Companion rule for the model proposal in #1046.

Source / Target

• Source: MaximumAcyclicAgreementForest (default variant) — see [Model] MaximumAcyclicAgreementForest #1046
• Target: ILP/i32

Motivation

A direct integer-linear-programming formulation for MAAF gives an immediate solver path via the existing ILP solver backend, independent of the structural MAAF → MinimumFeedbackVertexSet route. The formulation is compact and natural: cut-indicator variables per tree edge plus block-ancestor indicator variables plus a topological-rank constraint enforcing acyclicity.

Reference

Wu, "Close lower and upper bounds for the minimum reticulate network of multiple phylogenetic trees", Bioinformatics 26(12), 2010. https://doi.org/10.1093/bioinformatics/btq198

See also Chen, Wang, "Algorithms for reticulate networks of multiple phylogenetic trees", IEEE/ACM TCBB 9(2), 2012 for a related ILP.

Reduction Algorithm

Let $T_1, T_2$ be the input rooted binary phylogenetic trees on leaf set $X$, $n = |X|$. Let $E_t$ denote the non-root edges of $T_t$ ($|E_t| = 2n - 2$).

1. Cut-indicator variables. For each non-root edge $e \in E_1 \cup E_2$, introduce a binary variable $y_e \in {0,1}$: $y_e = 1$ iff $e$ is cut (removed from $T_t$).
2. Block-ancestor variables. Let $B = {1,\dots,n}$ be candidate block indices. For each ordered pair $(i, j) \in B \times B$ with $i \neq j$, introduce a binary variable $z_{ij} \in {0,1}$ indicating the arc $i \to j$ in the block ancestor digraph.
3. Rank variables. For each $i \in B$, introduce an integer variable $r_i \in {0, 1, \dots, n-1}$.
4. Partition-agreement constraints. For each pair of leaves $(\ell, m) \in X \times X$, let $P_t(\ell, m)$ be the unique edge path from $\ell$ to $m$ in $T_t$. The constraint
   $$\sum_{e \in P_1(\ell,m)} y_e ;=; 0 ;;\Longleftrightarrow;; \sum_{e \in P_2(\ell,m)} y_e ;=; 0$$
   (linearized via auxiliary indicators $s_{\ell,m}^{(t)} \in {0,1}$ with $s_{\ell,m}^{(t)} \leq \sum_{e \in P_t(\ell,m)} y_e$ and $s_{\ell,m}^{(t)} \geq y_e$ for every $e \in P_t(\ell,m)$, plus $s_{\ell,m}^{(1)} = s_{\ell,m}^{(2)}$) enforces that $\ell$ and $m$ lie in the same block of $F$ iff neither tree cuts their connecting path.
5. Acyclicity constraints. For each $(i, j) \in B \times B$, $i \neq j$: $r_i - r_j + n \cdot z_{ij} \leq n - 1$ (standard Miller–Tucker–Zemlin-style rank constraint, infeasible precisely when the $z$-digraph contains a cycle).
6. Objective.
   $$\text{minimize} ;; 1 + \sum_{e \in E_1} y_e.$$
   (Each cut in $T_1$ produces one additional block; by the partition-agreement constraints the number of $T_1$-cuts equals the number of $T_2$-cuts and equals $|F| - 1$.)

Return the resulting ILP as the target instance.

Size Overhead

Target size fields from pred show ILP → size_fields = [num_constraints, num_variables].

(The 4ドルn$ term counts the $y_e$ and $s^{(t)}{\ell,m}$ variables up to lower-order terms; $n^2$ bounds the $z{ij}$ plus partition-agreement equalities.)

Validation Method

Closed-loop test: construct the source instance, apply the reduction, solve the target ILP (brute-force or ILP solver), lift back to a partition $F$, and verify $|F| - 1$ matches the ground-truth hybridization number.

Example (fully worked)

Source. $X = {a,b,c,d}$, $T_1 = ((a,b),(c,d))$, $T_2 = ((a,c),(b,d))$.

Construction. $T_1$ and $T_2$ each have 6 non-root edges. The ILP has 12 $y$ variables, 12 $z$ variables, 4 $r$ variables, 32 $s$ variables (2 per ordered leaf pair per tree), with 16 partition-agreement equalities and 12 acyclicity constraints.

Target solved. Optimal objective = 1ドル + 3 = 4$; optimal $y$ assignment cuts 3 edges in $T_1$ (all three internal edges incident to the root on either side), producing the partition ${{a},{b},{c},{d}}$.

Lifted solution. $F = {{a},{b},{c},{d}}$, $|F| = 4$, hybridization number = 3.

BibTeX

@article{Wu2010,
 author = {Wu, Yufeng},
 title = {Close lower and upper bounds for the minimum reticulate network of multiple phylogenetic trees},
 journal = {Bioinformatics},
 volume = {26},
 number = {12},
 pages = {i140--i148},
 year = {2010},
 doi = {10.1093/bioinformatics/btq198},
}