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A* Algorithm: Conceptual

Dijkstra’s algorithm is great for finding the shortest distance from a start vertex to all other vertices in the graph. However, it is not the best when we are just looking for the shortest distance from a single start vertex to a single end vertex. Let’s think about this.

Dijkstra’s algorithm considers **ALL** neighboring vertices and simply pops the vertex with the shortest distance calculated so far from the queue. As a refresher, here’s the pseudocode for Dijkstra’s Algorithm:
```pseudo
create dictionary to map vertices to their distance from start vertex

assign start vertex a distance of 0 in min heap

assign every other vertex a distance of infinity in min heap

remove the vertex with the minimum distance from the min heap and set it to the current vertex

while min heap is not empty:
  for each current vertex:
    for each neighbor in neighbors:
    new distance = (distance to current vertex) + (edge weight of current vertex to neighbor)

    if new distance is less than its current distance:
      current distance = new distance

return distances
```

However, considering vertices that take us away from our goal might be a waste of time. For instance, take a look at the graph on the left. 

What if we modeled all the train stations in North America using a graph and we were looking for the shortest train distance from New York to Los Angeles?

We shouldn’t consider train stations in Canada because it would be inefficient to look in that direction. However, as shown in the graph on the left, Dijkstra’s looks in all directions, and the algorithm will check all neighboring stations. 

Luckily, there are modifications we can make to Dijkstra's Algorithm. Let's see how we can optimize it!

A map shows the different train lines that can be taken from New York to various locations across North America.

Dijkstra's Isn't Always the Best...

 Rather than simply checking the distance up to the current vertex, we will check the distance up to the current vertex + the estimated distance from the current vertex to the end vertex. We call this estimated distance the _heuristic_. For instance, in a graph where the vertices are cities, and the edges are roads, we can estimate the distance between two cities through a distance formula:

```tex
d = \sqrt {\left( {x_1 - x_2 } \right)^2 + \left( {y_1 - y_2 } \right)^2 }
```

There are only a few important changes you will need to make to Dijkstra's algorithm to turn it into a star of an algorithm:

1. **Add a target for the search.** The new algorithm cannot optimize with a heuristic unless it has a clear destination.
2. **Gather possible optimal paths and identify a single shortest path.** You want to find a path that has the shortest distance for the least cost.
3. **Implement a heuristic that determines the likely distance remaining.** The main difference between Dijkstra's and this new algorithm is that this one knows which direction to head in.

This new algorithm is called **_A\*_**. It's sometimes referred to as a _greedy algorithm_ because it makes a locally optimal choice at every vertex. The heuristic that A\* uses makes it an introductory example of artificial intelligence!

In fact, going back to our train example. With A\*, the search would be visualized as the following:

![A* Train Gif](https://content.codecademy.com/programs/cs-path/a*%20conceptual/atrain.gif "A Star Train Gif")


What is A*?

- The A\* algorithm is a greedy graph search algorithm that optimizes looking for a target vertex.
- A\* is a modification of Dijkstra’s done by adding the estimated distance of each vertex to the goal vertex when searching.
- We can modify Dijkstra's and turn it into A\* by changing the following:
 - Adding a target for the search.
 - Gathering possible optimal paths and identify a single shortest path.
 - Implementing a heuristic that determines the likely distance remaining.
- The runtime of A\* is O(b<sup>d</sup>) where `b` is the branching factor of the graph and `d` is the depth of the goal vertex from the start vertex.
- A\* is an introductory glimpse into artificial intelligence.


Review of A*

In A\*, we have a goal vertex. Thus, the algorithm is optimized such that in most graphs, every vertex will **NOT** be searched, so let’s think about how fast this algorithm runs.

Take a look at the graph.

Let’s say `A` is the start vertex and `G` is the end vertex. Through A\*, we will not be searching many of the vertices left of `A`, so describing the runtime as the same as  Dijkstra’s is not very descriptive. Remember that Dijkstra’s has a runtime of `O((V+E) log V)` because in the worst case you search every vertex and go through all the edges while storing data in a min-heap.

For A\*, let’s try describing the runtime in better terms. Using the above graph, let’s call `b` the branching factor of the graph. The branching factor is the _average_ number of edges per vertex in the graph. In this case, **on average**, every vertex has 2 edges, and thus, the branching factor is equal to 2. Let's call `d` the depth of the goal vertex from the start vertex. Because the goal vertex is 3 edges away from the start vertex, `d` is equal to 3.

In the worst case, we would look at all of the edges in the direction of the goal vertex until we reach the goal vertex. We would look at  `b` edges for every vertex in our search for close to `d` iterations. Thus, the runtime is **O(b<sup>d</sup>)**.
