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This approach leverages the concept of "Manhattan distance" coupled with the "parity check" to determine if the cell can be reached in exactly t
steps.
The minimum number of steps required to reach from (sx, sy) to (fx, fy) in a grid is the maximum of the horizontal and vertical distances between these points, known as Chebyshev distance. If this minimal distance is greater than t
, the function should return false instantly.
Moreover, if the difference between t
and this minimum distance is even, the extra steps required can effectively be taken by oscillating on the grid or taking additional allowable steps. If not, reaching in t
steps becomes impossible.
Time Complexity: O(1), as it involves simple arithmetic operations.
Space Complexity: O(1), as no extra space is used.
Solve with full IDE support and test cases
1def is_cell_reachable(sx, sy, fx, fy, t):
2 min_steps = max(abs(fx - sx), abs(fy - sy))
3 return min_steps <= t and (t - min_steps) % 2 == 0
The Python function computes the required minimum steps and parity check using simple built-in functions and returns True or False based on whether it can reach in exactly t
steps.
This approach models the grid and implements a Breadth-First Search (BFS) to simulate traversing from the starting point to check if reaching precisely at t
seconds is possible.
Instead of directly computing the distance, this method carries out a brute-force simulation of the movement, exploring all potential paths using a FIFO queue structure inherent to BFS algorithms. The goal is to maintain a count of steps and analyze pathways that may uniquely cover the grid in t
steps.
Time Complexity: Highly complex, typically O(n^2).
Space Complexity: Also complex, with decisions based on array allocations with a theoretical upper limit.
1function isCellReachable(sx, sy, fx, fy, t) {
2 const queue = [[sx, sy, 0]];
3 const directions =
In this JavaScript interpretation, the BFS similarly adopts queuing for determining reachability by methodically scanning point possibilities. The function carries through potential locations step-by-step, comparing against the target parameters directly.