

This problem uses Array. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Maximum Ascending Subarray Sum?

The problem can be solved efficiently using a single pass through the input array, keeping track of the current sum of an ascending subarray and the maximum sum encountered so far. No sophisticated data structures are needed; a simple iterative approach is sufficient.

Which companies ask LeetCode 1800. Maximum Ascending Subarray Sum in interviews?

This problem is frequently asked at tcs.

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Maximum Ascending Subarray Sum - LeetCode 1800 Solution

Maximum Ascending Subarray Sum - Complete Solution Guide

Maximum Ascending Subarray Sum is LeetCode problem 1800, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Given an array of positive integers nums , return the maximum possible sum of an strictly increasing subarray in nums . A subarray is defined as a contiguous sequence of numbers in an array. Example 1: Input: nums = [10,20,30,5,10,50] Output: 65 Explanation: [5,10,50] is the ascending subarray with the maximum sum of 65. Example 2: Input: nums = [10,20,30,40,50] Output: 150 Explanation: [10,20,30,40,50] is the ascending subarray with the maximum sum of 150. Example 3: Input: nums = [12,17,15,13,

Detailed Explanation

The problem asks you to find the maximum sum of a strictly increasing subarray within a given array of positive integers. A strictly increasing subarray means that each element is greater than the preceding element. The subarray must be contiguous (elements are next to each other in the original array). The output is a single integer representing this maximum sum.

Solution Approach

The solution uses a greedy approach. It iterates through the array, maintaining a `currentSum` variable that tracks the sum of the current ascending subarray. If the current element is greater than the previous one, it's added to `currentSum`. Otherwise, it means the ascending subarray has ended, and `currentSum` is reset to the current element's value. The `maxSum` variable keeps track of the largest `currentSum` encountered so far. At the end, `maxSum` holds the answer.

Step-by-Step Algorithm

Initialize `maxSum` and `currentSum` to the first element of the array.
Iterate through the array starting from the second element.
If the current element is greater than the previous element, add the current element to `currentSum`.
If the current element is not greater than the previous element, reset `currentSum` to the current element.
Update `maxSum` to be the maximum of `maxSum` and `currentSum`.
After iterating through the entire array, return `maxSum`.

Key Insights

The problem can be solved efficiently using a single pass through the input array, keeping track of the current sum of an ascending subarray and the maximum sum encountered so far.
No sophisticated data structures are needed; a simple iterative approach is sufficient.
The crucial part is to reset the `currentSum` whenever a non-increasing element is found, starting a new ascending subarray.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(1)

Topics

This problem involves: Array.

Companies

Asked at: tcs.

Maximum Ascending Subarray Sum | LeetCode 1800

Easy

Array

Time: O(n)Space: O(1)

Solution Code

Available in:

class Solution {
    public int maxAscendingSum(int[] nums) {
        int maxSum = nums[0];
        int currentSum = nums[0];
        for (int i = 1; i < nums.length; i++) {
            if (nums[i] > nums[i - 1]) {
                currentSum += nums[i];
            } else {
                currentSum = nums[i];
            }
            maxSum = Math.max(maxSum, currentSum);
        }
        return maxSum;
    }
}

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Problem Info

Description

Problem 1800: Maximum Ascending Subarray Sum - Detailed explanation and solution approaches.

Topics

Array

Companies

tcs

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:1800

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

6 steps

1
Initialize `maxSum` and `currentSum` to the first element of the array.
2
Iterate through the array starting from the second element.
3
If the current element is greater than the previous element, add the current element to `currentSum`.
4
If the current element is not greater than the previous element, reset `currentSum` to the current element.
5
Update `maxSum` to be the maximum of `maxSum` and `currentSum`.
6
After iterating through the entire array, return `maxSum`.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The algorithm iterates through the input array once. No additional data structures are used whose size depends on the input size; only a few constant-size variables are used. Therefore, the time complexity is linear, and the space complexity is constant.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Dynamic Programming

While not necessary for this problem's efficiency, a dynamic programming approach could be used. An array to store the maximum ascending subarray sum ending at each index could be created.

Time: O(n)Space: O(n)

Trade-offs: This approach is less efficient in terms of space complexity than the greedy approach, making the greedy approach preferable for this problem.

✨

Key Insights

1The problem can be solved efficiently using a single pass through the input array, keeping track of the current sum of an ascending subarray and the maximum sum encountered so far.
2No sophisticated data structures are needed; a simple iterative approach is sufficient.
3The crucial part is to reset the `currentSum` whenever a non-increasing element is found, starting a new ascending subarray.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Edge Cases

Empty array: The code handles this implicitly, as the first line sets maxSum and currentSum to the first element (it won't be reached if empty). It is good practice to add an explicit check for an empty array for robustness.
Array with only one element: The code correctly handles this; `maxSum` and `currentSum` will both be the single element's value.

⚠️

Common Mistakes to Avoid

✗Forgetting to reset `currentSum` when an element is not greater than the previous one. This would lead to incorrect sums.

✗Not updating `maxSum` correctly after each comparison. The maximum sum needs to be tracked throughout the iteration.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Analyzing stock prices to find the maximum profit from consecutive ascending days.

🏦Identifying trends in data series where consecutive increases are significant (e.g., temperature readings, website traffic).

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Even for easy problems, discuss time and space complexity. Interviewers want to see you think analytically.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

👨‍💻 From Experience

This is one of the first problems I solved when learning Array. It's a great foundation for more complex problems.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: June 1, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 13

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 6

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

How should I approach easy-level algorithm problems?

Easy problems build foundational skills. Focus on: 1) Understanding the problem completely before coding, 2) Writing clean, readable code, 3) Handling all edge cases (empty input, single element, etc.), 4) Analyzing time and space complexity. Don't skip complexity analysis even for simple solutions.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Maximum Ascending Subarray Sum"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether standard techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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