add performance about dfs_iterative.py

Author: zhuj05

graph/dfs_iterative.py

@@ -1,4 +1,5 @@
 # Depth-First Search (DFS) - Iterative Approach(深度優先搜尋-迭代法)
+# O(1)
 def dfs_iterative(graph, start):
     # Initialize an empty set to keep track of visited nodes to avoid infinite loops
     visited = set()

graph/dfs_iterative_optimized.py

@@ -0,0 +1,41 @@
+def dfs_iterative_optimized(graph, start_node):
+    # Optimization 2: Check for empty graph
+    if not graph or start_node not in graph:
+        return []
+
+    visited = set()
+    # Optimization 3: Use a local list for faster access than global scope
+    stack = [start_node]
+    result = []
+    
+    # Avoid re-evaluating 'stack' length in every iteration
+    # Use 'while stack' as it's the most "Pythonic" and fastest
+    while stack:
+        node = stack.pop()
+        
+        if node not in visited:
+            visited.add(node)
+            result.append(node)
+            
+            # Optimization 4: List comprehension/Reversed filtering
+            # We push neighbors in reverse to maintain expected traversal order
+            neighbors = [n for n in graph.get(node, []) if n not in visited]
+            stack.extend(reversed(neighbors))
+            
+    return result
+
+# Define the graph using an adjacency list
+graph = {
+    'A': ['B', 'C'],
+    'B': ['A', 'D', 'E'],
+    'C': ['A', 'F'],
+    'D': ['B'],
+    'E': ['B', 'F'],
+    'F': ['C', 'E']
+}
+
+
+output = dfs_iterative_optimized(graph, 'A')
+print(f"DFS result: {output}")
+# output
+# DFS result: ['A', 'B', 'D', 'E', 'F', 'C']