Use PriorityQueue for Dijkstra's, requires Hash for Vertex

src/algorithms.rs

@@ -28,30 +28,14 @@
 //! [Depth-first search]: https://en.wikipedia.org/wiki/Depth-first_search
 //! [Dijkstra's algorithm]: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
 
-use std::cmp::Ordering;
-use std::collections::{BinaryHeap, VecDeque};
+use priority_queue::PriorityQueue;
+use std::cmp::Reverse;
+use std::collections::VecDeque;
+use std::hash::Hash;
 
 use crate::maps::EntityMap;
 use crate::traits::{GraphTopology, IncidenceCursor};
 
-#[derive(PartialEq, Eq)]
-struct DistanceOrderedVertex<V> {
-    distance: u32,
-    vertex: V,
-}
-
-impl<V: Eq> PartialOrd for DistanceOrderedVertex<V> {
-    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
-        Some(self.cmp(other))
-    }
-}
-
-impl<V: Eq> Ord for DistanceOrderedVertex<V> {
-    fn cmp(&self, other: &Self) -> Ordering {
-        other.distance.cmp(&self.distance)
-    }
-}
-
 /// Return data type for [`dijkstra`] and [`dijkstra_unweighted`].
 pub struct DijkstraResult<V: Copy> {
     /// Vertex map of minimum distances from a given `source` vertex.
@@ -61,7 +45,7 @@ pub struct DijkstraResult<V: Copy> {
 }
 
 // TODO: Generalize the return type of the weight function.
-// TODO: Add complexity information for Dijkstra's algorithm variants.
+// TODO: A Fibonacci heap would lower complexity to O(|E| + |V| log |V|) by making decrease-key O(1) amortized instead of O(log |V|). No standard Rust implementation exists; high constant factors may negate the asymptotic gain in practice.
 /// [Dijkstra's algorithm] with custom edge weights, returns minimum distances and predecessors.
 ///
 /// Calculates the shortest paths from `source` to all vertices in `graph` with edge weights given
@@ -69,6 +53,8 @@ pub struct DijkstraResult<V: Copy> {
 /// of each vertex on some shortest path from `source` to that vertex. Returns `None` for any vertex
 /// not connected to `source`.
 ///
+/// Time complexity is *O((|V| + |E|) log |V|)*, space complexity is *O(|V|)*.
+///
 /// # Panics
 ///
 /// Panics if `source` is not a valid vertex of `graph`.
@@ -95,6 +81,7 @@ pub struct DijkstraResult<V: Copy> {
 pub fn dijkstra<G, W>(graph: &G, source: G::Vertex, weights: W) -> DijkstraResult<G::Vertex>
 where
     G: GraphTopology,
+    G::Vertex: Hash,
     W: Fn(G::Edge) -> u32,
 {
     let mut predecessors = graph.vertex_map(None);
@@ -113,6 +100,8 @@ where
 /// by `weights` function. Returns the distances from `source` to each vertex. Returns `None` for any
 /// vertex not connected to `source`.
 ///
+/// Time complexity is *O((|V| + |E|) log |V|)*, space complexity is *O(|V|)*.
+///
 /// # Panics
 ///
 /// Panics if `source` is not a valid vertex of `graph`.
@@ -142,6 +131,7 @@ pub fn dijkstra_distances<G, W>(
 ) -> EntityMap<G::Vertex, Option<u32>>
 where
     G: GraphTopology,
+    G::Vertex: Hash,
     W: Fn(G::Edge) -> u32,
 {
     dijkstra_impl(graph, source, weights, |_, _| {})
@@ -153,6 +143,8 @@ where
 /// the distances from `source` to each vertex, and the predecessors of each vertex on some shortest
 /// path from `source` to that vertex. Returns `None` for any vertex not connected to `source`.
 ///
+/// Time complexity is *O((|V| + |E|) log |V|)*, space complexity is *O(|V|)*.
+///
 /// Prefer [`bfs`] for unweighted graphs, it has better time and space complexity.
 ///
 /// # Panics
@@ -179,6 +171,7 @@ where
 pub fn dijkstra_unweighted<G>(graph: &G, source: G::Vertex) -> DijkstraResult<G::Vertex>
 where
     G: GraphTopology,
+    G::Vertex: Hash,
 {
     dijkstra(graph, source, |_| 1)
 }
@@ -189,6 +182,8 @@ where
 /// the distances from `source` to each vertex. Returns `None` for any vertex not connected to
 /// `source`.
 ///
+/// Time complexity is *O((|V| + |E|) log |V|)*, space complexity is *O(|V|)*.
+///
 /// Prefer [`bfs_distances`] for unweighted graphs, it has better time and space complexity.
 ///
 /// # Panics
@@ -217,6 +212,7 @@ pub fn dijkstra_distances_unweighted<G>(
 ) -> EntityMap<G::Vertex, Option<u32>>
 where
     G: GraphTopology,
+    G::Vertex: Hash,
 {
     dijkstra_distances(graph, source, |_| 1)
 }
@@ -229,32 +225,27 @@ fn dijkstra_impl<G, W, F>(
 ) -> EntityMap<G::Vertex, Option<u32>>
 where
     G: GraphTopology,
+    G::Vertex: Hash,
     W: Fn(G::Edge) -> u32,
     F: FnMut(G::Vertex, G::Vertex),
 {
     let mut distances = graph.vertex_map(None);
-    let mut heap = BinaryHeap::new();
+    let mut heap = PriorityQueue::new();
 
     distances[source] = Some(0);
-    heap.push(DistanceOrderedVertex {
-        vertex: source,
-        distance: 0,
-    });
+    heap.push(source, Reverse(0u32));
 
-    while let Some(v) = heap.pop() {
-        for incidence in graph.incidences(v.vertex) {
-            let new_distance = distances[v.vertex].unwrap() + weights(incidence.1);
+    while let Some((v, Reverse(v_distance))) = heap.pop() {
+        for incidence in graph.incidences(v) {
+            let new_distance = v_distance + weights(incidence.1);
             if match distances[incidence.0] {
                 None => true,
                 Some(old_distance) if old_distance > new_distance => true,
                 _ => false,
             } {
                 distances[incidence.0] = Some(new_distance);
-                on_relax(incidence.0, v.vertex);
-                heap.push(DistanceOrderedVertex {
-                    vertex: incidence.0,
-                    distance: new_distance,
-                });
+                on_relax(incidence.0, v);
+                heap.push_increase(incidence.0, Reverse(new_distance));
             }
         }
     }