Welcome back! As we focus on mastering interview-oriented problems using linked lists in Go, we'll explore practical, algorithmic challenges you may encounter during technical interviews.

Consider the following real-life problem: You’re tasked with organizing a digital library where some books have been accidentally duplicated. Your goal is to identify and remove these redundant entries to ensure each title in your catalog is unique.

A naive approach would be to browse each book and compare it with every other title in a nested loop fashion. Just like in a large library, this approach would be inefficient, with a time complexity of $O(n^2)$. As the dataset grows, the time taken increases exponentially with each additional book — similar to searching an entire library for duplicates each time a new book is added.

To address the issues with the naive approach, we use a more strategic method similar to maintaining a checklist: marking off each book as we encounter it. In Go, we achieve this by using a map to record unique titles, thereby reducing our time complexity to $O(n)$.

Go
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1type ListNode struct {
2    Value int
3    Next  *ListNode
4}
5
6func RemoveDuplicates(head *ListNode) *ListNode {
7    if head == nil || head.Next == nil {
8        return head
9    }
10
11    seenBooks := make(map[int]bool)
12    current := head
13    seenBooks[current.Value] = true
14
15    for current.Next != nil {
16        if seenBooks[current.Next.Value] {
17            current.Next = current.Next.Next
18        } else {
19            seenBooks[current.Next.Value] = true
20            current = current.Next
21        }
22    }
23    return head
24}

In this solution, we use a Go map to track previously seen books. As we traverse the linked list, if we encounter a book already present in the map, we remove it by adjusting pointers. Otherwise, we add it to the map. This approach efficiently ensures all titles are unique.

Imagine a long-distance race where you must analyze the runners' performance at every third checkpoint to gauge the race's progression.

We will traverse the linked list and compute the sum and count of every third element. Let’s examine the solution to ensure it’s clear!

Go
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1func AverageOfEveryThird(head *ListNode) float64 {
2    if head == nil || head.Next == nil || head.Next.Next == nil {
3        return 0.0
4    }
5
6    sum, count := 0, 0
7    current := head
8    for index := 1; current != nil; index++ {
9        if index%3 == 0 {
10            sum += current.Value
11            count++
12        }
13        current = current.Next
14    }
15    if count == 0 {
16        return 0.0
17    }
18    return float64(sum) / float64(count)
19}

We iterate through the linked list, summing values at every third checkpoint. The index variable keeps track of our position, and the condition index%3 == 0 ensures we only sum every third node's value. The function finally returns the average time across these checkpoints.

Throughout this lesson, we have explored optimization strategies for common linked list problems, emphasizing the importance of efficient algorithmic approaches and their practical coding implementations. By understanding both the theoretical and practical aspects of these problems, you've acquired tools to excel in technical interviews. Now it's time to apply these skills through practice, improving your proficiency with linked lists in Go.