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Algorithmic Aspects of Quiver Representations

Avatar for Tasuku Soma Tasuku Soma
September 29, 2025

Algorithmic Aspects of Quiver Representations

Presented at the 13th Hungarian-Japanese Symposium on Discrete Mathematics and Its Applications

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Tasuku Soma

September 29, 2025
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  1. Algorithmic Aspects of Quiver Representations 1 2 3 4 1

    1 1 1 0 2 3 0 1 2 3 4 5 6 7 8 1 1 1 4 0 0 5 0 1 1 1 1 0 1 C4 C2 C2 C3 V (i) V (a) Tasuku Soma (Institute of Statistical Mathematics & RIKEN AIP) Joint work with Yuni Iwamasa (Kyoto University) Taihei Oki (Hokkaido University) 1 / 34
  2. Overview • Quiver representation is a generalization of matrices. •

    σ-semistability of quiver reps generalizes nc-nonsingularity of linear matrices, membership of Brascamp-Lieb polytopes, etc. • There are nice connections to network/submodular flow! 2 / 34
  3. 1 Basics of Quiver Representations 2 Deciding σ-Semistability 3 King’s

    Polyhedral Cone and Network Flows 4 Conclusion 3 / 34
  4. What is a Quiver? A quiver is just a directed

    graph. Notation Q = ( Q0 vertex set , Q1 arc set ) For arc a ∈ Q1 , • ha: head of a • ta: tail of a a ta ha In this talk, we consider only acyclic quivers. 4 / 34
  5. Quiver Representations A representation V of Q consists of: •

    (finite) C-vector space V (i) for each vertex i ∈ Q0 • matrix V (a) : V (ta) → V (ha) for each arc a ∈ Q1 1 2 3 4 1 1 1 1 0 2 3 0 1 2 3 4 5 6 7 8 1 1 1 4 0 0 5 0 1 1 1 1 0 1 C4 C2 C2 C3 V (i) V (a) 5 / 34
  6. Quiver Representations A representation V of Q consists of: •

    (finite) C-vector space V (i) for each vertex i ∈ Q0 • matrix V (a) : V (ta) → V (ha) for each arc a ∈ Q1 Dimension vector dim V := (dim V (i))i∈Q0 1 2 3 4 1 1 1 1 0 2 3 0 1 2 3 4 5 6 7 8 1 1 1 4 0 0 5 0 1 1 1 1 0 1 C4 C2 C2 C3 V (i) V (a) dim V = (4, 2, 2, 3) 5 / 34
  7. Quiver Representations A representation V of Q consists of: •

    (finite) C-vector space V (i) for each vertex i ∈ Q0 • matrix V (a) : V (ta) → V (ha) for each arc a ∈ Q1 Dimension vector dim V := (dim V (i))i∈Q0 1 2 3 4 1 1 1 1 0 2 3 0 1 2 3 4 5 6 7 8 1 1 1 4 0 0 5 0 1 1 1 1 0 1 C4 C2 C2 C3 V (i) V (a) dim V = (4, 2, 2, 3) Representation space Rep(Q, α) ... set of reps of Q with dim vec α 5 / 34
  8. Examples Quiver Algebraic Object A matrix A generic partitioned matrix

      x11 A11 x12 A12 x21 A21 x22 A22 x31 A31 x32 A32   A1 A2 Ak generalized Kronecker matrix tuple (A1 , . . . , Ak ) linear matrix A = x1 A1 + · · · + xk Ak (xi : indeterminates) 1 2 1 2 3 A11 A 23 bipartite 6 / 34
  9. Group action on Quiver Representations Group GL(Q, α) = i∈Q0

    GL(α(i)) naturally acts on Rep(Q, α) by change of basis: g · V := (gha V (a)g−1 ta )a∈Q1 . This defines the orbit of V . 1 2 3 4 V (a1 ) V (a 2 ) V (a3 ) V (a4 ) V (a 5 ) V (1) V (2) V (3) V (4) g1 ⟳ g2 ⟳ g4 ⟳ ⟳ g3 7 / 34
  10. Group action on Quiver Representations Group GL(Q, α) = i∈Q0

    GL(α(i)) naturally acts on Rep(Q, α) by change of basis: g · V := (gha V (a)g−1 ta )a∈Q1 . This defines the orbit of V . 1 2 3 4 V (a1 )g − 1 1 V (a 2 )g − 1 1 V (a3 ) V (a4 ) V (a 5 ) V (1) V (2) V (3) V (4) g1 ⟳ g2 ⟳ g4 ⟳ ⟳ g3 7 / 34
  11. Group action on Quiver Representations Group GL(Q, α) = i∈Q0

    GL(α(i)) naturally acts on Rep(Q, α) by change of basis: g · V := (gha V (a)g−1 ta )a∈Q1 . This defines the orbit of V . 1 2 3 4 g2 V (a1 )g − 1 1 V (a 2 )g − 1 1 V (a3 )g−1 2 V (a4 ) V (a 5 )g − 1 2 V (1) V (2) V (3) V (4) g1 ⟳ g2 ⟳ g4 ⟳ ⟳ g3 7 / 34
  12. Group action on Quiver Representations Group GL(Q, α) = i∈Q0

    GL(α(i)) naturally acts on Rep(Q, α) by change of basis: g · V := (gha V (a)g−1 ta )a∈Q1 . This defines the orbit of V . 1 2 3 4 g2 V (a1 )g − 1 1 g 3 V (a 2 )g − 1 1 g3 V (a3 )g−1 2 g4 V (a4 )g − 1 3 g 4 V (a 5 )g − 1 2 V (1) V (2) V (3) V (4) g1 ⟳ g2 ⟳ g4 ⟳ ⟳ g3 7 / 34
  13. Examples Quiver Transformation A elementary gAh−1 A1 A2 Ak generalized

    Kronecker simultaneous elementary (gA1 h−1, . . . , gAk h−1) gAh−1 = x1 gA1 h−1 + · · · + xk gAk h−1 (xi : indeterminates) 1 2 1 2 3 A11 A 23 bipartite block-wise elementary   g1 g2 g3     x11 A11 x12 A12 x21 A21 x22 A22 x31 A31 x32 A32   h−1 1 h−1 2 8 / 34
  14. Reps of Acyclic Quiver are Unstable Theorem For any rep

    V of an acyclic quiver Q, g · V can be made arbitrarily close to 0 by the GL(Q, α)-action. 9 / 34
  15. Reps of Acyclic Quiver are Unstable Theorem For any rep

    V of an acyclic quiver Q, g · V can be made arbitrarily close to 0 by the GL(Q, α)-action. We will use the following basic result in network flow: Lemma Let c : Q1 → R be an arc cost. Then, ∃ vertex potential p : Q0 → R s.t. p(ha) − p(ta) ≤ c(a) (a ∈ Q1 ) ⇐⇒ Q has no negative cycle w.r.t. c. 9 / 34
  16. Reps of Acyclic Quiver are Unstable Theorem For any rep

    V of an acyclic quiver Q, g · V can be made arbitrarily close to 0 by the GL(Q, α)-action. We will use the following basic result in network flow: Lemma Let c : Q1 → R be an arc cost. Then, ∃ vertex potential p : Q0 → R s.t. p(ha) − p(ta) ≤ c(a) (a ∈ Q1 ) ⇐⇒ Q has no negative cycle w.r.t. c. Since Q is acyclic, there is p s.t. p(ha) − p(ta) ≤ −1 (a ∈ Q1 ). For gi = tp(i)I (i ∈ Q0 ) with t ∈ R, (g · V )(a) = t p(ha)−p(ta) V (a) → 0 (t → +∞) 9 / 34
  17. σ-Semistability of Quiver Representations σ : Q0 → Z ...

    integer vertex weight Definition • A quiver rep V is σ-unstable if both g · V and χσ (g) := i∈Q0 det(gi )σ(i) character can be made arbitrarily close to 0 simultaneously by the GL(Q, α)-action ( ⇐⇒ the orbit closure of (V, 1) contains the origin) • If not, V is σ-semistable. 10 / 34
  18. Subrepresentations A subrepresentation W of V is a collection of

    • subspace W(i) ≤ V (i) (i ∈ Q0 ) • restricted matrix W(a) = V (a)|W(ta) (a ∈ Q1 ) satisfying im W(a) ≤ W(ha) (a ∈ Q1 ). Example A B C D E F G rep V A D F subrep B D E F G NOT subrep 11 / 34
  19. King’s Criterion [King 1994] A characterization of σ-semistability by a

    linear system: King’s criterion A rep V of an acyclic quiver is σ-semistable ⇐⇒ σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) σ(α) := i∈Q0 σ(i)α(i) W ≤ V means W is a subrep of V 12 / 34
  20. King’s Criterion [King 1994] A characterization of σ-semistability by a

    linear system: King’s criterion A rep V of an acyclic quiver is σ-semistable ⇐⇒ σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) σ(α) := i∈Q0 σ(i)α(i) W ≤ V means W is a subrep of V Example V : A1 . . . Ak , dim V = (n, n), σ = (1, −1) ←→ linear matrix A = k a=1 xa Aa dim W1 − dim W2 ≤ 0 (W1 ≤ Cn, k Aa W1 ≤ W2 ) 12 / 34
  21. King’s Criterion [King 1994] A characterization of σ-semistability by a

    linear system: King’s criterion A rep V of an acyclic quiver is σ-semistable ⇐⇒ σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) σ(α) := i∈Q0 σ(i)α(i) W ≤ V means W is a subrep of V Example V : A1 . . . Ak , dim V = (n, n), σ = (1, −1) ←→ linear matrix A = k a=1 xa Aa dim W − dim k Aa W ≤ 0 (W ≤ Cn) Nc-nonsingularity of A 12 / 34
  22. King’s Criterion: necessity of σ(dim V ) = 0 As

    we’ve seen, there is p : Q0 → R s.t. p(ha) − p(ta) ≤ −1 (a ∈ Q1 ). If σ(dim V ) ̸= 0, we can further assume i σ(i) dim V (i)p(i) ≤ −1 by shifting p. 13 / 34
  23. King’s Criterion: necessity of σ(dim V ) = 0 As

    we’ve seen, there is p : Q0 → R s.t. p(ha) − p(ta) ≤ −1 (a ∈ Q1 ). If σ(dim V ) ̸= 0, we can further assume i σ(i) dim V (i)p(i) ≤ −1 by shifting p. For gi = tp(i)I (i ∈ Q0 ) with t ∈ R, (g · V )(a) = t p(ha)−p(ta) V (a) and χσ (g) = t i σ(i)p(i) dim V (i) . Both go to 0 as t → +∞. 13 / 34
  24. King’s Criterion: necessity of σ(dim W) ≤ 0 By basis

    change, one can assume block upper triangular form: V (a) = W(ta) W(ta)⊥ W(ha) W(a) Y (a) W(ha)⊥ Z(a) For gi = tΠW(i) (i ∈ Q0 ), where ΠW(i) is the orthogonal projection matrix onto W(i), (g · V )(a) = t−1 1 t W(a) tY (a) 1 Z(a) → W(a) Z(a) (t → 0) and χσ (g) = tσ(dim W) → 0 (t → 0) if σ(dim W) > 0. Finally, W, Z can be brought to 0 individually. 14 / 34
  25. King’s Criterion: submodularity Subreps of V form a modular lattice:

    If W1 , W2 are subreps of V , then (W1 + W2 )(i) := W1 (i) + W2 (i), (W1 ∩ W2 )(i) := W1 (i) ∩ W2 (i), are also subreps of V . Furthermore, dim W1 + dim W2 = dim(W1 + W2 ) + dim(W1 ∩ W2 ). King’s criterion σ(dim W) ≤ 0 (W ≤ V ) can be checked by maximizing modular function σ(dim W) over the modular lattice of subreps. (But unfortunately, there is no known algorithm for submodular func minimization on modular lattice yet.) 15 / 34
  26. Our results Our results 1 We develop new deterministic algorithms

    for fundamental tasks on the σ-semistability of quiver reps: • deciding the σ-semistability • finding a maximizer of King’s criterion • finding the Harder–Narasimhan filtration improves previous time complexity [Chindris, Derksen 2021; Cheng 2024] 2 We also introduce a new polyhedral cone corresponding to King’s criterion (King’s cone). We show: • King’s cone of rank-one reps ⊂ submodular flow polytope. • Corollary: σ-semistability of rank-one reps can be decided in strongly polynomial time. 16 / 34
  27. Related Work operator scaling [Gurvits 2004; Franks 2018; Burgisser, Franks,

    Garg, Oliveira, Walter, Wigderson 2018] ≈ bipartite quiver σ-semistability of quiver reps this work noncommutative optimization [Bürgisser, Franks, Garg, Oliveira, Walter, Wigderson 2019] geometric invariant theory • Combinatorial structure is unclear? • Not always polytime (can be exponential in dims) • Can exploit combinatorial structure (submodularity) • poly(Q, α, bit(V ), |σ|) time for various tasks (pseudopoly only in σ) 17 / 34
  28. Reduction to Bipartite Quivers [Derksen, Makam 2017] Let • Q+

    0 = {i ∈ Q0 : σ(i) > 0} ... sources • Q− 0 = {i ∈ Q0 : σ(i) < 0} ... sinks • Q0 0 = Q0 \ (Q+ 0 ∪ Q− 0 ) ... rest 18 / 34
  29. Reduction to Bipartite Quivers [Derksen, Makam 2017] Let • Q+

    0 = {i ∈ Q0 : σ(i) > 0} ... sources • Q− 0 = {i ∈ Q0 : σ(i) < 0} ... sinks • Q0 0 = Q0 \ (Q+ 0 ∪ Q− 0 ) ... rest Consider the bipartite quiver representing all Q+ 0 –Q− 0 paths of Q: Q+ 0 Q− 0 1 2 3 4 5 6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 Q+ 0 Q− 0 1 6 4 5 9 a1 a2 a3 a1 a5 a8 a6 a3 a7 a8 a6 a4 a 7a 8a 9 18 / 34
  30. Reduction to Bipartite Quivers [Derksen, Makam 2017] Q+ 0 Q−

    0 1 2 3 4 5 6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 Q+ 0 Q− 0 1 6 4 5 9 a1 a2 a3 a1 a5 a8 a6 a3 a7 a8 a6 a4 a 7a 8a 9 For a path P = a1 a2 · · · ak in Q, define V (P) := V (ak ) · · · V (a2 )V (a1 ). V induces a rep V ′ on the bipartite quiver by assigning V (P) to each path P. 19 / 34
  31. Reduction to Bipartite Quivers [Derksen, Makam 2017] Q+ 0 Q−

    0 1 2 3 4 5 6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 Q+ 0 Q− 0 1 6 4 5 9 a1 a2 a3 a1 a5 a8 a6 a3 a7 a8 a6 a4 a 7a 8a 9 For a path P = a1 a2 · · · ak in Q, define V (P) := V (ak ) · · · V (a2 )V (a1 ). V induces a rep V ′ on the bipartite quiver by assigning V (P) to each path P. Theorem ([Derksen, Makam 2017; Huszar 2021]) V is σ-semistable ⇐⇒ V ′ is σ′-semistable, where σ′ is the restriction of σ to Q+ 0 ˙ ∪Q− 0 19 / 34
  32. Operator Scaling with Specified Marginals [Franks 2018] Given: matrices A1

    , . . . , Ak ∈ Cm×n, marginals (p, q) ∈ Rm ↓ × Rn ↓ , ε > 0 Find: upper triangular (g, h) ∈ GL(m) × GL(n) s.t. k i=1 gAi h†hAi g† ≈ε Diag(p), k i=1 hA† i g†gAi h† ≈ε Diag(q) (A ≈ε B means ∥A − B∥tr ≤ ε) Applications Edmond’s problem [Garg, Gurvits, Oliveira, Wigderson 2019], Brascamp-Lieb polytopes [Garg, Gurvits, Oliveira, Wigderson 2019], statistics [Franks, Moitra 2020], etc. 20 / 34
  33. Operator Sinkhorn Iteration [Gurvits 2004; Franks 2018] Until convergence do:

    1 Compute the Cholesky decomposition CC† = k i=1 ˜ Ai ˜ A† i . Set g = Diag(p)1/2C−1 and ˜ Ai ← g ˜ Ai . (left normalization) 2 Compute the Cholesky decomposition CC† = k i=1 ˜ A† i ˜ Ai . Set h = Diag(q)1/2C−1 and ˜ Ai ← ˜ Ai h†. (right normalization) 21 / 34
  34. Operator Sinkhorn Iteration [Gurvits 2004; Franks 2018] Until convergence do:

    1 Compute the Cholesky decomposition CC† = k i=1 ˜ Ai ˜ A† i . Set g = Diag(p)1/2C−1 and ˜ Ai ← g ˜ Ai . (left normalization) 2 Compute the Cholesky decomposition CC† = k i=1 ˜ A† i ˜ Ai . Set h = Diag(q)1/2C−1 and ˜ Ai ← ˜ Ai h†. (right normalization) Theorem ([Burgisser, Franks, Garg, Oliveira, Walter, Wigderson 2018]) If a solution exists, then the operator Sinkhorn iteration converges to a solution in O(ε−2(b + N log(ℓN))) iterations (b: maximum bit length of Ai , N := max{m, n}, ℓ: the smallest positive integer s.t. ℓ(p, q) is integral). 21 / 34
  35. Block Structured Operators Input matrices and marginals often have a

    block structure: V11 O O O , O V12 O O , O O V21 O , O O O V22 , p = p(1)1 p(2)1 , q = q(1)1 q(2)1 In such cases, (g, h) can be taken to have the same block structure: g = g1 g2 , h = h1 h2 22 / 34
  36. Block Structured Operators Input matrices and marginals often have a

    block structure: V11 O O O , O V12 O O , O O V21 O , O O O V22 , p = p(1)1 p(2)1 , q = q(1)1 q(2)1 In such cases, (g, h) can be taken to have the same block structure: g = g1 g2 , h = h1 h2 Such block structured input can be seen as a representation and weight of a bipartite quiver Q = (Q+ 0 ˙ ∪Q− 0 , Q1 ): 1 2 1 2 q(1) q(2) p(1) p(2) Q+ 0 Q− 0 V11 V12 V21 V22 22 / 34
  37. σ-Semistability and Operator Scaling Theorem ([Burgisser, Franks, Garg, Oliveira, Walter,

    Wigderson 2018]) For block structured inputs, TFAE: 1 For any ε > 0, there exists (g, h) satisfying the scaling condition. 2 i∈Q+ 0 q(i) dim V (i) = i∈Q− 0 p(i) dim V (i) and s∈Q+ 0 q(s) dim W(s) ≤ t∈Q− 0 p(t) dim s∈Q+ 0 V (st)W(s) for any collection of subspaces W(s) ≤ V (s) (s ∈ Q+ 0 ). Furthermore, ε = O(1/ℓ) is sufficient to check the conditions. 1 2 1 2 q(1) q(2) p(1) p(2) Q+ 0 Q− 0 V11 V12 V21 V22 King’s criterion for rep of a bipartite quiver and weight σ = (q; −p). ℓ: the smallest positive integer s.t. ℓ(p, q) is integral. 23 / 34
  38. Reduction of General Quiver Operator scaling in bipartite quiver after

    Derksen-Makam reduction is as follows: Quiver Rep Scaling Given: Quiver rep V ∈ Rep(Q, α), weight σ Find: g = t∈Q− 0 gt and h = s∈Q+ 0 hs s.t. s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t ≈ε |σ(t)|Iα(t) (t ∈ Q− 0 ), t∈Q− 0 P:s–t path hs V (P)†g† t gt V (P)h† s ≈ε |σ(s)|Iα(s) (s ∈ Q+ 0 ) 24 / 34
  39. Example Q+ 0 Q− 0 1 2 3 4 5

    6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 +2 +2 −1 −2 −1 Constraint for sink 5 : Paths to 5 : P1 = (1, 2, 3, 5), P2 = (1, 2, 7, 8, 3, 5), P3 = (6, 7, 8, 3, 5) g5 V (P1 )h† 1 h1 V (P1 )†g† 5 + g5 V (P2 )h† 1 h1 V (P2 )†g† 5 + g5 V (P3 )h† 6 h6 V (P3 )†g† 5 = 2I 25 / 34
  40. Key Observation Q+ 0 Q− 0 1 2 3 4

    5 6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 Q+ 0 Q− 0 1 6 4 5 9 a1 a2 a3 a1 a5 a8 a6 a3 a7 a8 a6 a4 a 7a 8a 9 One can show: • King’s criterion ←→ scalability condition of block-structured operator scaling. • The latter cond can be checked by operator Sinkhorn with ε = O(1/σ+(α)). 26 / 34
  41. Key Observation To run operator Sinkhorn iteration, we need to

    compute matrix sum-products over s–t paths: s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t for t ∈ Q− 0 and vice versa. How can we compute it in polytime? 27 / 34
  42. Key Observation To run operator Sinkhorn iteration, we need to

    compute matrix sum-products over s–t paths: s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t for t ∈ Q− 0 and vice versa. How can we compute it in polytime? Observation This matrix sum-products can be computed in a bottom up manner in acyclic quivers. 1 2 3 4 a1 a3 a2 X1 ← h1 h† 1 X2 ← h2 h† 2 X3 ← V (a1 )X1 V (a1 )† +V (a2 )X2 V (a2 )† X4 ← V (a3 )X3 V (a3 )† desired matrix sum-product! 27 / 34
  43. Key Observation To run operator Sinkhorn iteration, we need to

    compute matrix sum-products over s–t paths: s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t for t ∈ Q− 0 and vice versa. How can we compute it in polytime? Observation This matrix sum-products can be computed in a bottom up manner in acyclic quivers. 1 2 3 4 a1 a3 a2 X1 ← h1 h† 1 X2 ← h2 h† 2 X3 ← V (a1 )X1 V (a1 )† +V (a2 )X2 V (a2 )† X4 ← V (a3 )X3 V (a3 )† desired matrix sum-product! 27 / 34
  44. Key Observation To run operator Sinkhorn iteration, we need to

    compute matrix sum-products over s–t paths: s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t for t ∈ Q− 0 and vice versa. How can we compute it in polytime? Observation This matrix sum-products can be computed in a bottom up manner in acyclic quivers. 1 2 3 4 a1 a3 a2 X1 ← h1 h† 1 X2 ← h2 h† 2 X3 ← V (a1 )X1 V (a1 )† +V (a2 )X2 V (a2 )† X4 ← V (a3 )X3 V (a3 )† desired matrix sum-product! 27 / 34
  45. Key Observation To run operator Sinkhorn iteration, we need to

    compute matrix sum-products over s–t paths: s∈Q+ 0 P:s–t path gt V (P)h† s hs V (P)†g† t for t ∈ Q− 0 and vice versa. How can we compute it in polytime? Observation This matrix sum-products can be computed in a bottom up manner in acyclic quivers. Theorem Operator Sinkhorn (with matrix sum-product computation) decides the σ-semistability of V in time polynomial in the size of Q, α(Q0 ), bit complexity of V , and absolute values of the entries of σ. 27 / 34
  46. King’s Polyhedral Cone King’s criterion A rep V of an

    acyclic quiver is σ-semistable if and only if σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) 28 / 34
  47. King’s Polyhedral Cone King’s criterion A rep V of an

    acyclic quiver is σ-semistable if and only if σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) King’s cone For a rep V of an acyclic quiver, define King’s cone as a set of σ ∈ RQ0 s.t. σ(dim V ) = 0 and σ(dim W) ≤ 0 (W ≤ V ) This polyhedral cone has nice connections to network flow! 28 / 34
  48. Toy Example: Scalar Representations Suppose that V (a) is a

    scalar for all arc a ∈ Q1 , i.e., dim V = 1. Define the support quiver Q(V ) = (Q0 , Q1 (V )) by Q1 (V ) = {a ∈ Q1 : V (a) ̸= 0}. Obs. dim W ∈ {0, 1}Q0 (W ≤ V ) corresponds to a vertex set X ⊆ Q0 in Q1 (V ) s.t. no arc goes out of X (lower set). X a1 a2 a3 a4 a7 a8 a9 a5 a6 +2 +2 −1 −2 −1 29 / 34
  49. Toy Example: Scalar Representations King’s criterion reads: σ(Q0 ) =

    0 and σ(X) ≤ 0 (X ⊆ Q0 : lower set) ⇐⇒ ∃ σ-flow in Q(V ), where the arc capacity ≡ +∞. σ(X) := i∈X σ(i) 1 2 3 4 5 6 7 8 9 a1 a2 a3 a4 a7 a8 a9 a5 a6 +2 +2 −1 −2 −1 30 / 34
  50. Toy Example: Scalar Representations King’s criterion reads: σ(Q0 ) =

    0 and σ(X) ≤ 0 (X ⊆ Q0 : lower set) ⇐⇒ ∃ σ-flow in Q(V ), where the arc capacity ≡ +∞. σ(X) := i∈X σ(i) Corollary For a scalar rep, • King’s cone is the set of the flow-boundaries in the support quiver. • σ-semistablity of scalar reps can be decided in strongly polytime. 30 / 34
  51. Rank-one Representations Suppose that V (a) = va f† a

    (i.e., rank-one) for all arc a ∈ Q1 . Q G • subrep W ≤ V ←→ lower set X in G • King’s criterion reads: i∈Q0 (σ+(i) dim⟨fa / ∈ X⟩ + σ−(i) dim⟨vb ∈ X⟩) ≥ σ(Q+ 0 ) (X: lower set) ... feasiblility of submodular flow in G 31 / 34
  52. Rank-one Representations Q v1 f† 1 v2 f† 2 v3

    f† 3 v4 f† 4 v5 f† 5 f1 f2 v1 v2 f3 f4 f5 v3 v4 v5 edge exists if fa †vb ̸= 0 G 32 / 34
  53. Rank-one Representations Q v1 f† 1 v2 f† 2 v3

    f† 3 v4 f† 4 v5 f† 5 f1 f2 v1 v2 f3 f4 f5 v3 v4 v5 edge exists if fa †vb ̸= 0 G correspondence • subrep W −→ lower X = {va ∈ W(ha)}a∈Q1 ∪ {fa / ∈ W(ta)⊥}a∈Q1 • lower X −→ subrep W(i) = ⟨fa / ∈ X : a ∈ Out(i)⟩⊥ (i ∈ Q+ 0 ) ⟨va ∈ X : a ∈ In(i)⟩ (i / ∈ Q+ 0 ) 32 / 34
  54. Rank-one Representations Submodular flow f1 f2 v1 v2 f3 f4

    f5 v3 v4 v5 linearly independent linearly independent 33 / 34
  55. Rank-one Representations Submodular flow f1 f2 v1 v2 f3 f4

    f5 v3 v4 v5 linearly independent linearly independent Theorem For a rank-one rep, • King’s cone is the set of the boundaries of submodular flow in the auxiliary graph G. • σ-semistablity of rank-one reps can be decided in strongly polytime by the feasibility test of submodular flow. 33 / 34
  56. Conclusion Summary • We devised algorithms for σ-semistability of quiver

    reps. • σ-semistability is decidable in strongly polynomial time for rank-one reps. Open Problems • Can we improve the time complexity dependence on |σ| to log |σ|? (Nontrivial even for star quivers! cf. Brascamp-Lieb polytope membership) • Can we decide σ-semistability for rank-2 reps in strongly polytime? (recent result: possible for star quivers [Franks, Soma, Goemans 2023; Hirai, Iwamasa, Oki, Soma 2024; Oki, Soma 2025]) • Applications? (cf. mixed matrices [Murota 1999]) 34 / 34