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feat: implement Hungarian algorithm for optimal instance pairing in reconciler
- Replaced the previous greedy matching approach with the Hungarian (Kuhn-Munkres) algorithm for improved accuracy in instance pairing, especially in ambiguous groups with multiple same-named instances. - Introduced a new Hungarian.lua module to encapsulate the algorithm's logic. - Updated the reconciler to utilize the new optimal assignment function, enhancing overall matching performance. - Documented changes in the CHANGELOG for clarity on the new implementation.
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Joe
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7 changed files with 234 additions and 287 deletions
103
src/hungarian.rs
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103
src/hungarian.rs
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@ -0,0 +1,103 @@
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//! Minimum-cost bipartite matching via the Hungarian (Kuhn-Munkres) algorithm.
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//!
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//! Given a cost matrix `cost[i][j]` for `rows` row-agents and `cols`
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//! column-agents, returns a Vec of `(row, col)` pairs representing the
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//! optimal assignment that minimizes total cost. Handles rectangular
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//! matrices by padding to square with `pad_cost`.
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//!
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//! Complexity: O(N^3) where N = max(rows, cols).
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/// Compute the minimum-cost assignment for a (possibly rectangular) cost matrix.
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///
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/// `pad_cost` fills dummy entries when the matrix is padded to square.
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/// It should be larger than any real cost in the matrix so that dummy
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/// assignments are never preferred over real ones.
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pub fn min_cost_assignment(
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cost: &[Vec<u32>],
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rows: usize,
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cols: usize,
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pad_cost: u32,
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) -> Vec<(usize, usize)> {
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if rows == 0 || cols == 0 {
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return Vec::new();
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}
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let n = rows.max(cols);
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let big = pad_cost as i64;
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let mut c = vec![vec![0i64; n]; n];
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for i in 0..n {
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for j in 0..n {
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c[i][j] = if i < rows && j < cols {
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cost[i][j] as i64
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} else {
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big
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};
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}
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}
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let mut u = vec![0i64; n + 1];
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let mut v = vec![0i64; n + 1];
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let mut p = vec![0usize; n + 1];
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let mut way = vec![0usize; n + 1];
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for i in 1..=n {
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p[0] = i;
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let mut j0 = 0usize;
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let mut min_v = vec![i64::MAX; n + 1];
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let mut used = vec![false; n + 1];
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loop {
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used[j0] = true;
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let i0 = p[j0];
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let mut delta = i64::MAX;
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let mut j1 = 0usize;
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for j in 1..=n {
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if used[j] {
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continue;
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}
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let cur = c[i0 - 1][j - 1] - u[i0] - v[j];
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if cur < min_v[j] {
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min_v[j] = cur;
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way[j] = j0;
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}
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if min_v[j] < delta {
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delta = min_v[j];
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j1 = j;
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}
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}
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for j in 0..=n {
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if used[j] {
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u[p[j]] += delta;
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v[j] -= delta;
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} else {
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min_v[j] -= delta;
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}
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}
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j0 = j1;
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if p[j0] == 0 {
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break;
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}
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}
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loop {
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let j1 = way[j0];
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p[j0] = p[j1];
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j0 = j1;
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if j0 == 0 {
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break;
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}
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}
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}
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let mut result = Vec::with_capacity(rows.min(cols));
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for j in 1..=n {
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if p[j] != 0 && p[j] <= rows && j <= cols {
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result.push((p[j] - 1, j - 1));
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}
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}
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result
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}
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@ -10,6 +10,7 @@ mod tree_view;
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mod change_processor;
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pub mod git;
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mod glob;
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pub mod hungarian;
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mod json;
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pub mod logging;
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mod lua_ast;
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@ -28,93 +28,6 @@ const UNMATCHED_PENALTY: u32 = 10_000;
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/// O(n^k) explosion on deeply nested trees with many same-named instances.
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const MAX_SCORING_DEPTH: u32 = 3;
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/// Minimum-cost bipartite matching via the Hungarian (Kuhn-Munkres) algorithm.
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/// See `src/syncback/matching.rs` for detailed documentation.
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fn min_cost_assignment(cost: &[Vec<u32>], rows: usize, cols: usize) -> Vec<(usize, usize)> {
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if rows == 0 || cols == 0 {
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return Vec::new();
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}
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let n = rows.max(cols);
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let big = UNMATCHED_PENALTY.saturating_mul(2);
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let mut c = vec![vec![0i64; n]; n];
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for i in 0..n {
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for j in 0..n {
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c[i][j] = if i < rows && j < cols {
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cost[i][j] as i64
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} else {
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big as i64
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};
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}
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}
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let mut u = vec![0i64; n + 1];
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let mut v = vec![0i64; n + 1];
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let mut p = vec![0usize; n + 1];
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let mut way = vec![0usize; n + 1];
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for i in 1..=n {
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p[0] = i;
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let mut j0 = 0usize;
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let mut min_v = vec![i64::MAX; n + 1];
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let mut used = vec![false; n + 1];
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loop {
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used[j0] = true;
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let i0 = p[j0];
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let mut delta = i64::MAX;
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let mut j1 = 0usize;
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for j in 1..=n {
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if used[j] {
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continue;
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}
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let cur = c[i0 - 1][j - 1] - u[i0] - v[j];
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if cur < min_v[j] {
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min_v[j] = cur;
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way[j] = j0;
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}
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if min_v[j] < delta {
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delta = min_v[j];
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j1 = j;
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}
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}
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for j in 0..=n {
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if used[j] {
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u[p[j]] += delta;
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v[j] -= delta;
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} else {
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min_v[j] -= delta;
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}
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}
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j0 = j1;
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if p[j0] == 0 {
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break;
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}
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}
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loop {
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let j1 = way[j0];
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p[j0] = p[j1];
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j0 = j1;
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if j0 == 0 {
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break;
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}
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}
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}
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let mut result = Vec::with_capacity(rows.min(cols));
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for j in 1..=n {
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if p[j] != 0 && p[j] <= rows && j <= cols {
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result.push((p[j] - 1, j - 1));
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}
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}
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result
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}
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/// Session-scoped cache for the matching algorithm. Caches
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/// `compute_change_count` results so that recursive scoring work is
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/// reused across calls at different tree levels.
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@ -262,7 +175,12 @@ pub fn match_forward(
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cost_matrix.push(row);
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}
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let assignment = min_cost_assignment(&cost_matrix, m, n);
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let assignment = crate::hungarian::min_cost_assignment(
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&cost_matrix,
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m,
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n,
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UNMATCHED_PENALTY.saturating_mul(2),
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);
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for (row, col) in assignment {
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let si = avail_snap[row];
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let ti = avail_tree[col];
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@ -419,7 +337,12 @@ fn match_children_for_scoring(
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cost_matrix.push(row);
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}
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let assignment = min_cost_assignment(&cost_matrix, m, n);
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let assignment = crate::hungarian::min_cost_assignment(
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&cost_matrix,
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m,
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n,
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UNMATCHED_PENALTY.saturating_mul(2),
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);
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for (row, col) in assignment {
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let si = avail_snap[row];
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let ti = avail_tree[col];
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@ -26,103 +26,6 @@ const UNMATCHED_PENALTY: u32 = 10_000;
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/// only flat property comparison is used (no subtree recursion).
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const MAX_SCORING_DEPTH: u32 = 3;
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/// Minimum-cost bipartite matching via the Hungarian (Kuhn-Munkres) algorithm.
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///
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/// Given a cost matrix `cost[i][j]` for `rows` row-agents and `cols`
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/// column-agents, returns a Vec of `(row, col)` pairs representing the
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/// optimal assignment that minimizes total cost. Handles rectangular
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/// matrices by padding to square with `UNMATCHED_PENALTY`.
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///
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/// Complexity: O(N^3) where N = max(rows, cols).
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fn min_cost_assignment(cost: &[Vec<u32>], rows: usize, cols: usize) -> Vec<(usize, usize)> {
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if rows == 0 || cols == 0 {
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return Vec::new();
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}
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let n = rows.max(cols);
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let big = UNMATCHED_PENALTY.saturating_mul(2);
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// Build square matrix, padding with `big` for dummy entries.
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let mut c = vec![vec![0i64; n]; n];
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for i in 0..n {
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for j in 0..n {
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c[i][j] = if i < rows && j < cols {
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cost[i][j] as i64
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} else {
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big as i64
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};
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}
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}
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// u[i] = potential for row i, v[j] = potential for column j
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let mut u = vec![0i64; n + 1];
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let mut v = vec![0i64; n + 1];
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// p[j] = row assigned to column j (1-indexed, 0 = unassigned)
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let mut p = vec![0usize; n + 1];
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// way[j] = previous column in augmenting path for column j
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let mut way = vec![0usize; n + 1];
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for i in 1..=n {
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p[0] = i;
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let mut j0 = 0usize;
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let mut min_v = vec![i64::MAX; n + 1];
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let mut used = vec![false; n + 1];
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loop {
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used[j0] = true;
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let i0 = p[j0];
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let mut delta = i64::MAX;
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let mut j1 = 0usize;
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for j in 1..=n {
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if used[j] {
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continue;
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}
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let cur = c[i0 - 1][j - 1] - u[i0] - v[j];
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if cur < min_v[j] {
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min_v[j] = cur;
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way[j] = j0;
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}
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if min_v[j] < delta {
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delta = min_v[j];
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j1 = j;
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}
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}
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for j in 0..=n {
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if used[j] {
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u[p[j]] += delta;
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v[j] -= delta;
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} else {
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min_v[j] -= delta;
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}
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}
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j0 = j1;
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if p[j0] == 0 {
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break;
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}
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}
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loop {
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let j1 = way[j0];
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p[j0] = p[j1];
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j0 = j1;
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if j0 == 0 {
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break;
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}
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}
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}
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let mut result = Vec::with_capacity(rows.min(cols));
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for j in 1..=n {
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if p[j] != 0 && p[j] <= rows && j <= cols {
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result.push((p[j] - 1, j - 1));
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}
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}
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result
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}
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/// Session-scoped cache for the syncback matching algorithm.
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pub struct MatchingSession {
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cost_cache: RefCell<HashMap<(Ref, Ref), u32>>,
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@ -295,7 +198,12 @@ pub fn match_children(
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}
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// Optimal assignment via the Hungarian algorithm.
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let assignment = min_cost_assignment(&cost_matrix, m, n);
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let assignment = crate::hungarian::min_cost_assignment(
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&cost_matrix,
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m,
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n,
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UNMATCHED_PENALTY.saturating_mul(2),
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);
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for (row, col) in assignment {
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let ni = avail_new[row];
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let oi = avail_old[col];
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@ -469,7 +377,12 @@ fn match_children_for_scoring(
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cost_matrix.push(row);
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}
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let assignment = min_cost_assignment(&cost_matrix, m, n);
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let assignment = crate::hungarian::min_cost_assignment(
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&cost_matrix,
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m,
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n,
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UNMATCHED_PENALTY.saturating_mul(2),
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);
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for (row, col) in assignment {
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let ni = avail_new[row];
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let oi = avail_old[col];
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