Here we are given a set of points and start by computing distances between them; those will be our edge weights. If you already have edge weights then this preprocessing step is not needed. It is by far the bottleneck in the code below; the rest is a fraction of a second on 1000 vertices. Also I did not try to optimize the preprocessing (let alone the non-bottleneck parts) for speed e.g. using packable arrays and Compile.
So here is what might be a correct implementation of Prim's algorithm.
--- edit ---
I am replacing what I had with a corrected version, based on findings by the original poster. It uses a binary heap so I need code for that as well.
It will return the total length of the spanning tree as well as the list of edges. As before it takes a list of points in the plane, and the graph is by assumption the complete graph with vertices being the points and edge weights being the distance between the points. One can modify for other graphs without too much trouble.
I also coded in a slightly (more than usual) awkward way. The intent is to make it less difficult to use Compile. I do not have time to pursue that myself. As it stands, this seems to be around 4x slower than the Kruskal implementation I showed. At least the asymptotics look about right.
heapbottompointer = 1;
heaptoppointer = 2;
makeHeap[len_, elemsize_] := ConstantArray[0., {len + 1, elemsize}]
Clear[addToHeap, removeFromHeap]
SetAttributes[addToHeap, HoldFirst];
addToHeap[heap_, elem : {_Real, __}] := Module[
{j1, j2},
heap[[heapbottompointer, 1]] += 1;
j1 = heap[[heapbottompointer, 1]];
heap[[j1 + 1]] = elem;
While[(j2 = Floor[j1/2]) >= 1 &&
heap[[j2 + 1, 1]] > heap[[j1 + 1, 1]],
heap[[{j1 + 1, j2 + 1}]] = heap[[{j2 + 1, j1 + 1}]];
j1 = j2;
];
]
SetAttributes[removeFromHeap, HoldFirst];
removeFromHeap[heap_] := Module[
{prev = 2, j1 = 2, j2 = 3, top = heap[[heaptoppointer]], last, next},
last = heap[[heapbottompointer, 1]];
While[j1 <= last,
If[j2 <= last,
next = If[heap[[j1 + 1, 1]] <= heap[[j2 + 1, 1]], j1, j2],
next = j1
];
heap[[prev]] = heap[[next + 1]];
prev = next + 1;
{j1, j2} = 2*next + {0, 1};
];
heap[[heapbottompointer, 1]] -= 1;
heap[[prev]] = heap[[last + 1]];
j1 = prev - 1;
While[(j2 = Floor[j1/2]) >= 1 &&
heap[[j2 + 1, 1]] > heap[[j1 + 1, 1]],
heap[[{j1 + 1, j2 + 1}]] = heap[[{j2 + 1, j1 + 1}]];
j1 = j2;
];
top
]
Prim[pts_] := Module[
{edges, n = Length[pts], vert, heap, nedges = 1, tdist = 0.,
treelist, top, used, verts, addedges},
edges = Table[EuclideanDistance[pts[[i]], pts[[j]]], {i, n}, {j, n}];
used = ConstantArray[False, n];
used[[1]] = True;
heap = makeHeap[n^2, 3];
Do[
addToHeap[heap, {edges[[1, j]], 1., N[j]}], {j, 2, n}];
treelist = ConstantArray[{0, 0}, n - 1];
While[heap[[1, 1]] >= 1 && nedges <= n - 1,
top = removeFromHeap[heap];
verts = Round[Rest[top]];
If[used[[verts[[1]]]] && ! used[[verts[[2]]]] || !
used[[verts[[1]]]] && used[[verts[[2]]]],
If[! used[[verts[[1]]]] && used[[verts[[2]]]],
verts = Reverse[verts]];
used[[verts[[2]]]] = True;
tdist += top[[1]];
treelist[[nedges]] = verts;
nedges++;
addedges = edges[[verts[[2]]]];
Do[If[! used[[j]],
addToHeap[heap, {addedges[[j]], N[verts[[2]]], N[j]}]], {j,
n}];
]
];
{tdist, treelist}
]
Example:
n = 1000;
pts = RandomReal[{-10, 10}, {n, 2}];
Timing[tree = Prim[pts];]
(* {20.64, Null} *)
--- end edit ---
Alternatively, use Kruskal's algorithm. Same preprocessing as above is used here. Again, I beleive this is a correct implementation but caveat emptor.
Kruskal[pts_] := Module[
{n = Length[pts], vpairs, jj = 0, hh, pair, dist, c1, c2, c1c2},
Do[hh[k] = {k}, {k, n}];
vpairs =
Sort[Flatten[
Table[{(pts[[k]] - pts[[l]]).(pts[[k]] - pts[[l]]), {k, l}}, {k,
1, n - 1}, {l, k + 1, n}], 1]];
First[Last[Reap[While[jj < Length[vpairs], jj++;
{dist, pair} = vpairs[[jj]];
{c1, c2} = {hh[pair[[1]]], hh[pair[[2]]]};
If[c1 =!= c2, Sow[Apply[Rule, vpairs[[jj, 2]]]];
c1c2 = Union[c1, c2];
Do[hh[c1c2[[k]]] = c1c2, {k, Length[c1c2]}];
If[Length[hh[pair[[1]]]] == n, Break[]];];]]]]]
Timing[krus = Kruskal[pts];]
(* Out[65]= {5.090000, Null} *)
--- edit 2021-05-16 ---
A proper implementation of the "lazy" version of Prim's algorithm will use a priority queue. Which is what I used above. The "eager" version, which tends to be faster, needs an indexed priority queue. A proper version of Kruskal's algorithm (which I did not quite achieve above) will use a data structure called a merge-find set. I have reference implementations of these data structures, along with minimum spanning tree examples, in the Wolfram Function Repository.
PriorityQueue
IndexedQueue
MergeFindSet
None of these suffer from being blindingly fast. The merge-find set implementation of Kruskal's algorithm indeed seems to be slightly slower than the more naive implementation in this note. I do not know offhand what might be the bottleneck(s). Testing, as those the function pages will show, indicates at least that the asymptotics are as expected.
--- end edit ---
