Fix #230: Add BiconnectivityAugmentation model

docs/paper/reductions.typ

@@ -30,6 +30,7 @@
   "MinimumVertexCover": [Minimum Vertex Cover],
   "MaxCut": [Max-Cut],
   "GraphPartitioning": [Graph Partitioning],
+  "BiconnectivityAugmentation": [Biconnectivity Augmentation],
   "KColoring": [$k$-Coloring],
   "MinimumDominatingSet": [Minimum Dominating Set],
   "MaximumMatching": [Maximum Matching],
@@ -434,6 +435,13 @@ Graph Partitioning is a core NP-hard problem arising in VLSI design, parallel co
   caption: [Graph with $n = 6$ vertices partitioned into $A = {v_0, v_1, v_2}$ (blue) and $B = {v_3, v_4, v_5}$ (red). The 3 crossing edges $(v_1, v_3)$, $(v_2, v_3)$, $(v_2, v_4)$ are shown in bold red; internal edges are gray.],
 ) <fig:graph-partitioning>
 ]
+#problem-def("BiconnectivityAugmentation")[
+  Given an undirected graph $G = (V, E)$, a set $F$ of candidate edges on $V$ with $F inter E = emptyset$, weights $w: F -> RR$, and a budget $B in RR$, find $F' subset.eq F$ such that $sum_(e in F') w(e) <= B$ and the augmented graph $G' = (V, E union F')$ is biconnected, meaning $G'$ is connected and deleting any single vertex leaves it connected.
+][
+Biconnectivity augmentation is a classical network-design problem: add backup links so the graph survives any single vertex failure. The weighted candidate-edge formulation modeled here captures communication, transportation, and infrastructure planning settings where only a prescribed set of new links is feasible and each carries a cost. In this library, the exact baseline is brute-force enumeration over the $m = |F|$ candidate edges, yielding $O^*(2^m)$ time and matching the exported complexity metadata for the model.
+
+*Example.* Consider the path graph $v_0 - v_1 - v_2 - v_3 - v_4 - v_5$ with candidate edges $(v_0, v_2)$, $(v_0, v_3)$, $(v_0, v_4)$, $(v_1, v_3)$, $(v_1, v_4)$, $(v_1, v_5)$, $(v_2, v_4)$, $(v_2, v_5)$, $(v_3, v_5)$ carrying weights $(1, 2, 3, 1, 2, 3, 1, 2, 1)$ and budget $B = 4$. Selecting $F' = {(v_0, v_2), (v_1, v_3), (v_2, v_4), (v_3, v_5)}$ uses total weight $1 + 1 + 1 + 1 = 4$ and eliminates every articulation point: after deleting any single vertex, the remaining graph is still connected. Reducing the budget to $B = 3$ makes the instance infeasible, because one of the path endpoints remains attached through a single articulation vertex.
+]
 #problem-def("KColoring")[
   Given $G = (V, E)$ and $k$ colors, find $c: V -> {1, ..., k}$ minimizing $|{(u, v) in E : c(u) = c(v)}|$.
 ][

docs/src/reductions/problem_schemas.json

@@ -41,6 +41,27 @@
       }
     ]
   },
+  {
+    "name": "BiconnectivityAugmentation",
+    "description": "Add weighted potential edges to make a graph biconnected within budget",
+    "fields": [
+      {
+        "name": "graph",
+        "type_name": "G",
+        "description": "The underlying graph G=(V,E)"
+      },
+      {
+        "name": "potential_weights",
+        "type_name": "Vec<(usize, usize, W)>",
+        "description": "Potential edges with augmentation weights"
+      },
+      {
+        "name": "budget",
+        "type_name": "W::Sum",
+        "description": "Maximum total augmentation weight B"
+      }
+    ]
+  },
   {
     "name": "BinPacking",
     "description": "Assign items to bins minimizing number of bins used, subject to capacity",
@@ -195,6 +216,17 @@
       }
     ]
   },
+  {
+    "name": "LongestCommonSubsequence",
+    "description": "Find the longest string that is a subsequence of every input string",
+    "fields": [
+      {
+        "name": "strings",
+        "type_name": "Vec<Vec<u8>>",
+        "description": "The input strings"
+      }
+    ]
+  },
   {
     "name": "MaxCut",
     "description": "Find maximum weight cut in a graph",