LeetCode #2120 — MEDIUM

Execution of All Suffix Instructions Staying in a Grid

Move from brute-force thinking to an efficient approach using core interview patterns strategy.

Solve on LeetCode
The Problem

Problem Statement

There is an n x n grid, with the top-left cell at (0, 0) and the bottom-right cell at (n - 1, n - 1). You are given the integer n and an integer array startPos where startPos = [startrow, startcol] indicates that a robot is initially at cell (startrow, startcol).

You are also given a 0-indexed string s of length m where s[i] is the ith instruction for the robot: 'L' (move left), 'R' (move right), 'U' (move up), and 'D' (move down).

The robot can begin executing from any ith instruction in s. It executes the instructions one by one towards the end of s but it stops if either of these conditions is met:

  • The next instruction will move the robot off the grid.
  • There are no more instructions left to execute.

Return an array answer of length m where answer[i] is the number of instructions the robot can execute if the robot begins executing from the ith instruction in s.

Example 1:

Input: n = 3, startPos = [0,1], s = "RRDDLU"
Output: [1,5,4,3,1,0]
Explanation: Starting from startPos and beginning execution from the ith instruction:
- 0th: "RRDDLU". Only one instruction "R" can be executed before it moves off the grid.
- 1st:  "RDDLU". All five instructions can be executed while it stays in the grid and ends at (1, 1).
- 2nd:   "DDLU". All four instructions can be executed while it stays in the grid and ends at (1, 0).
- 3rd:    "DLU". All three instructions can be executed while it stays in the grid and ends at (0, 0).
- 4th:     "LU". Only one instruction "L" can be executed before it moves off the grid.
- 5th:      "U". If moving up, it would move off the grid.

Example 2:

Input: n = 2, startPos = [1,1], s = "LURD"
Output: [4,1,0,0]
Explanation:
- 0th: "LURD".
- 1st:  "URD".
- 2nd:   "RD".
- 3rd:    "D".

Example 3:

Input: n = 1, startPos = [0,0], s = "LRUD"
Output: [0,0,0,0]
Explanation: No matter which instruction the robot begins execution from, it would move off the grid.

Constraints:

  • m == s.length
  • 1 <= n, m <= 500
  • startPos.length == 2
  • 0 <= startrow, startcol < n
  • s consists of 'L', 'R', 'U', and 'D'.

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: There is an n x n grid, with the top-left cell at (0, 0) and the bottom-right cell at (n - 1, n - 1). You are given the integer n and an integer array startPos where startPos = [startrow, startcol] indicates that a robot is initially at cell (startrow, startcol). You are also given a 0-indexed string s of length m where s[i] is the ith instruction for the robot: 'L' (move left), 'R' (move right), 'U' (move up), and 'D' (move down). The robot can begin executing from any ith instruction in s. It executes the instructions one by one towards the end of s but it stops if either of these conditions is met: The next instruction will move the robot off the grid. There are no more instructions left to execute. Return an array answer of length m where answer[i] is the number of instructions the robot can execute if the robot begins executing from the ith instruction in s.

Baseline thinking

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

Pattern signal: General problem-solving

Example 1

3
[0,1]
"RRDDLU"

Example 2

2
[1,1]
"LURD"

Example 3

1
[0,0]
"LRUD"

Related Problems

  • Out of Boundary Paths (out-of-boundary-paths)
  • Robot Return to Origin (robot-return-to-origin)
Step 02

Core Insight

What unlocks the optimal approach

  • The constraints are not very large. Can we simulate the execution by starting from each index of s?
  • Before any of the stopping conditions is met, stop the simulation for that index and set the answer for that index.
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 #2120: Execution of All Suffix Instructions Staying in a Grid
class Solution {
    public int[] executeInstructions(int n, int[] startPos, String s) {
        int m = s.length();
        int[] ans = new int[m];
        Map<Character, int[]> mp = new HashMap<>(4);
        mp.put('L', new int[] {0, -1});
        mp.put('R', new int[] {0, 1});
        mp.put('U', new int[] {-1, 0});
        mp.put('D', new int[] {1, 0});
        for (int i = 0; i < m; ++i) {
            int x = startPos[0], y = startPos[1];
            int t = 0;
            for (int j = i; j < m; ++j) {
                char c = s.charAt(j);
                int a = mp.get(c)[0], b = mp.get(c)[1];
                if (0 <= x + a && x + a < n && 0 <= y + b && y + b < n) {
                    x += a;
                    y += b;
                    ++t;
                } else {
                    break;
                }
            }
            ans[i] = t;
        }
        return 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(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.