Fix #117: [Model] GraphPartitioning (#570)

* Add plan for #117: [Model] GraphPartitioning

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Implement #117: Add GraphPartitioning model

- Add GraphPartitioning (Minimum Bisection) problem model
- Single type param G (unweighted, counts crossing edges)
- Balanced bisection: requires |A| = |B| = n/2 (even n)
- Direction: Minimize
- Register in CLI (dispatch, aliases, create command)
- Add unit tests (11 tests covering all cases)
- Add problem-def entry in paper with CeTZ visualization

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Review fixes: strengthen test assertion, warn on odd vertex count

- Assert exact cut value in unbalanced_invalid test
- Warn and round up when random generation gets odd vertex count

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: remove plan file after implementation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: add config length check and binary validation in GraphPartitioning::evaluate

Address Copilot review comments: validate config length matches vertex count
and use filter-based counting instead of sum for correct binary detection.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: add GraphPartitioning to CLI help "Flags by problem type" table

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: use is_multiple_of() to satisfy clippy lint

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: GiggleLiu

docs/paper/reductions.typ

@@ -29,6 +29,7 @@
   "MaximumIndependentSet": [Maximum Independent Set],
   "MinimumVertexCover": [Minimum Vertex Cover],
   "MaxCut": [Max-Cut],
+  "GraphPartitioning": [Graph Partitioning],
   "KColoring": [$k$-Coloring],
   "MinimumDominatingSet": [Minimum Dominating Set],
   "MaximumMatching": [Maximum Matching],
@@ -379,6 +380,57 @@ Max-Cut is NP-hard on general graphs @barahona1982 but polynomial-time solvable
 caption: [The house graph with max cut $S = {v_0, v_3}$ (blue) vs $overline(S) = {v_1, v_2, v_4}$ (white). Cut edges shown in bold blue; 5 of 6 edges are cut.],
 ) <fig:house-maxcut>
 ]
+#problem-def("GraphPartitioning")[
+  Given an undirected graph $G = (V, E)$ with $|V| = n$ (even), find a partition of $V$ into two disjoint sets $A$ and $B$ with $|A| = |B| = n slash 2$ that minimizes the number of edges crossing the partition:
+  $ "cut"(A, B) = |{(u, v) in E : u in A, v in B}|. $
+][
+Graph Partitioning is a core NP-hard problem arising in VLSI design, parallel computing, and scientific simulation, where balanced workload distribution with minimal communication is essential. Closely related to Max-Cut (which _maximizes_ rather than _minimizes_ the cut) and to the Ising Spin Glass model. NP-completeness was proved by Garey, Johnson and Stockmeyer (1976). Arora, Rao and Vazirani (2009) gave an $O(sqrt(log n))$-approximation algorithm. Standard partitioning tools include METIS, KaHIP, and Scotch.
+
+*Example.* Consider the graph $G$ with $n = 6$ vertices and 9 edges: $(v_0, v_1)$, $(v_0, v_2)$, $(v_1, v_2)$, $(v_1, v_3)$, $(v_2, v_3)$, $(v_2, v_4)$, $(v_3, v_4)$, $(v_3, v_5)$, $(v_4, v_5)$. The optimal balanced partition is $A = {v_0, v_1, v_2}$, $B = {v_3, v_4, v_5}$, with cut value 3: the crossing edges are $(v_1, v_3)$, $(v_2, v_3)$, $(v_2, v_4)$. All other balanced partitions yield a cut of at least 3.
+
+#figure(
+  canvas(length: 1cm, {
+    // 6-vertex layout: two columns of 3
+    let verts = (
+      (0, 2),     // v0: top-left
+      (0, 1),     // v1: mid-left
+      (0, 0),     // v2: bottom-left
+      (2.5, 2),   // v3: top-right
+      (2.5, 1),   // v4: mid-right
+      (2.5, 0),   // v5: bottom-right
+    )
+    let edges = ((0,1),(0,2),(1,2),(1,3),(2,3),(2,4),(3,4),(3,5),(4,5))
+    let side-a = (0, 1, 2)
+    let cut-edges = edges.filter(e => side-a.contains(e.at(0)) != side-a.contains(e.at(1)))
+    // Draw edges
+    for (u, v) in edges {
+      let crossing = cut-edges.any(e => (e.at(0) == u and e.at(1) == v) or (e.at(0) == v and e.at(1) == u))
+      g-edge(verts.at(u), verts.at(v),
+        stroke: if crossing { 2pt + graph-colors.at(1) } else { 1pt + luma(180) })
+    }
+    // Draw partition regions
+    import draw: *
+    on-layer(-1, {
+      rect((-0.5, -0.5), (0.5, 2.5),
+        fill: graph-colors.at(0).transparentize(90%),
+        stroke: (dash: "dashed", paint: graph-colors.at(0), thickness: 0.8pt))
+      content((0, 2.8), text(8pt, fill: graph-colors.at(0))[$A$])
+      rect((2.0, -0.5), (3.0, 2.5),
+        fill: graph-colors.at(1).transparentize(90%),
+        stroke: (dash: "dashed", paint: graph-colors.at(1), thickness: 0.8pt))
+      content((2.5, 2.8), text(8pt, fill: graph-colors.at(1))[$B$])
+    })
+    // Draw nodes
+    for (k, pos) in verts.enumerate() {
+      let in-a = side-a.contains(k)
+      g-node(pos, name: "v" + str(k),
+        fill: if in-a { graph-colors.at(0) } else { graph-colors.at(1) },
+        label: text(fill: white)[$v_#k$])
+    }
+  }),
+  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("KColoring")[
   Given $G = (V, E)$ and $k$ colors, find $c: V -> {1, ..., k}$ minimizing $|{(u, v) in E : c(u) = c(v)}|$.
 ][

problemreductions-cli/src/cli.rs

@@ -208,6 +208,7 @@ Flags by problem type:
   QUBO                            --matrix
   SpinGlass                       --graph, --couplings, --fields
   KColoring                       --graph, --k
+  GraphPartitioning               --graph
   Factoring                       --target, --m, --n
   BinPacking                      --sizes, --capacity
   PaintShop                       --sequence

problemreductions-cli/src/commands/create.rs

@@ -5,6 +5,7 @@ use crate::problem_name::{parse_problem_spec, resolve_variant};
 use crate::util;
 use anyhow::{bail, Context, Result};
 use problemreductions::models::algebraic::{ClosestVectorProblem, BMF};
+use problemreductions::models::graph::GraphPartitioning;
 use problemreductions::models::misc::{BinPacking, PaintShop};
 use problemreductions::prelude::*;
 use problemreductions::registry::collect_schemas;
@@ -74,6 +75,7 @@ fn example_for(canonical: &str, graph_type: Option<&str>) -> &'static str {
             Some("UnitDiskGraph") => "--positions \"0,0;1,0;0.5,0.8\" --radius 1.5",
             _ => "--graph 0-1,1-2,2-3 --weights 1,1,1,1",
         },
+        "GraphPartitioning" => "--graph 0-1,1-2,2-3,0-2,1-3,0-3",
         "MaxCut" | "MaximumMatching" | "TravelingSalesman" => {
             "--graph 0-1,1-2,2-3 --edge-weights 1,1,1"
         }
@@ -192,6 +194,19 @@ pub fn create(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
             create_vertex_weight_problem(args, canonical, graph_type, &resolved_variant)?
         }
 
+        // Graph partitioning (graph only, no weights)
+        "GraphPartitioning" => {
+            let (graph, _) = parse_graph(args).map_err(|e| {
+                anyhow::anyhow!(
+                    "{e}\n\nUsage: pred create GraphPartitioning --graph 0-1,1-2,2-3,0-2,1-3,0-3"
+                )
+            })?;
+            (
+                ser(GraphPartitioning::new(graph))?,
+                resolved_variant.clone(),
+            )
+        }
+
         // Graph problems with edge weights
         "MaxCut" | "MaximumMatching" | "TravelingSalesman" => {
             let (graph, _) = parse_graph(args).map_err(|e| {
@@ -887,6 +902,27 @@ fn create_random(
             }
         }
 
+        // GraphPartitioning (graph only, no weights; requires even vertex count)
+        "GraphPartitioning" => {
+            let num_vertices = if num_vertices % 2 != 0 {
+                eprintln!(
+                    "Warning: GraphPartitioning requires even vertex count; rounding {} up to {}",
+                    num_vertices,
+                    num_vertices + 1
+                );
+                num_vertices + 1
+            } else {
+                num_vertices
+            };
+            let edge_prob = args.edge_prob.unwrap_or(0.5);
+            if !(0.0..=1.0).contains(&edge_prob) {
+                bail!("--edge-prob must be between 0.0 and 1.0");
+            }
+            let graph = util::create_random_graph(num_vertices, edge_prob, args.seed);
+            let variant = variant_map(&[("graph", "SimpleGraph")]);
+            (ser(GraphPartitioning::new(graph))?, variant)
+        }
+
         // Graph problems with edge weights
         "MaxCut" | "MaximumMatching" | "TravelingSalesman" => {
             let edge_prob = args.edge_prob.unwrap_or(0.5);

problemreductions-cli/src/dispatch.rs

@@ -210,6 +210,7 @@ pub fn load_problem(
         "MaximumClique" => deser_opt::<MaximumClique<SimpleGraph, i32>>(data),
         "MaximumMatching" => deser_opt::<MaximumMatching<SimpleGraph, i32>>(data),
         "MinimumDominatingSet" => deser_opt::<MinimumDominatingSet<SimpleGraph, i32>>(data),
+        "GraphPartitioning" => deser_opt::<GraphPartitioning<SimpleGraph>>(data),
         "MaxCut" => deser_opt::<MaxCut<SimpleGraph, i32>>(data),
         "MaximalIS" => deser_opt::<MaximalIS<SimpleGraph, i32>>(data),
         "TravelingSalesman" => deser_opt::<TravelingSalesman<SimpleGraph, i32>>(data),
@@ -267,6 +268,7 @@ pub fn serialize_any_problem(
         "MaximumClique" => try_ser::<MaximumClique<SimpleGraph, i32>>(any),
         "MaximumMatching" => try_ser::<MaximumMatching<SimpleGraph, i32>>(any),
         "MinimumDominatingSet" => try_ser::<MinimumDominatingSet<SimpleGraph, i32>>(any),
+        "GraphPartitioning" => try_ser::<GraphPartitioning<SimpleGraph>>(any),
         "MaxCut" => try_ser::<MaxCut<SimpleGraph, i32>>(any),
         "MaximalIS" => try_ser::<MaximalIS<SimpleGraph, i32>>(any),
         "TravelingSalesman" => try_ser::<TravelingSalesman<SimpleGraph, i32>>(any),

problemreductions-cli/src/problem_name.rs

@@ -32,6 +32,7 @@ pub fn resolve_alias(input: &str) -> String {
         "3sat" => "KSatisfiability".to_string(),
         "ksat" | "ksatisfiability" => "KSatisfiability".to_string(),
         "qubo" => "QUBO".to_string(),
+        "graphpartitioning" => "GraphPartitioning".to_string(),
         "maxcut" => "MaxCut".to_string(),
         "spinglass" => "SpinGlass".to_string(),
         "ilp" => "ILP".to_string(),

src/lib.rs

@@ -40,7 +40,7 @@ pub mod prelude {
     // Problem types
     pub use crate::models::algebraic::{BMF, QUBO};
     pub use crate::models::formula::{CNFClause, CircuitSAT, KSatisfiability, Satisfiability};
-    pub use crate::models::graph::{BicliqueCover, SpinGlass};
+    pub use crate::models::graph::{BicliqueCover, GraphPartitioning, SpinGlass};
     pub use crate::models::graph::{
         KColoring, MaxCut, MaximalIS, MaximumClique, MaximumIndependentSet, MaximumMatching,
         MinimumDominatingSet, MinimumVertexCover, TravelingSalesman,

src/models/graph/graph_partitioning.rs

@@ -0,0 +1,142 @@
+//! GraphPartitioning problem implementation.
+//!
+//! The Graph Partitioning (Minimum Bisection) problem asks for a balanced partition
+//! of vertices into two equal halves minimizing the number of crossing edges.
+
+use crate::registry::{FieldInfo, ProblemSchemaEntry};
+use crate::topology::{Graph, SimpleGraph};
+use crate::traits::{OptimizationProblem, Problem};
+use crate::types::{Direction, SolutionSize};
+use serde::{Deserialize, Serialize};
+
+inventory::submit! {
+    ProblemSchemaEntry {
+        name: "GraphPartitioning",
+        module_path: module_path!(),
+        description: "Find minimum cut balanced bisection of a graph",
+        fields: &[
+            FieldInfo { name: "graph", type_name: "G", description: "The undirected graph G=(V,E)" },
+        ],
+    }
+}
+
+/// The Graph Partitioning (Minimum Bisection) problem.
+///
+/// Given an undirected graph G = (V, E) with |V| = n (even),
+/// partition V into two disjoint sets A and B with |A| = |B| = n/2,
+/// minimizing the number of edges crossing the partition.
+///
+/// # Type Parameters
+///
+/// * `G` - The graph type (e.g., `SimpleGraph`)
+///
+/// # Example
+///
+/// ```
+/// use problemreductions::models::graph::GraphPartitioning;
+/// use problemreductions::topology::SimpleGraph;
+/// use problemreductions::types::SolutionSize;
+/// use problemreductions::{Problem, Solver, BruteForce};
+///
+/// // Square graph: 0-1, 1-2, 2-3, 3-0
+/// let graph = SimpleGraph::new(4, vec![(0, 1), (1, 2), (2, 3), (3, 0)]);
+/// let problem = GraphPartitioning::new(graph);
+///
+/// let solver = BruteForce::new();
+/// let solutions = solver.find_all_best(&problem);
+///
+/// // Minimum bisection of a 4-cycle: cut = 2
+/// for sol in solutions {
+///     let size = problem.evaluate(&sol);
+///     assert_eq!(size, SolutionSize::Valid(2));
+/// }
+/// ```
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct GraphPartitioning<G> {
+    /// The underlying graph structure.
+    graph: G,
+}
+
+impl<G: Graph> GraphPartitioning<G> {
+    /// Create a GraphPartitioning problem from a graph.
+    ///
+    /// # Arguments
+    /// * `graph` - The undirected graph to partition
+    pub fn new(graph: G) -> Self {
+        Self { graph }
+    }
+
+    /// Get a reference to the underlying graph.
+    pub fn graph(&self) -> &G {
+        &self.graph
+    }
+
+    /// Get the number of vertices in the underlying graph.
+    pub fn num_vertices(&self) -> usize {
+        self.graph.num_vertices()
+    }
+
+    /// Get the number of edges in the underlying graph.
+    pub fn num_edges(&self) -> usize {
+        self.graph.num_edges()
+    }
+}
+
+impl<G> Problem for GraphPartitioning<G>
+where
+    G: Graph + crate::variant::VariantParam,
+{
+    const NAME: &'static str = "GraphPartitioning";
+    type Metric = SolutionSize<i32>;
+
+    fn variant() -> Vec<(&'static str, &'static str)> {
+        crate::variant_params![G]
+    }
+
+    fn dims(&self) -> Vec<usize> {
+        vec![2; self.graph.num_vertices()]
+    }
+
+    fn evaluate(&self, config: &[usize]) -> SolutionSize<i32> {
+        let n = self.graph.num_vertices();
+        if config.len() != n {
+            return SolutionSize::Invalid;
+        }
+        // Balanced bisection requires even n
+        if !n.is_multiple_of(2) {
+            return SolutionSize::Invalid;
+        }
+        // Check balanced: exactly n/2 vertices in partition 1
+        let count_ones = config.iter().filter(|&&x| x == 1).count();
+        if count_ones != n / 2 {
+            return SolutionSize::Invalid;
+        }
+        // Count crossing edges
+        let mut cut = 0i32;
+        for (u, v) in self.graph.edges() {
+            if config[u] != config[v] {
+                cut += 1;
+            }
+        }
+        SolutionSize::Valid(cut)
+    }
+}
+
+impl<G> OptimizationProblem for GraphPartitioning<G>
+where
+    G: Graph + crate::variant::VariantParam,
+{
+    type Value = i32;
+
+    fn direction(&self) -> Direction {
+        Direction::Minimize
+    }
+}
+
+crate::declare_variants! {
+    GraphPartitioning<SimpleGraph> => "2^num_vertices",
+}
+
+#[cfg(test)]
+#[path = "../../unit_tests/models/graph/graph_partitioning.rs"]
+mod tests;

src/models/graph/mod.rs

@@ -7,13 +7,15 @@
 //! - [`MinimumDominatingSet`]: Minimum dominating set
 //! - [`MaximumClique`]: Maximum weight clique
 //! - [`MaxCut`]: Maximum cut on weighted graphs
+//! - [`GraphPartitioning`]: Minimum bisection (balanced graph partitioning)
 //! - [`KColoring`]: K-vertex coloring
 //! - [`MaximumMatching`]: Maximum weight matching
 //! - [`TravelingSalesman`]: Traveling Salesman (minimum weight Hamiltonian cycle)
 //! - [`SpinGlass`]: Ising model Hamiltonian
 //! - [`BicliqueCover`]: Biclique cover on bipartite graphs
 
 pub(crate) mod biclique_cover;
+pub(crate) mod graph_partitioning;
 pub(crate) mod kcoloring;
 pub(crate) mod max_cut;
 pub(crate) mod maximal_is;
@@ -26,6 +28,7 @@ pub(crate) mod spin_glass;
 pub(crate) mod traveling_salesman;
 
 pub use biclique_cover::BicliqueCover;
+pub use graph_partitioning::GraphPartitioning;
 pub use kcoloring::KColoring;
 pub use max_cut::MaxCut;
 pub use maximal_is::MaximalIS;

src/models/mod.rs

@@ -12,8 +12,9 @@ pub mod set;
 pub use algebraic::{ClosestVectorProblem, BMF, ILP, QUBO};
 pub use formula::{CNFClause, CircuitSAT, KSatisfiability, Satisfiability};
 pub use graph::{
-    BicliqueCover, KColoring, MaxCut, MaximalIS, MaximumClique, MaximumIndependentSet,
-    MaximumMatching, MinimumDominatingSet, MinimumVertexCover, SpinGlass, TravelingSalesman,
+    BicliqueCover, GraphPartitioning, KColoring, MaxCut, MaximalIS, MaximumClique,
+    MaximumIndependentSet, MaximumMatching, MinimumDominatingSet, MinimumVertexCover, SpinGlass,
+    TravelingSalesman,
 };
 pub use misc::{BinPacking, Factoring, Knapsack, PaintShop};
 pub use set::{MaximumSetPacking, MinimumSetCovering};