[apnorton]

The article doesn't explicitly state it in this manner in one concise place, but the way I would always think about A* from a "practical/easy-to-remember" perspective back when I was doing competitive programming is that they're all the same algorithm, but with different priorities on the priority queue:

Breadth-first Search: Priority is order of discovery of edges (that is, no priority queue/just a regular queue)

Dijkstra: Priority is distance so far + next edge distance

A*: Priority is distance so far + next edge distance + estimate of distance to target node.

This also helps me remember whether the estimate must over- or under-estimate: Since Dijkstra is making the estimate "0", clearly the "admissible heuristic" criteria must be an under-estimation.

[nostrademons]

Another way I like to think about it is that every graph traversal can be represented as a white set of unknown nodes, a grey set of known but unvisited nodes, and a black set of visited nodes. The data structure used to represent the grey set defines the algorithm:

DFS = queue

BFS = stack

Dijstra's = priority queue keyed by edge weight

A* = priority queue with heuristic function

Beam search = bounded priority queue with heuristic function

Topological sort = priority queue keyed by number of unvisited inbound edges

Copying garbage collector = pointer address

Mark & sweep garbage collector = dirty bit on the object pointed to

Generational garbage collector = multi-level grey set represented by the write barriers between each generation.

[a-dub]

A* = priority queue with heuristic function _with specific properties_ ... in particular it must be an "admissible" hueristic that never overestimates the true value.

[anitil]

This is very insightful and a handy mental model I'll be using thanks. A small nit is that I think you have DFS and BFS swapped?

[salamanderman]

Breadth-first is a queue. Depth-first is a stack. A* is a priority queue.

[cornstalks]

More specifically Dijkstra's is a priority queue. A* is a priority queue with an added estimate to the cost function to prioritize searching nodes closer to the destination.

[JohnKemeny]

OP's point is that

· BFS is priority queue with key h(n) + g(n), where h(n) = 0, g(n) = #edges

· Dijkstra's is priority queue with key h(n) + g(n), where h(n) = 0, g(n) = sum over edges

· A* is priority queue with key h(n) + g(n), where h(n) = heuristic(n), g(n) = sum over edges

It's cute.

[dataflow]

> This also helps me remember whether the estimate must over- or under-estimate: Since Dijkstra is making the estimate "0", clearly the "admissible heuristic" criteria must be an under-estimation.

You're thinking too hard. :-) Just think of a map the same way a 10-year-old would.

Straight-line (Euclidean) distance is the most obvious heuristic on a map for estimating distance, and it's admissible.

Straight lines minimize distances i.e. they never overestimate. Which is enough to remind you that you want an underestimate.

> the way I would always think about A* from a "practical/easy-to-remember" perspective back when I was doing competitive programming is that they're all the same algorithm, but with different priorities on the priority queue

A much less obvious but much more fascinating (IMO) way to look at A* is that A* is actually Dijkstra but with a modified graph, where you adjust the heuristic delta between each edge's vertices to the edge's weight.

To remember the sign of the adjustment with this method, just imagine the next vertex getting super close to the destination, and then work out whether the weight needs to increase or decrease significantly in that case. (It needs to decrease, given you're getting closer to the destination.)

[awesome_dude]

Which algorithm should I apply:

I have no information other than the fact that my agent has a decision to make (left or right).

- DFS or BFS

I have some information about the cost of the decision.

- UCS or Djikstra's algorithm

I have some idea of the cost of the decision, and a rough idea which direction the goal is in.

- A star

As well as knowing the cost, and a rough idea of the direction, I also know that I have a uniform cost grid.

- Jump point search

[noqc]

I think probably the easiest way to remember under/over is just to remember that euclidean distance is a very common admissible heuristic.

[djmips]

Any other tips for competitive coding ie a book or source of wisdom in a similar vein?

[amy_petrik]

leetcode will strengthen the youngblood programmer for competitive matters such as competitions or interviews

[breckognize]

And depth first search is just a stack!

[dietr1ch]

Yes, but it doesn't come right away. When preferring deeper nodes for a moment you have a loop-safe Depth-first traversal, and then when simplifying things in case the Graph is a Tree you get your regular stack-based Depth-first traversal, in which if you settle for the first goal you get back a tail-call optimised DFS.

[tonyhart7]

well they all just loop???

[bcrosby95]

Simplifications help people memorize things but if you get too reductive it becomes useless.