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An output-sensitive algorithm for computing
(projections of) resultant polytopes
Vissarion Fisikopoulos
Joint work with I....
An interesting class of polytopes: resultant polytopes
Geometry: Minkowski summands of secondary polytopes,
equivalence cl...
Existing work

Theory of resultants, secondary polytopes, Cayley trick [GKZ ’94]
TOPCOM [Rambau ’02] computes all vertices...
What is a resultant polytope?
Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn

A0

a1

A1

a3

a2
a4
What is a resultant polytope?
Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn
∪n
A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0...
What is a resultant polytope?
Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn
∪n
A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0...
What is a resultant polytope?
Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn
∪n
A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0...
What is a resultant polytope?
Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn
∪n
A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0...
Connection with Algebra

The Newton polytope of f, N(f), is the convex hull of the set of
exponents of its monomials with ...
Connection with Algebra

The Newton polytope of f, N(f), is the convex hull of the set of
exponents of its monomials with ...
Connection with Algebra

The Newton polytope of f, N(f), is the convex hull of the set of
exponents of its monomials with ...
The idea of the algorithm
Input: A ∈ Z2n defined by A0 , A1 , . . . , An ⊂ Zn
Simplistic method:
compute the secondary poly...
The idea of the algorithm
Input: A ∈ Z2n defined by A0 , A1 , . . . , An ⊂ Zn
New Algorithm:
Vertex oracle: given a directi...
The Oracle
Input: A ⊂ Z2n , direction w ∈ (R|A| )×
Output: vertex ∈ N(R), extremal wrt w
1. use w as a lifting to construc...
The Oracle
Input: A ⊂ Z2n , direction w ∈ (R|A| )×
Output: vertex ∈ N(R), extremal wrt w
1. use w as a lifting to construc...
The Oracle
Input: A ⊂ Z2n , direction w ∈ (R|A| )×
Output: vertex ∈ N(R), extremal wrt w
1. use w as a lifting to construc...
The Oracle

Input: A ⊂ Z2n , direction w ∈ (R|A| )×
Output: vertex ∈ N(R), extremal wrt w
1. use w as a lifting to constru...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step

initialization:
Q ⊂ N(R)
...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Incremental Algorithm
Input: A
Output: H-rep. QH , V-rep. QV of Q = N(R)
1. initialization step
2. all hyperplanes of QH a...
Complexity
Theorem
We compute the Vertex- and Halfspace-representations of N(R), as well
as a triangulation T of N(R), in
...
ResPol package

C++
CGAL, triangulation [Boissonnat,Devillers,Hornus]
extreme points d [G¨rtner] (preprocessing step)
a
Ha...
Output-sensitivity

oracle calls ≤ vtx(N(R)) + fct(N(R))
output vertices bound polynomially the output triangulation size
...
Hashing and Gfan

hashing determinants speeds ≤ 10-100x when dim(N(R)) = 3, 4
faster than Gfan [Yu-Jensen’11] for dimN(R) ...
Ongoing and future work
approximate resultant polytopes (dim(N(R)) ≥ 7) using approximate
volume computation
combinatorial...
Ongoing and future work

(figure courtesy of M.Joswig)

Facet and vertex graph of the largest 4-dimensional resultant polyt...
Convex hull implementations
From V- to H-rep. of N(R).
triangulation (on/off-line), polymake beneath-beyond,
cdd, lrs

tim...
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An algorithm for computing resultant polytopes

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Presentation of the paper "An output-sensitive algorithm for computing (projections of) resultant polytopes" in the Annual Symposium on Computational Geometry (SoCG 2012)

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An algorithm for computing resultant polytopes

  1. 1. An output-sensitive algorithm for computing (projections of) resultant polytopes Vissarion Fisikopoulos Joint work with I.Z. Emiris, C. Konaxis and L. Pe˜aranda n Department of Informatics, University of Athens SoCG, Chapel Hill, NC, USA, 18.Jun.2012
  2. 2. An interesting class of polytopes: resultant polytopes Geometry: Minkowski summands of secondary polytopes, equivalence classes of secondary vertices, generalization of Birkhoff polytopes Motivation: useful to express the solvability of polynomial systems Applications: discriminant and resultant computation, implicitization of parametric hypersurfaces Enneper’s Minimal Surface
  3. 3. Existing work Theory of resultants, secondary polytopes, Cayley trick [GKZ ’94] TOPCOM [Rambau ’02] computes all vertices of secondary polytope. [Michiels & Verschelde DCG’99] define and enumerate coarse equivalence classes of secondary polytope vertices. [Michiels & Cools DCG’00] describe a decomposition of Σ(A) in Minkoski summands, including N(R). Tropical geometry [Sturmfels-Yu ’08] leads to algorithms for the resultant polytope (GFan library) [Jensen-Yu ’11] and the discriminant polytope (TropLi software) [Rincón ’12].
  4. 4. What is a resultant polytope? Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn A0 a1 A1 a3 a2 a4
  5. 5. What is a resultant polytope? Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn ∪n A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0, . . . , 1, . . . , 0) ⊂ Zn A0 a1 A1 a3 A a3 , 1 a1 , 0 a2 a4 a4 , 1 a2 , 0
  6. 6. What is a resultant polytope? Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn ∪n A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0, . . . , 1, . . . , 0) ⊂ Zn Given T a triangulation of conv(A), a cell is a-mixed if it is the Minkowski sum of n 1-dimensional segments from Aj , j = i, and some vertex a ∈ Ai . A0 a1 A1 a3 A a3 , 1 a1 , 0 a2 a4 a4 , 1 a2 , 0
  7. 7. What is a resultant polytope? Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn ∪n A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0, . . . , 1, . . . , 0) ⊂ Zn Given T a triangulation of conv(A), a cell is a-mixed if it is the Minkowski sum of n 1-dimensional segments from Aj , j = i, and some vertex a ∈ Ai . ∑ a∈A ρT (a) = a−mixed vol(σ) ∈ N, σ∈T:a∈σ A0 a1 A1 a3 A a3 , 1 a1 , 0 ρT = (0, 2, 1, 0) a2 a4 a4 , 1 a2 , 0
  8. 8. What is a resultant polytope? Given n + 1 point sets A0 , A1 , . . . , An ⊂ Zn ∪n A = i=0 (Ai × {ei }) ⊂ Z2n where ei = (0, . . . , 1, . . . , 0) ⊂ Zn Given T a triangulation of conv(A), a cell is a-mixed if it is the Minkowski sum of n 1-dimensional segments from Aj , j = i, and some vertex a ∈ Ai . ∑ ρT (a) = a∈A a−mixed vol(σ) ∈ N, σ∈T:a∈σ Resultant polytope N(R) = conv(ρT : T triang. of conv(A)) A0 A1 A N (R)
  9. 9. Connection with Algebra The Newton polytope of f, N(f), is the convex hull of the set of exponents of its monomials with non-zero coefficient. The resultant R is the polynomial in the coefficients of a system of polynomials which is zero iff the system has a common solution. A0 f0 (x) = ax2 + b A1 f1 (x) = cx2 + dx + e N (R) R(a, b, c, d, e) = ad2 b + c2 b2 − 2caeb + a2 e2
  10. 10. Connection with Algebra The Newton polytope of f, N(f), is the convex hull of the set of exponents of its monomials with non-zero coefficient. The resultant R is the polynomial in the coefficients of a system of polynomials which is zero iff the system has a common solution. A0 f0 (x, y) = ax + by + c A1 f1 (x, y) = dx + ey + f A2 f2 (x, y) = gx + hy + i N (R) 4-dimensional Birkhoff polytope R(a, b, c, d, e, f, g, h, i) = abc def ghi
  11. 11. Connection with Algebra The Newton polytope of f, N(f), is the convex hull of the set of exponents of its monomials with non-zero coefficient. The resultant R is the polynomial in the coefficients of a system of polynomials which is zero iff the system has a common solution. A0 f0 (x, y) = axy 2 + x4 y + c A1 f1 (x, y) = dx + ey A2 f2 (x, y) = gx2 + hy + i N (R) NP-hard to compute the resultant in the general case
  12. 12. The idea of the algorithm Input: A ∈ Z2n defined by A0 , A1 , . . . , An ⊂ Zn Simplistic method: compute the secondary polytope Σ(A) many-to-one relation between vertices of Σ(A) and N(R) vertices Cannot enumerate 1 representative per class by walking on secondary edges
  13. 13. The idea of the algorithm Input: A ∈ Z2n defined by A0 , A1 , . . . , An ⊂ Zn New Algorithm: Vertex oracle: given a direction vector compute a vertex of N(R) Output sensitive: computes only one triangulation of A per N(R) vertex + one per N(R) facet Computes projections of N(R) or Σ(A)
  14. 14. The Oracle Input: A ⊂ Z2n , direction w ∈ (R|A| )× Output: vertex ∈ N(R), extremal wrt w 1. use w as a lifting to construct regular subdivision S of A S w face of Σ(A)
  15. 15. The Oracle Input: A ⊂ Z2n , direction w ∈ (R|A| )× Output: vertex ∈ N(R), extremal wrt w 1. use w as a lifting to construct regular subdivision S of A 2. refine S into triangulation T of A T S w T face of Σ(A)
  16. 16. The Oracle Input: A ⊂ Z2n , direction w ∈ (R|A| )× Output: vertex ∈ N(R), extremal wrt w 1. use w as a lifting to construct regular subdivision S of A 2. refine S into triangulation T of A 3. return ρT ∈ N|A| ρT T S w T face of Σ(A) N (R)
  17. 17. The Oracle Input: A ⊂ Z2n , direction w ∈ (R|A| )× Output: vertex ∈ N(R), extremal wrt w 1. use w as a lifting to construct regular subdivision S of A 2. refine S into triangulation T of A 3. return ρT ∈ N|A| Oracle property: its output is a vertex of the target polytope (Lem. 5).
  18. 18. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step initialization: Q ⊂ N(R) dim(Q)=dim(N(R)) Q N (R)
  19. 19. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 2 kinds of hyperplanes of QH : legal if it supports facet ⊂ N(R) illegal otherwise Q N (R)
  20. 20. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} Extending an illegal facet Q w N (R)
  21. 21. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Extending an illegal facet Q N (R)
  22. 22. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Validating a legal facet Q N (R)
  23. 23. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Validating a legal facet Q N (R)
  24. 24. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  25. 25. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / At any step, Q is an inner approximation . . . Q N (R)
  26. 26. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Qo At any step, Q is an inner approximation . . . from which we can compute an outer approximation Qo . Q N (R)
  27. 27. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  28. 28. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  29. 29. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  30. 30. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  31. 31. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  32. 32. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  33. 33. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  34. 34. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  35. 35. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  36. 36. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  37. 37. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  38. 38. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  39. 39. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  40. 40. Incremental Algorithm Input: A Output: H-rep. QH , V-rep. QV of Q = N(R) 1. initialization step 2. all hyperplanes of QH are illegal 3. while ∃ illegal hyperplane H ⊂ QH with outer normal w do call oracle for w and compute v, QV ← QV ∪ {v} if v ∈ QV ∩ H then QH ← CH(QV ∪ {v}) else H is legal / Q N (R)
  41. 41. Complexity Theorem We compute the Vertex- and Halfspace-representations of N(R), as well as a triangulation T of N(R), in O∗ ( m5 |vtx(N(R))| · |T|2 ), where m = dim N(R), and |T| the number of full-dim faces of T. Elements of proof Computation is done in dimension m = |A| − 2n + 1. At most ≤ vtx(N(R)) + fct(N(R)) oracle calls (Lem. 9). Beneath-and-Beyond algorithm for converting V-rep. to H-rep [Joswig ’02].
  42. 42. ResPol package C++ CGAL, triangulation [Boissonnat,Devillers,Hornus] extreme points d [G¨rtner] (preprocessing step) a Hashing of determinantal predicates: optimizing sequences of similar determinants http://sourceforge.net/projects/respol Applications of ResPol on I.Emiris talk this afternoon (CGAL, an Open Gate to Computational Geometry!)
  43. 43. Output-sensitivity oracle calls ≤ vtx(N(R)) + fct(N(R)) output vertices bound polynomially the output triangulation size subexponential runtime wrt to input points (L), output vertices (R)
  44. 44. Hashing and Gfan hashing determinants speeds ≤ 10-100x when dim(N(R)) = 3, 4 faster than Gfan [Yu-Jensen’11] for dimN(R) ≤ 6, else competitive dim(N(R)) = 4:
  45. 45. Ongoing and future work approximate resultant polytopes (dim(N(R)) ≥ 7) using approximate volume computation combinatorial characterization of 4-dimensional resultant polytopes computation of discriminant polytopes More on I.Emiris talk this afternoon (CGAL, an Open Gate to Computational Geometry!) (figure courtesy of M.Joswig) Facet and vertex graph of the largest 4-dimensional resultant polytope
  46. 46. Ongoing and future work (figure courtesy of M.Joswig) Facet and vertex graph of the largest 4-dimensional resultant polytope Thank You !
  47. 47. Convex hull implementations From V- to H-rep. of N(R). triangulation (on/off-line), polymake beneath-beyond, cdd, lrs time (sec) 100 10 1 triang_on 0.1 0.01 bb cdd lrs triang_off 0 500 1000 1500 2000 Number of points 2500 3000 dim(N(R)) = 4

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