LeetCode #2059 — MEDIUM

Minimum Operations to Convert Number

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

You are given a 0-indexed integer array nums containing distinct numbers, an integer start, and an integer goal. There is an integer x that is initially set to start, and you want to perform operations on x such that it is converted to goal. You can perform the following operation repeatedly on the number x:

If 0 <= x <= 1000, then for any index i in the array (0 <= i < nums.length), you can set x to any of the following:

  • x + nums[i]
  • x - nums[i]
  • x ^ nums[i] (bitwise-XOR)

Note that you can use each nums[i] any number of times in any order. Operations that set x to be out of the range 0 <= x <= 1000 are valid, but no more operations can be done afterward.

Return the minimum number of operations needed to convert x = start into goal, and -1 if it is not possible.

Example 1:

Input: nums = [2,4,12], start = 2, goal = 12
Output: 2
Explanation: We can go from 2 → 14 → 12 with the following 2 operations.
- 2 + 12 = 14
- 14 - 2 = 12

Example 2:

Input: nums = [3,5,7], start = 0, goal = -4
Output: 2
Explanation: We can go from 0 → 3 → -4 with the following 2 operations. 
- 0 + 3 = 3
- 3 - 7 = -4
Note that the last operation sets x out of the range 0 <= x <= 1000, which is valid.

Example 3:

Input: nums = [2,8,16], start = 0, goal = 1
Output: -1
Explanation: There is no way to convert 0 into 1.

Constraints:

  • 1 <= nums.length <= 1000
  • -109 <= nums[i], goal <= 109
  • 0 <= start <= 1000
  • start != goal
  • All the integers in nums are distinct.

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 a 0-indexed integer array nums containing distinct numbers, an integer start, and an integer goal. There is an integer x that is initially set to start, and you want to perform operations on x such that it is converted to goal. You can perform the following operation repeatedly on the number x: If 0 <= x <= 1000, then for any index i in the array (0 <= i < nums.length), you can set x to any of the following: x + nums[i] x - nums[i] x ^ nums[i] (bitwise-XOR) Note that you can use each nums[i] any number of times in any order. Operations that set x to be out of the range 0 <= x <= 1000 are valid, but no more operations can be done afterward. Return the minimum number of operations needed to convert x = start into goal, and -1 if it is not possible.

Baseline thinking

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

Pattern signal: Array

Example 1

[2,4,12]
2
12

Example 2

[3,5,7]
0
-4

Example 3

[2,8,16]
0
1

Related Problems

  • Minimum Operations to Reduce X to Zero (minimum-operations-to-reduce-x-to-zero)
Step 02

Core Insight

What unlocks the optimal approach

  • Once x drops below 0 or goes above 1000, is it possible to continue performing operations on x?
  • How can you use BFS to find the minimum operations?
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
Upper-end input sizes
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 #2059: Minimum Operations to Convert Number
class Solution {
    public int minimumOperations(int[] nums, int start, int goal) {
        IntBinaryOperator op1 = (x, y) -> x + y;
        IntBinaryOperator op2 = (x, y) -> x - y;
        IntBinaryOperator op3 = (x, y) -> x ^ y;
        IntBinaryOperator[] ops = {op1, op2, op3};
        boolean[] vis = new boolean[1001];
        Queue<int[]> queue = new ArrayDeque<>();
        queue.offer(new int[] {start, 0});
        while (!queue.isEmpty()) {
            int[] p = queue.poll();
            int x = p[0], step = p[1];
            for (int num : nums) {
                for (IntBinaryOperator op : ops) {
                    int nx = op.applyAsInt(x, num);
                    if (nx == goal) {
                        return step + 1;
                    }
                    if (nx >= 0 && nx <= 1000 && !vis[nx]) {
                        queue.offer(new int[] {nx, step + 1});
                        vis[nx] = true;
                    }
                }
            }
        }
        return -1;
    }
}
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(1)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
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.