Is Minimum Swaps to Avoid Forbidden Values easy or hard?

Minimum Swaps to Avoid Forbidden Values is typically classified as a hard problem because it combines greedy reasoning, counting conflicts, and permutation-style cycle resolution. Recognizing that swaps correspond to cycles is the main challenge.

Minimum Swaps to Avoid Forbidden Values Python/Java solution

Most implementations iterate through the array, store conflicting indices, and use a hash map to locate valid swap targets. The same logic translates directly across Python, Java, C++, and Go with O(n) time complexity.

How to solve Minimum Swaps to Avoid Forbidden Values in O(n)?

First record all indices where nums[i] equals the forbidden value. Use a hash map from values to indices to find swap partners that resolve two conflicts simultaneously. If conflicts form dependency chains, resolve them as permutation cycles where a cycle of length k requires k-1 swaps.

What is the best approach for Minimum Swaps to Avoid Forbidden Values?

The most efficient solution uses a greedy strategy with hash table tracking or cycle detection. Identify indices where nums[i] equals the forbidden value, then swap elements so both positions become valid. This approach runs in O(n) time with O(n) auxiliary space.

Is Minimum Swaps to Avoid Forbidden Values asked at Google/Amazon/Meta?

Problems involving minimum swaps, derangements, and conflict resolution appear frequently in interviews at companies like Google, Amazon, and Meta. Variants often involve avoiding specific positions or minimizing swaps while respecting constraints.

What data structure is used in Minimum Swaps to Avoid Forbidden Values?

Hash tables are commonly used to map values to indices and quickly find valid swap partners. Arrays or visited sets help track processed positions when resolving cycles in the mismatch graph.

What is the time complexity of Minimum Swaps to Avoid Forbidden Values?

The optimal solution runs in O(n) time because the array is scanned once to detect conflicts and each index participates in at most one swap or cycle resolution. Extra storage such as a hash map or visited array requires O(n) space.

3785. Minimum Swaps to Avoid Forbidden Values - Practice Online

You are given two integer arrays, nums and forbidden, each of length n.

You may perform the following operation any number of times (including zero):

Choose two distinct indices i and j, and swap nums[i] with nums[j].

Return the minimum number of swaps required such that, for every index i, the value of nums[i] is not equal to forbidden[i]. If no amount of swaps can ensure that every index avoids its forbidden value, return -1.

Example 1:

Input: nums = [1,2,3], forbidden = [3,2,1]

Output: 1

Explanation:

One optimal set of swaps:

Select indices i = 0 and j = 1 in nums and swap them, resulting in nums = [2, 1, 3].
After this swap, for every index i, nums[i] is not equal to forbidden[i].

Example 2:

Input: nums = [4,6,6,5], forbidden = [4,6,5,5]

Output: 2

Explanation:

One optimal set of swaps:

Select indices i = 0 and j = 2 in nums and swap them, resulting in nums = [6, 6, 4, 5].
Select indices i = 1 and j = 3 in nums and swap them, resulting in nums = [6, 5, 4, 6].
After these swaps, for every index i, nums[i] is not equal to forbidden[i].

Example 3:

Input: nums = [7,7], forbidden = [8,7]

Output: -1

Explanation:

It is not possible to make nums[i] different from forbidden[i] for all indices.

Example 4:

Input: nums = [1,2], forbidden = [2,1]

Output: 0

Explanation:

No swaps are required because nums[i] is already different from forbidden[i] for all indices, so the answer is 0.

Constraints:

1 <= n == nums.length == forbidden.length <= 10⁵
1 <= nums[i], forbidden[i] <= 10⁹