BSD-3-Clause licensed by Devon Hollowood
This version can be pinned in stack with:search-algorithms-0.3.2@sha256:9d224b9c6b5875598e6fc91497a178f3ca6e45768c637d07d4f874e6211a331b,2203

Module documentation for 0.3.2

Depends on 2 packages(full list with versions):


Haskell library containing common graph search algorithms

Build Status

Lots of problems can be modeled as graphs, but oftentimes one doesn’t want to use an explicit graph structure to represent the problem. Maybe the graph would be too big (or is infinite), maybe making an explicit graph is unwieldy for the problem at hand, or maybe one just wants to generalize over graph implementations. That’s where this library comes in: this is a collection of generalized search algorithms, so that one doesn’t have to make the graphs explicit. In general, this means that one provides each search function with a function to generate neighboring states, possibly some functions to generate additional information for the search, a predicate which tells when the search is complete, and an initial state to start from. The result is a path from the initial state to a “solved” state, or Nothing if no such path is possible.


Documentation is hosted on Hackage.


This library shares a similar functionality with the astar library (which I was unaware of when I released the first version of this library). astar‘s interface has since influenced the development of this library’s interface, and this library owes a debt of gratitude to astar for that reason.


Change-making problem

import Algorithm.Search (bfs)

countChange target = bfs (add_one_coin `pruning` (> target)) (== target) 0
    add_one_coin amt = map (+ amt) coins
    coins = [1, 5, 10, 25]

-- countChange gives the subtotals along the way to the end:
-- >>> countChange 67
-- Just [1, 2, 7, 17, 42, 67]

Simple directed acyclic graph:

import Algorithm.Search (dfs)
import qualified Data.Map as Map

graph = Map.fromList [
  (1, [2, 3]),
  (2, [4]),
  (3, [4]),
  (4, [])

-- Run dfs on the graph:
-- >>> dfs (graph Map.!) (== 4) 1
-- Just [3,4]

Using A* to find a path in an area with a wall:

import Algorithm.Search (aStar)

taxicabNeighbors :: (Int, Int) -> [(Int, Int)]
taxicabNeighbors (x, y) = [(x, y + 1), (x - 1, y), (x + 1, y), (x, y - 1)]

isWall :: (Int, Int) -> Bool
isWall (x, y) = x == 1 && (-2) <= y && y <= 1

taxicabDistance :: (Int, Int) -> (Int, Int) -> Int
taxicabDistance (x1, y1) (x2, y2) = abs (x2 - x1) + abs (y2 - y1)

findPath :: (Int, Int) -> (Int, Int) -> Maybe (Int, [(Int, (Int, Int))])
findPath start end =
  let next = taxicabNeighbors
      cost = taxicabDistance
      remaining = (taxicabDistance end)
  in aStar (next `pruning` isWall) cost remaining (== end) start

-- findPath p1 p2 finds a path between p1 and p2, avoiding the wall
-- >>> findPath (0, 0) (2, 0)
-- Just (6,[(0,1),(0,2),(1,2),(2,2),(2,1),(2,0)])
-- This correctly goes up and around the wall



All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

0.3.2 - 2021-12-27

  • Add two new functions, dijkstraAssoc and aStarAssoc. These allow for the simultaneous computation of neighboring states and their costs. (Thank you to nagydani)

0.3.1 - 2010-08-19

  • Dependencies version bump

0.3.0 - 2017-11-29


  • Monadic versions of search algorithms and helper functions

0.2.0 - 2017-05-13


  • BREAKING CHANGE: Simplified return type of dijkstra and aStar.
    • This should make these functions more ergonomic.
    • Introduced new incrementalCosts function to compensate.
  • BREAKING CHANGE: Replaced searches’ prunes arguments with pruning combinator.
  • BREAKING CHANGE: Split searches’ next arguments into multiple arguments for dijkstra and aStar.
    • This should make these functions more ergonomic.
  • next arguments now only require a way of generating Foldables, instead of lists specifically.

0.1.0 - 2017-03-07

  • Initial release