LeetCode #1703 — HARD

Minimum Adjacent Swaps for K Consecutive Ones

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given an integer array, nums, and an integer k. nums comprises of only 0's and 1's. In one move, you can choose two adjacent indices and swap their values.

Return the minimum number of moves required so that nums has k consecutive 1's.

Example 1:

Input: nums = [1,0,0,1,0,1], k = 2
Output: 1
Explanation: In 1 move, nums could be [1,0,0,0,1,1] and have 2 consecutive 1's.

Example 2:

Input: nums = [1,0,0,0,0,0,1,1], k = 3
Output: 5
Explanation: In 5 moves, the leftmost 1 can be shifted right until nums = [0,0,0,0,0,1,1,1].

Example 3:

Input: nums = [1,1,0,1], k = 2
Output: 0
Explanation: nums already has 2 consecutive 1's.

Constraints:

  • 1 <= nums.length <= 105
  • nums[i] is 0 or 1.
  • 1 <= k <= sum(nums)
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given an integer array, nums, and an integer k. nums comprises of only 0's and 1's. In one move, you can choose two adjacent indices and swap their values. Return the minimum number of moves required so that nums has k consecutive 1's.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Greedy · Sliding Window

Example 1

[1,0,0,1,0,1]
2

Example 2

[1,0,0,0,0,0,1,1]
3

Example 3

[1,1,0,1]
2

Related Problems

  • Minimum Swaps to Group All 1's Together (minimum-swaps-to-group-all-1s-together)
  • Minimum Number of Operations to Make Array Continuous (minimum-number-of-operations-to-make-array-continuous)
  • Minimum Adjacent Swaps to Make a Valid Array (minimum-adjacent-swaps-to-make-a-valid-array)
Step 02

Core Insight

What unlocks the optimal approach

  • Choose k 1s and determine how many steps are required to move them into 1 group.
  • Maintain a sliding window of k 1s, and maintain the steps required to group them.
  • When you slide the window across, should you move the group to the right? Once you move the group to the right, it will never need to slide to the left again.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1703: Minimum Adjacent Swaps for K Consecutive Ones
class Solution {
    public int minMoves(int[] nums, int k) {
        List<Integer> arr = new ArrayList<>();
        int n = nums.length;
        for (int i = 0; i < n; ++i) {
            if (nums[i] != 0) {
                arr.add(i);
            }
        }
        int m = arr.size();
        int[] s = new int[m + 1];
        for (int i = 0; i < m; ++i) {
            s[i + 1] = s[i] + arr.get(i);
        }
        long ans = 1 << 60;
        int x = (k + 1) / 2;
        int y = k - x;
        for (int i = x - 1; i < m - y; ++i) {
            int j = arr.get(i);
            int ls = s[i + 1] - s[i + 1 - x];
            int rs = s[i + 1 + y] - s[i + 1];
            long a = (j + j - x + 1L) * x / 2 - ls;
            long b = rs - (j + 1L + j + y) * y / 2;
            ans = Math.min(ans, a + b);
        }
        return (int) ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n)
Space
O(m)

Approach Breakdown

EXHAUSTIVE
O(2ⁿ) time
O(n) space

Try every possible combination of choices. With n items each having two states (include/exclude), the search space is 2ⁿ. Evaluating each combination takes O(n), giving O(n × 2ⁿ). The recursion stack or subset storage uses O(n) space.

GREEDY
O(n log n) time
O(1) space

Greedy algorithms typically sort the input (O(n log n)) then make a single pass (O(n)). The sort dominates. If the input is already sorted or the greedy choice can be computed without sorting, time drops to O(n). Proving greedy correctness (exchange argument) is harder than the implementation.

Shortcut: Sort + single pass → O(n log n). If no sort needed → O(n). The hard part is proving it works.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Using greedy without proof

Wrong move: Locally optimal choices may fail globally.

Usually fails on: Counterexamples appear on crafted input orderings.

Fix: Verify with exchange argument or monotonic objective before committing.

Shrinking the window only once

Wrong move: Using `if` instead of `while` leaves the window invalid for multiple iterations.

Usually fails on: Over-limit windows stay invalid and produce wrong lengths/counts.

Fix: Shrink in a `while` loop until the invariant is valid again.