Problem Link :- leetcode-605
Problem statement
You have a long flowerbed in which some of the plots are planted, and some are not. However, flowers cannot be planted in adjacent plots.
Given an integer array flowerbed containing 0's and 1's, where 0 means empty and 1 means not empty, and an integer n, return if n new flowers can be planted in the flowerbed without violating the no-adjacent-flowers rule.
Given an integer array flowerbed containing 0's and 1's, where 0 means empty and 1 means not empty, and an integer n, return if n new flowers can be planted in the flowerbed without violating the no-adjacent-flowers rule.
Significance of problem
The "Can Place Flowers" problem holds significance as it addresses a practical scenario of optimizing the planting of flowers in a flowerbed while adhering to a specific rule: flowers cannot be planted in adjacent plots. This problem encapsulates fundamental concepts in algorithmic reasoning and garden planning, making it a valuable exercise for learners.
Firstly, this problem cultivates a deep understanding of array manipulation. By traversing the flowerbed and strategically deciding where to plant flowers based on the given conditions, learners engage in real-world problem-solving, honing their ability to manipulate array elements efficiently. This skill is transferrable to a myriad of scenarios where array manipulation plays a pivotal role.
Secondly, the problem introduces learners to the concept of optimization within constraints. As the task is to maximize the number of flowers planted while respecting the adjacency rule, learners develop an appreciation for balancing competing priorities. This optimization mindset is invaluable in algorithmic design, where finding efficient solutions under specific constraints is a common objective.
Moreover, the problem fosters a sense of practicality by simulating a garden scenario. The rules mimic the constraints gardeners face when planning flower arrangements, adding a layer of realism to the algorithmic challenge. This contextualization encourages learners to apply algorithmic thinking to real-world scenarios, enhancing their problem-solving skills in practical contexts.
Firstly, this problem cultivates a deep understanding of array manipulation. By traversing the flowerbed and strategically deciding where to plant flowers based on the given conditions, learners engage in real-world problem-solving, honing their ability to manipulate array elements efficiently. This skill is transferrable to a myriad of scenarios where array manipulation plays a pivotal role.
Secondly, the problem introduces learners to the concept of optimization within constraints. As the task is to maximize the number of flowers planted while respecting the adjacency rule, learners develop an appreciation for balancing competing priorities. This optimization mindset is invaluable in algorithmic design, where finding efficient solutions under specific constraints is a common objective.
Moreover, the problem fosters a sense of practicality by simulating a garden scenario. The rules mimic the constraints gardeners face when planning flower arrangements, adding a layer of realism to the algorithmic challenge. This contextualization encourages learners to apply algorithmic thinking to real-world scenarios, enhancing their problem-solving skills in practical contexts.
Easiest Explanation
Imagine you have a long garden bed where some parts already have flowers growing and some parts are empty. But there's a rule that says you can't plant new flowers in the empty parts right next to the parts where there are already flowers growing. Now you have been given a task to figure out if it's possible to plant some new flowers in the empty parts of the garden bed without breaking that rule.
You have a list that tells you which parts of the garden bed are empty (represented by 0) and which parts already have flowers (represented by 1). You also know how many new flowers you want to plant. Your job is to look at the list and figure out if you can plant the new flowers without putting any of them next to a part that already has flowers growing. If you can plant the new flowers without breaking the rule, you say "yes" it's possible, and if you can't, you say "no" it's not possible
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
You have a list that tells you which parts of the garden bed are empty (represented by 0) and which parts already have flowers (represented by 1). You also know how many new flowers you want to plant. Your job is to look at the list and figure out if you can plant the new flowers without putting any of them next to a part that already has flowers growing. If you can plant the new flowers without breaking the rule, you say "yes" it's possible, and if you can't, you say "no" it's not possible
I hope that explanation helps! If you find any difficulty in solving, feel free to comment with your doubts.
class Solution(object):
def canPlaceFlowers(self, flowerbed, n):
size = len(flowerbed)
i = 0
while i < size and n > 0:
if flowerbed[i] == 0 and (i == 0 or flowerbed[i-1] == 0) and (i == size-1 or flowerbed[i+1] == 0):
flowerbed[i] = 1
n -= 1
i += 1
return n <= 0
class Solution { public: bool canPlaceFlowers(vector<int>& flowerbed, int n) { int size = flowerbed.size(); int i=0; while(i<size && n>0 ){ if(flowerbed[i]==0 && (i==0 || flowerbed[i-1]==0) && (i==size-1 ||flowerbed[i+1]==0)){ flowerbed[i] = 1; n--; } i++; } if(n<=0) return true; else return false; } };
Here's how the function works:
1. The function takes in two parameters, a vector representing the flowerbed and an integer representing the number of new flowers to be planted.
2. The size of the flowerbed vector is obtained and stored in the size variable.
3. An index variable i is initialized to 0.
4. A while loop is used to iterate through the flowerbed vector until either the end of the vector is reached or n new flowers have been planted. The loop checks if the current position in the flowerbed is empty (represented by 0), and if the adjacent positions are also empty (i.e., there are no flowers already planted there). If all of these conditions are met, a new flower is planted at that position, and n is decremented. The loop continues to iterate through the flowerbed until either all n new flowers have been planted or the end of the flowerbed vector is reached.
5. Finally, after the while loop has completed, the function checks whether all n new flowers have been planted. If n is less than or equal to 0, then all new flowers have been planted without violating the no-adjacent-flowers rule, and the function returns true. Otherwise, the function returns false.
2. The size of the flowerbed vector is obtained and stored in the size variable.
3. An index variable i is initialized to 0.
4. A while loop is used to iterate through the flowerbed vector until either the end of the vector is reached or n new flowers have been planted. The loop checks if the current position in the flowerbed is empty (represented by 0), and if the adjacent positions are also empty (i.e., there are no flowers already planted there). If all of these conditions are met, a new flower is planted at that position, and n is decremented. The loop continues to iterate through the flowerbed until either all n new flowers have been planted or the end of the flowerbed vector is reached.
5. Finally, after the while loop has completed, the function checks whether all n new flowers have been planted. If n is less than or equal to 0, then all new flowers have been planted without violating the no-adjacent-flowers rule, and the function returns true. Otherwise, the function returns false.
Learning Outcomes
Solving the "Can Place Flowers" problem yields a multitude of learning outcomes, contributing to the development of essential skills and knowledge in algorithmic thinking and array manipulation.
Firstly, learners enhance their proficiency in array manipulation. The problem necessitates traversing a flowerbed array and making strategic decisions based on certain conditions. This exercise reinforces the understanding of array indices, conditional statements, and the manipulation of array elements, laying a solid foundation for handling arrays in various algorithmic contexts.
Secondly, the problem cultivates algorithmic reasoning and optimization skills. Learners engage in the process of strategically placing flowers while adhering to specific constraints. This experience sharpens their ability to think algorithmically, finding optimal solutions under defined conditions. The optimization mindset developed in this context is transferable to a wide range of algorithmic challenges where efficiency is paramount.
Furthermore, learners gain practical insights into problem-solving within constraints. The task of maximizing the number of planted flowers while respecting the no-adjacent-flowers rule mirrors real-world scenarios, where solutions must balance competing priorities. This skill is crucial in algorithmic design, where constraints often play a crucial role in determining the feasibility and efficiency of solutions.
Moreover, the problem encourages learners to approach algorithmic challenges with a practical mindset. By simulating a garden scenario, the problem contextualizes abstract algorithmic concepts, fostering an appreciation for the real-world applications of algorithmic thinking. This practical approach prepares learners to apply their skills to diverse scenarios beyond the realm of coding challenges.
Firstly, learners enhance their proficiency in array manipulation. The problem necessitates traversing a flowerbed array and making strategic decisions based on certain conditions. This exercise reinforces the understanding of array indices, conditional statements, and the manipulation of array elements, laying a solid foundation for handling arrays in various algorithmic contexts.
Secondly, the problem cultivates algorithmic reasoning and optimization skills. Learners engage in the process of strategically placing flowers while adhering to specific constraints. This experience sharpens their ability to think algorithmically, finding optimal solutions under defined conditions. The optimization mindset developed in this context is transferable to a wide range of algorithmic challenges where efficiency is paramount.
Furthermore, learners gain practical insights into problem-solving within constraints. The task of maximizing the number of planted flowers while respecting the no-adjacent-flowers rule mirrors real-world scenarios, where solutions must balance competing priorities. This skill is crucial in algorithmic design, where constraints often play a crucial role in determining the feasibility and efficiency of solutions.
Moreover, the problem encourages learners to approach algorithmic challenges with a practical mindset. By simulating a garden scenario, the problem contextualizes abstract algorithmic concepts, fostering an appreciation for the real-world applications of algorithmic thinking. This practical approach prepares learners to apply their skills to diverse scenarios beyond the realm of coding challenges.
Conclusion
In conclusion, solving the "Flower Planting" problem provides valuable insights into array manipulation, algorithmic reasoning, and practical problem-solving. Learners not only refine their skills in efficiently navigating and manipulating arrays but also cultivate a strategic mindset in algorithmic decision-making. The task's real-world context, simulating flower planting in a garden, bridges the gap between abstract algorithms and tangible scenarios, fostering a deeper understanding of how algorithmic thinking applies to practical situations.
Moreover, the problem instills a sense of optimization, challenging learners to find the most efficient solutions under specific constraints. This optimization mindset is a transferable skill applicable to a broad spectrum of algorithmic challenges. Overall, the "Flower Planting" problem serves as a comprehensive exercise, equipping learners with essential tools for tackling diverse algorithmic problems and reinforcing the practical applications of algorithmic thinking in real-world contexts.
Moreover, the problem instills a sense of optimization, challenging learners to find the most efficient solutions under specific constraints. This optimization mindset is a transferable skill applicable to a broad spectrum of algorithmic challenges. Overall, the "Flower Planting" problem serves as a comprehensive exercise, equipping learners with essential tools for tackling diverse algorithmic problems and reinforcing the practical applications of algorithmic thinking in real-world contexts.
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