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Typescript Coding Chronicles: Can Place Flowers

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 `true` if `n` new flowers can be planted in the flowerbed without violating the no-adjacent-flowers rule and `false` otherwise.

Example 1:

• Input: `flowerbed = [1,0,0,0,1]`, `n = 1`
• Output: `true`

Example 2:

• Input: `flowerbed = [1,0,0,0,1]`, `n = 2`
• Output: `false`

Constraints:

• `1 <= flowerbed.length <= 2 * 10^4`
• `flowerbed[i]` is 0 or 1.
• There are no two adjacent flowers in flowerbed.
• `0 <= n <= flowerbed.length`

Initial Thought Process:

To solve this problem, we need to iterate through the flowerbed and check each position to determine if a flower can be planted. If a position is empty (0) and both adjacent positions are either empty or out of bounds, we can plant a flower there.

Basic Solution:

Code:

``````function canPlaceFlowersBruteForce(flowerbed: number[], n: number): boolean {
let count = 0;

for (let i = 0; i < flowerbed.length; i++) {
if (flowerbed[i] === 0) {
let prevEmpty = (i === 0) || (flowerbed[i - 1] === 0);
let nextEmpty = (i === flowerbed.length - 1) || (flowerbed[i + 1] === 0);

if (prevEmpty && nextEmpty) {
flowerbed[i] = 1;
count++;
if (count >= n) {
return true;
}
}
}
}

return count >= n;
}
``````

Time Complexity Analysis:

• Time Complexity: O(n^2), where n is the length of the flowerbed array. The inner loop effectively makes this approach less efficient.
• Space Complexity: O(1), as we are modifying the flowerbed array in place and using only a constant amount of extra space.

Limitations:

The brute force solution is not optimal for larger input sizes due to its higher time complexity.

Optimized Solution:

The optimized solution will still iterate through the flowerbed array but will skip unnecessary checks by moving to the position after the next one once a flower is planted, ensuring we do not plant adjacent flowers.

Code:

``````function canPlaceFlowersOptimized(flowerbed: number[], n: number): boolean {
let count = 0;
let i = 0;

while (i < flowerbed.length) {
if (flowerbed[i] === 0 &&
(i === 0 || flowerbed[i - 1] === 0) &&
(i === flowerbed.length - 1 || flowerbed[i + 1] === 0)) {
flowerbed[i] = 1;  // Plant a flower here
count++;
i += 2;  // Move to the position after the next one
} else {
i++;
}

if (count >= n) {
return true;
}
}

return count >= n;
}
``````

Time Complexity Analysis:

• Time Complexity: O(n), where n is the length of the flowerbed array. We iterate through the flowerbed array once.
• Space Complexity: O(1), as we are modifying the flowerbed array in place and using only a constant amount of extra space.

Improvements Over Basic Solution:

• The optimized solution skips unnecessary checks by moving to the position after the next one once a flower is planted, ensuring we do not plant adjacent flowers.

Edge Cases and Testing:

Edge Cases:

1. The flowerbed is entirely empty.
2. The flowerbed has alternating empty and non-empty plots.
3. `n` is 0, meaning no new flowers need to be planted.
4. `n` is larger than the possible number of plantable positions in the flowerbed.

Test Cases:

``````console.log(canPlaceFlowersBruteForce([1,0,0,0,1], 1)); // true
console.log(canPlaceFlowersBruteForce([1,0,0,0,1], 2)); // false
console.log(canPlaceFlowersBruteForce([0,0,1,0,0], 1)); // true
console.log(canPlaceFlowersBruteForce([0,0,1,0,0], 2)); // true
console.log(canPlaceFlowersBruteForce([0,0,1,0,1], 1)); // false
console.log(canPlaceFlowersBruteForce([1,0,0,0,0,1], 1)); // true
console.log(canPlaceFlowersBruteForce([1,0,0,0,0,1], 2)); // false
console.log(canPlaceFlowersBruteForce([0,0,0,0,0,0], 3)); // true
console.log(canPlaceFlowersBruteForce([0,0,0,0,0,0], 4)); // false

console.log(canPlaceFlowersOptimized([1,0,0,0,1], 1)); // true
console.log(canPlaceFlowersOptimized([1,0,0,0,1], 2)); // false
console.log(canPlaceFlowersOptimized([0,0,1,0,0], 1)); // true
console.log(canPlaceFlowersOptimized([0,0,1,0,0], 2)); // true
console.log(canPlaceFlowersOptimized([0,0,1,0,1], 1)); // false
console.log(canPlaceFlowersOptimized([1,0,0,0,0,1], 1)); // true
console.log(canPlaceFlowersOptimized([1,0,0,0,0,1], 2)); // false
console.log(canPlaceFlowersOptimized([0,0,0,0,0,0], 3)); // true
console.log(canPlaceFlowersOptimized([0,0,0,0,0,0], 4)); // false
``````

General Problem-Solving Strategies:

1. Understand the Problem: Carefully read the problem statement to understand the requirements and constraints.
2. Identify Key Operations: Determine the key operations needed, such as checking adjacent plots and planting flowers.
3. Optimize for Readability: Use clear and concise logic to ensure the code is easy to follow.
4. Test Thoroughly: Test the solution with various cases, including edge cases, to ensure correctness.

Identifying Similar Problems:

1. Array Manipulation:

• Problems where you need to modify elements of an array based on specific conditions.
• Example: Moving zeros to the end of an array.
2. Greedy Algorithms:

• Problems where a greedy approach can be used to find an optimal solution by making the best choice at each step.
• Example: Interval scheduling to find the maximum number of non-overlapping intervals.
3. Simulation Problems:

• Problems where you need to simulate a process step-by-step based on given rules.
• Example: Simulating the spread of a virus in a population represented by an array.

Conclusion:

• The problem of determining if new flowers can be planted in a flowerbed without violating the no-adjacent-flowers rule can be efficiently solved using both a brute force approach and an optimized approach.
• Understanding the problem and breaking it down into manageable parts is crucial.
• Using clear logic and optimizing for readability ensures the solution is easy to follow.
• Testing with various edge cases ensures robustness.
• Recognizing patterns in problems can help apply similar solutions to other challenges.

By practicing such problems and strategies, you can improve your problem-solving skills and be better prepared for various coding challenges.