**Dijkstra's algorithm - Wikipedia, the free encyclopedia**

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**Dijkstra's algorithm - Wikipedia, the free encyclopedia**

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**Dijkstra's algorithm for shortest paths « Python recipes « ActiveState Code**

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Dijkstra(G,s) finds all shortest paths from s to each other vertex in the graph, and shortestPath(G,s,t) uses Dijkstra to find the shortest path from s to t. Uses the priorityDictionary data structure (Recipe 117228) to keep track of estimated distances to each vertex.

Python, 87 lines"

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**Lecture 10 - Dynamic Programming - YouTube**

**Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure | CosmoLearning Computer Science**

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**Optimal substructure - Wikipedia, the free encyclopedia**

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**Redesigning Google: how Larry Page engineered a beautiful revolution | The Verge**

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**Needleman-Wunsch Algorithm for Global Sequence Alignment in Python - CodesOfMyLife**

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**Sequence alignment - Wikipedia, the free encyclopedia**

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**python - Breadth-first search trace path - Stack Overflow**

graph = {

'1': ['2', '3', '4'],

'2': ['5', '6'],

'5': ['9', '10'],

'4': ['7', '8'],

'7': ['11', '12']

}

def bfs(graph, start, end):

# maintain a queue of paths

queue = []

# push the first path into the queue

queue.append([start])

while queue:

# get the first path from the queue

path = queue.pop(0)

# get the last node from the path

node = path[-1]

# path found

if node == end:

return path

# enumerate all adjacent nodes, construct a new path and push it into the queue

for adjacent in graph.get(node, []):

new_path = list(path)

new_path.append(adjacent)

queue.append(new_path)

print bfs(graph, '1', '11')"

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