From fb13cf95c3853e91c95943393bbe423c21fd5d3a Mon Sep 17 00:00:00 2001
From: Bruno BELANYI <bruno@belanyi.fr>
Date: Mon, 25 Dec 2023 17:20:26 +0100
Subject: [PATCH] 2023: d25: ex1: add solution

---
 2023/d25/ex1/ex1.py | 125 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 125 insertions(+)
 create mode 100755 2023/d25/ex1/ex1.py

diff --git a/2023/d25/ex1/ex1.py b/2023/d25/ex1/ex1.py
new file mode 100755
index 0000000..8b35f11
--- /dev/null
+++ b/2023/d25/ex1/ex1.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+
+import copy
+import random
+import sys
+from collections import Counter, defaultdict
+from typing import cast
+
+Graph = dict[str, Counter[str]]
+
+
+def solve(input: list[str]) -> int:
+    def parse(input: list[str]) -> Graph:
+        res: Graph = defaultdict(Counter)
+
+        for line in input:
+            node, destinations = line.split(": ")
+            for dest in destinations.split():
+                res[node][dest] = 1
+                res[dest][node] = 1
+
+        return res
+
+    def contract_edge(graph: Graph, s: str, t: str) -> None:
+        assert s != t  # Sanity check
+        assert s in graph  # Sanity check
+        assert t in graph  # Sanity check
+
+        # Merge the edges
+        graph[s] += graph[t]
+        # Don't count the potential t-s edge
+        del graph[s][s]
+        # Remove t from the graph
+        for other in graph[t].keys():
+            del graph[other][t]
+        del graph[t]
+        # Update the neighbours values
+        for other, v in graph[s].items():
+            graph[other][s] = v
+
+    # Stoer-Wagner algorithm
+    def min_cut_exact(graph: Graph) -> tuple[set[str], set[str]]:
+        def phase(graph: Graph) -> tuple[str, str, int]:
+            assert len(graph) >= 2  # Sanity check
+
+            candidates = set(graph.keys())
+            start = candidates.pop()
+            found = [start]
+            cut_weight: list[int] = []
+
+            while candidates:
+                (weight, node) = max(
+                    (sum(graph[node][other] for other in found), node)
+                    for node in candidates
+                )
+                candidates.discard(node)
+                found.append(node)
+                cut_weight.append(weight)
+
+            return found[-2], found[-1], cut_weight[-1]
+
+        partition: set[str] = set()
+        g = copy.deepcopy(graph)
+
+        # Initialize our best weight/parition
+        best_weight = cast(int, float("inf"))
+        best_partition = set(partition)
+
+        while len(g) > 1:
+            s, t, w = phase(g)
+            partition.add(t)
+            contract_edge(g, s, t)
+            if w < best_weight:
+                best_weight = w
+                best_partition = set(partition)
+
+        return best_partition, graph.keys() - best_partition
+
+    def min_cut_karger(graph: Graph, target: int) -> tuple[set[str], set[str]]:
+        assert len(graph) >= 2  # Sanity check
+
+        original_edges = [
+            (source, dest)
+            for source, destinations in graph.items()
+            for dest in destinations
+            if source < dest
+        ]
+
+        while True:
+            g = copy.deepcopy(graph)
+            contracted = {v: v for v in graph}
+            while len(g) > 2:
+                s, t = (contracted[v] for v in random.choice(original_edges))
+                if s == t:
+                    continue
+                contract_edge(g, s, t)
+                # Hacky union-find-ish algorithm, for laziness
+                for k, v in contracted.items():
+                    if v != t:
+                        continue
+                    contracted[k] = s
+            # Pick a partition at random here
+            node = next(iter(g))
+            # Check that we met our target
+            if g[node].total() > target:
+                continue
+            partition = {n for n, parent in contracted.items() if parent == node}
+            return partition, graph.keys() - partition
+
+    graph = parse(input)
+    # The exact algorithm is very slow on big graphs, at which point Karger's algorithm is better
+    if len(graph) < 100:
+        left, right = min_cut_exact(graph)
+    else:
+        left, right = min_cut_karger(graph, 3)
+    return len(left) * len(right)
+
+
+def main() -> None:
+    input = sys.stdin.read().splitlines()
+    print(solve(input))
+
+
+if __name__ == "__main__":
+    main()