Problem Link :- GFG POTD
Problem statement
Given a linked list of characters and a string S.Return all the anagrams of the string present in the given linked list.In case of overlapping anagrams choose the first anagram from left.
Significance of problem
The significance of the given problem statement lies in its practical application of anagrams within a linked list of characters. Anagrams, permutations of a string's characters, are a common linguistic and computational concept. This problem extends the understanding of anagrams into the realm of linked lists, showcasing the importance of efficient data structure manipulation in real-world scenarios.
The code offers an algorithmic solution for identifying anagrams, emphasizing the optimization of both time and space complexity. By traversing the linked list and employing two hash maps, the code efficiently tracks character occurrences, ensuring a systematic identification of anagrams. This process is not only vital for linguistic analysis but also finds applications in fields such as data processing, text mining, and natural language processing.
Moreover, the problem statement addresses the specific challenge of handling overlapping anagrams and prioritizing the leftmost occurrence. This requirement adds a layer of complexity, encouraging developers to think critically about the order of anagram identification. Such nuances are pivotal in scenarios where the sequence of occurrences carries significance, such as in linguistic or historical analyses.
In essence, the problem statement contributes to a well-rounded understanding of anagrams, data structures, and algorithmic design. It underscores the versatility of problem-solving skills, showcasing how a seemingly linguistic concept like anagrams can be efficiently tackled within a linked list, providing a bridge between theoretical concepts and their practical implementation in diverse computational domains.
The code offers an algorithmic solution for identifying anagrams, emphasizing the optimization of both time and space complexity. By traversing the linked list and employing two hash maps, the code efficiently tracks character occurrences, ensuring a systematic identification of anagrams. This process is not only vital for linguistic analysis but also finds applications in fields such as data processing, text mining, and natural language processing.
Moreover, the problem statement addresses the specific challenge of handling overlapping anagrams and prioritizing the leftmost occurrence. This requirement adds a layer of complexity, encouraging developers to think critically about the order of anagram identification. Such nuances are pivotal in scenarios where the sequence of occurrences carries significance, such as in linguistic or historical analyses.
In essence, the problem statement contributes to a well-rounded understanding of anagrams, data structures, and algorithmic design. It underscores the versatility of problem-solving skills, showcasing how a seemingly linguistic concept like anagrams can be efficiently tackled within a linked list, providing a bridge between theoretical concepts and their practical implementation in diverse computational domains.
Easiest Explanation
Imagine you have a long chain of different letters. Let's call it a "list of characters."
You also have a special word, let's call it "S."
Now, what we want to do is find all the words that can be made from rearranging the letters in the "list of characters" that have the exact same letters as "S." We call these words "anagrams."
For example, if the "list of characters" was "actsgt" and "S" was "cat," then we could make the following anagrams: "cat," "act," and "tac."
But, if there are multiple ways to make the same anagram (meaning, if there are multiple places in the "list of characters" where the same letters are found), we only want to include the first one we find.
So, the goal is to go through the entire "list of characters," find all the anagrams of "S," and then return a list of all the anagrams we found.
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
You also have a special word, let's call it "S."
Now, what we want to do is find all the words that can be made from rearranging the letters in the "list of characters" that have the exact same letters as "S." We call these words "anagrams."
For example, if the "list of characters" was "actsgt" and "S" was "cat," then we could make the following anagrams: "cat," "act," and "tac."
But, if there are multiple ways to make the same anagram (meaning, if there are multiple places in the "list of characters" where the same letters are found), we only want to include the first one we find.
So, the goal is to go through the entire "list of characters," find all the anagrams of "S," and then return a list of all the anagrams we found.
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
class Solution { public: vector<Node*> findAnagrams(struct Node* head, string s) { vector<Node*> ans; Node* start = head,*end = head; unordered_map<char, int> mp1,mp2; for(auto i : s) mp1[i]++; int cnt1 = mp1.size(); int cnt2 = 0; while(end != NULL){ mp2[end->data]++; if(mp2[end->data] == mp1[end->data]) cnt2++; while(mp2[end->data]>mp1[end->data]) { if(mp2[start->data]==mp1[start->data]) cnt2--; mp2[start->data]--; start=start->next; } if(cnt2==cnt1) { ans.push_back(start); Node* ahead=end->next; end->next=NULL; start=ahead,end=ahead; mp2.clear(); cnt2=0; } else end=end->next; } return ans; } };
Here's how the function works:
This program takes as input a linked list of characters and a string called "s", and it returns a vector of Node pointers that represent the nodes in the linked list where anagrams of "s" are found.
To find the anagrams, the program uses two pointers called "start" and "end" to traverse the linked list. It also uses two unordered maps called "mp1" and "mp2" to keep track of the frequency of characters in the string "s" and the characters in the linked list that have been visited so far.
The program starts by initializing "start" and "end" to the first node in the linked list, and then it populates "mp1" with the frequency of characters in "s". It also initializes two counters called "cnt1" and "cnt2" to keep track of the number of distinct characters in "s" and the number of distinct characters in the linked list that have been visited so far.
The program then enters a loop that continues until the end of the linked list is reached. During each iteration of the loop, the program adds the current character to "mp2" and checks whether the frequency of the character in "mp2" is equal to the frequency of the character in "mp1". If they are equal, it increments "cnt2" by 1.
The program then enters another loop that continues as long as the frequency of the current character in "mp2" is greater than the frequency of the character in "mp1". This loop moves the "start" pointer forward and adjusts "mp2" and "cnt2" accordingly.
If "cnt2" is equal to "cnt1", the program has found an anagram of "s". It adds the node pointed to by "start" to the vector "ans" and then resets "start", "end", "mp2", and "cnt2" to continue searching for more anagrams.
If "cnt2" is not equal to "cnt1", the program advances the "end" pointer to the next node in the linked list and repeats the process.
Finally, the program returns the vector "ans" containing all the nodes where anagrams of "s" were found in the linked list.
Learning Outcomes
Solving the anagram detection problem within a linked list imparts valuable learning outcomes spanning algorithmic thinking, data structure manipulation, and problem-solving efficiency. Firstly, the code showcases the application of hash maps to efficiently track character occurrences, providing a foundational understanding of how data structures can be leveraged to solve complex linguistic problems.
Additionally, the problem necessitates a nuanced approach to handle overlapping anagrams and prioritize the leftmost occurrences. This requirement enhances skills in designing algorithms with attention to specific constraints, fostering critical thinking and adaptability in problem-solving.
Moreover, the code encourages an appreciation for optimizing both time and space complexity. The use of hash maps and the iterative traversal of the linked list illustrate the importance of designing algorithms that balance efficiency and resource usage—a fundamental concept applicable across various computational challenges.
Furthermore, the problem underscores the practical significance of linguistic concepts like anagrams in computational contexts. This expands the learner's understanding of how language-related problems can be addressed within the domain of data structures, opening avenues for applications in text processing, data analysis, and natural language processing.
Additionally, the problem necessitates a nuanced approach to handle overlapping anagrams and prioritize the leftmost occurrences. This requirement enhances skills in designing algorithms with attention to specific constraints, fostering critical thinking and adaptability in problem-solving.
Moreover, the code encourages an appreciation for optimizing both time and space complexity. The use of hash maps and the iterative traversal of the linked list illustrate the importance of designing algorithms that balance efficiency and resource usage—a fundamental concept applicable across various computational challenges.
Furthermore, the problem underscores the practical significance of linguistic concepts like anagrams in computational contexts. This expands the learner's understanding of how language-related problems can be addressed within the domain of data structures, opening avenues for applications in text processing, data analysis, and natural language processing.
Conclusion
In conclusion, the anagram detection problem within a linked list encapsulates a multifaceted learning experience with far-reaching implications. The code solution not only provides a practical demonstration of algorithmic design but also emphasizes the application of essential data structures, specifically hash maps, in linguistic problem-solving.
Navigating the challenge of identifying overlapping anagrams and prioritizing leftmost occurrences instills critical thinking skills, encouraging developers to devise adaptive algorithms that address specific constraints. This nuanced approach enhances problem-solving agility and prepares learners for real-world scenarios where intricate considerations are commonplace.
Moreover, the emphasis on optimizing both time and space complexity underscores the importance of efficiency in algorithmic solutions. The iterative traversal of the linked list exemplifies the need for resource-conscious design, a fundamental principle that extends beyond this problem to various computational challenges.
The problem's incorporation of linguistic concepts like anagrams not only enriches language-related problem-solving skills but also broadens the understanding of how theoretical concepts find practical application in data-centric domains. Overall, mastering the anagram detection problem within a linked list contributes to a well-rounded skill set, encompassing algorithmic proficiency, data manipulation strategies, and critical thinking abilities essential for success in diverse programming landscapes.
Navigating the challenge of identifying overlapping anagrams and prioritizing leftmost occurrences instills critical thinking skills, encouraging developers to devise adaptive algorithms that address specific constraints. This nuanced approach enhances problem-solving agility and prepares learners for real-world scenarios where intricate considerations are commonplace.
Moreover, the emphasis on optimizing both time and space complexity underscores the importance of efficiency in algorithmic solutions. The iterative traversal of the linked list exemplifies the need for resource-conscious design, a fundamental principle that extends beyond this problem to various computational challenges.
The problem's incorporation of linguistic concepts like anagrams not only enriches language-related problem-solving skills but also broadens the understanding of how theoretical concepts find practical application in data-centric domains. Overall, mastering the anagram detection problem within a linked list contributes to a well-rounded skill set, encompassing algorithmic proficiency, data manipulation strategies, and critical thinking abilities essential for success in diverse programming landscapes.
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