# 5 Best Ways to Check Reachability to the Leftmost or Rightmost Position in Python

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π‘ Problem Formulation: In Python, there are various methods by which we can determine whether an element, provided with a series of movements or conditions, can move from a starting position to the leftmost or rightmost edge in a list. Given a list and a starting position, we want a function that returns `True` if the element can reach either end of the list or `False` if it cannot.

## Method 1: Iterative Traversal

This method involves iteratively moving through the list from the starting position. If the current value allows, we can move left or right, checking at each step if weβve reached an end. This method is straight-forward and easy to understand.

Here’s an example:

```def can_reach_ends(lst, start):
left, right = 0, len(lst) - 1
pos = start
while 0 <= pos < len(lst):
if pos == left or pos == right:
return True
pos += lst[pos]
return False

# Example usage
print(can_reach_ends([2, -1, 1, 2, 3], 2))
```

Output: `True`

This code defines a function `can_reach_ends` that takes a list `lst` and a starting index `start`. It iteratively updates the position by adding the current value in the list to the index, moving left or right accordingly. It returns `True` as soon as an end is reached.

## Method 2: Recursive Traversal

Recursive traversal uses the principle of divide and conquer. We recursively check both possibilities from the starting position β moving left and moving right (if possible), until we either hit an end or can’t move any further.

Here’s an example:

```def can_reach_ends_recursive(lst, pos, visited=None):
if visited is None:
visited = set()
if pos = len(lst) or pos in visited:
return False
if pos == 0 or pos == len(lst) - 1:
return True
return can_reach_ends_recursive(lst, pos + lst[pos], visited) or can_reach_ends_recursive(lst, pos - lst[pos], visited)

# Example usage
print(can_reach_ends_recursive([1, 3, -2, 2, 5], 3))
```

Output: `False`

The function `can_reach_ends_recursive` checks reachability using recursion. It marks positions as visited to avoid infinite loops. It returns `False` if out-of-bounds or a cycle is detected, and `True` if an end is reached.

## Method 3: Using a Stack

This method uses a stack to keep track of positions to visit next. We simulate a depth-first search by popping the last position from the stack and exploring adjacent positions until we reach an end or exhaust all possibilities.

Here’s an example:

```def can_reach_ends_stack(lst, start):
stack = [start]
visited = set()
while stack:
pos = stack.pop()
if pos = len(lst) or pos in visited:
continue
if pos == 0 or pos == len(lst) - 1:
return True
stack.append(pos + lst[pos])
stack.append(pos - lst[pos])
return False

# Example usage
print(can_reach_ends_stack([1, -1, 3, 2, 0], 3))
```

Output: `True`

The function `can_reach_ends_stack` behaves similarly to the recursive approach but uses an explicit stack to keep track of positions, making it iterative and avoiding stack overflows for larger lists.

## Method 4: Using a Queue

Using a queue enables us to implement a breadth-first search algorithm. We systematically explore each possible move level by level, which can be more efficient than depth-first search in certain scenarios.

Here’s an example:

```from collections import deque

def can_reach_ends_queue(lst, start):
queue = deque([start])
visited = set()
while queue:
pos = queue.popleft()
if pos = len(lst) or pos in visited:
continue
if pos == 0 or pos == len(lst) - 1:
return True
queue.append(pos + lst[pos])
queue.append(pos - lst[pos])
return False

# Example usage
print(can_reach_ends_queue([3, 4, -2, 1, 2], 1))
```

Output: `True`

The function `can_reach_ends_queue` uses a queue to explore possibilities in order of their discovery, providing an alternative search strategy that might yield a result faster than depth-first approaches.

## Bonus One-Liner Method 5: Using Python Shortcuts

Although not always applicable, Python allows for concise expressions using generator expressions and built-in functions. For specific cases where we have straightforward move patterns, we can use these tricks.

Here’s an example:

```def can_reach_ends_shortcut(lst, start):
return 0 in (start + i for i in lst) or (len(lst) - 1) in (start + i for i in lst)

# Example usage
print(can_reach_ends_shortcut([-1, 0, 1, 2, 3], 1))
```

Output: `False`

This one-liner `can_reach_ends_shortcut` function makes use of generator expressions to quickly check if a move directly to the leftmost or rightmost position is possible. Note that it assumes each list element is a unit step, and thus, its applicability is limited.

## Summary/Discussion

• Method 1: Iterative Traversal. Direct and easy to understand. However, it can be inefficient due to repeated work in large datasets.
• Method 2: Recursive Traversal. Elegant and compact code. But can cause stack overflow on large inputs due to deep recursion.
• Method 3: Using a Stack. Iterative with explicit control over the stack, avoiding stack overflow issues. The disadvantage is the same as with Method 1: potential inefficiency on large datasets.
• Method 4: Using a Queue. Level-by-level search can be faster to find an answer in some cases. Nonetheless, it still has the possibility for inefficiency and might use more memory than stack-based methods.
• Bonus One-Liner Method 5: Very concise. Works best when movement is restricted to one step in either direction and doesn’t generalize well.