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The goal could be efficiently approached by reversing the operations. Instead of trying to reach (tx, ty) from (sx, sy), we attempt to determine whether (sx, sy) can be reached from (tx, ty) using reverse operations. This is achieved by repeatedly applying the inverse operations: (x, x+y) can be reverted to (x, y), and (x+y, y) can be reverted to (x, y). The main idea is to use modulus operations when x != y.
Time Complexity: O(log(max(tx, ty)))
Space Complexity: O(1)
1var reachingPoints = function(sx, sy, tx, ty) {
2 while (tx > sx && ty > sy && tx != ty) {
3 if (tx > ty) tx %= ty;
4 else ty %= tx;
5 }
6 if (tx < sx || ty < sy) return false;
7 return tx == sx ? (ty - sy) % sx === 0 : (tx - sx) % sy === 0;
8};JavaScript's implementation uses the same procedure utilizing looping and modulus operations. Such steps ensure the point reduction leveraging both language flexibilities and efficient conditional checks.
Another strategy involves recursive backtracking, where the function makes recursive calls to simulate both directions (x + y, y) and (x, x + y) to reach the target point (tx, ty) from the start point (sx, sy). Although less efficient compared to the reverse operation method due to its depth, it's an introductory way to explore the possibilities.
Time Complexity: Exponential in the worst case
Space Complexity: Recursive stack size
1
The Python recursive approach pursues feasibility by exploring both possible moves repeatedly until the base condition (goal reached or surpassed) is met. It clearly suffers from larger-than-efficient computation with high endpoint values due to the non-optimal nature of recursive exploration.