Find All Possible Stable Binary Arrays II - Video Solutions
Find All Possible Stable Binary Arrays II | Derive From Part I | Detailed | Leetcode 3130 | MIK
Find All Possible Stable Binary Arrays II - Video Solution
Watch 10 video solutions for Find All Possible Stable Binary Arrays II, a hard level problem involving Dynamic Programming, Prefix Sum. This walkthrough by codestorywithMIK has 10,872 views views. Want to try solving it yourself? Practice on FleetCode or read the detailed text solution.
Problem Statement
You are given 3 positive integers zero, one, and limit.
A binary array arr is called stable if:
- The number of occurrences of 0 in
arris exactlyzero. - The number of occurrences of 1 in
arris exactlyone. - Each subarray of
arrwith a size greater thanlimitmust contain both 0 and 1.
Return the total number of stable binary arrays.
Since the answer may be very large, return it modulo 109 + 7.
Example 1:
Input: zero = 1, one = 1, limit = 2
Output: 2
Explanation:
The two possible stable binary arrays are [1,0] and [0,1].
Example 2:
Input: zero = 1, one = 2, limit = 1
Output: 1
Explanation:
The only possible stable binary array is [1,0,1].
Example 3:
Input: zero = 3, one = 3, limit = 2
Output: 14
Explanation:
All the possible stable binary arrays are [0,0,1,0,1,1], [0,0,1,1,0,1], [0,1,0,0,1,1], [0,1,0,1,0,1], [0,1,0,1,1,0], [0,1,1,0,0,1], [0,1,1,0,1,0], [1,0,0,1,0,1], [1,0,0,1,1,0], [1,0,1,0,0,1], [1,0,1,0,1,0], [1,0,1,1,0,0], [1,1,0,0,1,0], and [1,1,0,1,0,0].
Constraints:
1 <= zero, one, limit <= 1000
Approach Overview
Problem Overview: Count the number of binary arrays that use exactly zero zeros and one ones while ensuring no more than limit identical values appear consecutively. The result must include every valid arrangement that satisfies both the count constraint and the stability rule.
Approach 1: Hash Map Memoization (Top-Down DP) (Time: O(zero * one * limit), Space: O(zero * one * limit))
Model the problem as dynamic programming with memoization. Each state represents how many zeros and ones remain, what the last placed value was, and how long the current streak is. A recursive function explores placing either a 0 or 1 if doing so does not exceed the limit. Results for states like (z, o, last, streak) are cached in a hash map so repeated subproblems are computed once. This avoids exponential recursion and turns the search into a bounded DP over all valid states. The approach is straightforward to implement and clearly expresses the constraint logic, making it a good first solution when exploring dynamic programming problems with state transitions.
Approach 2: Sorting + Prefix Sum Optimized DP (Time: O((zero + one) log (zero + one) + zero * one), Space: O(zero * one))
A more optimized solution focuses on counting sequences without explicitly tracking every streak length. Instead of iterating over each possible extension one step at a time, prefix sums aggregate counts of valid transitions. You compute how many ways exist to end with a zero or one for each combination of used zeros and ones. Prefix ranges help subtract invalid configurations where the streak would exceed limit. Sorting can be used to normalize block expansions and process transitions in consistent order while prefix sums enable constant-time range queries. This reduces the number of repeated calculations and converts the naive streak iteration into efficient range updates, a common optimization technique in prefix sum based DP counting problems.
The DP table typically tracks two states: arrays ending in 0 and arrays ending in 1. For each cell, transitions add valid contributions from previous cells while subtracting sequences that would violate the consecutive limit. Prefix sums make these additions and removals constant-time instead of iterating over every possible streak length.
Recommended for interviews: Start with the memoized DP using a hash map to show you understand the state space and constraint handling. Interviewers usually expect the optimized DP with prefix sums afterward because it improves performance from streak-based iteration to aggregated transitions. Demonstrating both approaches shows strong command of DP state design and optimization techniques.
Complexity Analysis
| Approach | Time | Space | When to Use |
|---|---|---|---|
| Hash Map Memoized DP | O(zero * one * limit) | O(zero * one * limit) | When building the solution step‑by‑step or explaining the full DP state in interviews |
| Sorting + Prefix Sum DP | O((zero + one) log (zero + one) + zero * one) | O(zero * one) | Preferred optimized solution when counting large numbers of sequences efficiently |