Numerical Methods for Hodge Decompositionlekheng/meetings/hodge/hirani.pdfGraphs: 1-cochains...
Transcript of Numerical Methods for Hodge Decompositionlekheng/meetings/hodge/hirani.pdfGraphs: 1-cochains...
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Numerical Methods for Hodge Decomposition
Anil N. Hirani ∗
K. Kalyanaraman, H. Wang, S. Watts
(Some with A. Demlow)
∗ Department of Computer ScienceUniversity of Illinois at Urbana-Champaign
Applied Hodge Theory MinisymposiumICIAM 2011, VancouverTuesday, July 19, 2011
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Triangle meshes: 1-cochains
Tetrahedral meshes: 1- and 2-cochains
Manifolds: Differential forms and vector fields
Graphs: 1-cochains
gradients⊕ curls⊕ harmonics
Computing Hodge Decomposition:
Find two and infer third by subtraction
Curl and harmonic part are harder
Find gradient part, and curl OR harmonic part
Elementary in principle, but in practice:
Functional Analysis
Numerical Linear Algebra
Computational Geometry
Computational Topology
are all connected to the problem.2/31
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Functional Analysis :
Convergence and stability
Numerical Linear Algebra :
Least squares for theory and practice
On graphs conjugate gradient beats multigrid
Computational Geometry :
Meshing affects choice of method
Mesh properties for near-optimal solve time
Computational Topology :
Use (co)homology basis for harmonics
Experiments on clique complexes
1-norm and linear programming
Only the highlighted topics will be discussed
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Functional Analysis :
Convergence and stability
Numerical Linear Algebra :
Least squares for theory and practice
On graphs conjugate gradient beats multigrid
Computational Geometry :
Meshing affects choice of method
Mesh properties for near-optimal solve time
Computational Topology :
Use (co)homology basis for harmonics
Experiments on clique complexes
1-norm and linear programming
Only the highlighted topics will be discussed
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Functional Analysis :
Convergence and stability
Numerical Linear Algebra :
Least squares for theory and practice
On graphs conjugate gradient beats multigrid
Computational Geometry :
Meshing affects choice of method
Mesh properties for near-optimal solve time
Computational Topology :
Use (co)homology basis for harmonics
Experiments on clique complexes
1-norm and linear programming
Only the highlighted topics will be discussed3/31
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Hodge Decomposition in Finite DimensionsA pattern that appears in Hodge decomposition on graphs and meshes
U, V , W finite-dimensional inner product spaces
UA−−−−→ V
B−−−−→ Wy y yU∗
AT
←−−−− V ∗BT
←−−−− W ∗
B ◦ A = 0
V = imA⊕ imA⊥ = imA⊕ kerAT
V = imA⊕ imBT ⊕ (kerB ∩ kerAT )
V = imA⊕ imBT ⊕ ker ∆
∆ = AAT + BTB
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Laplace-deRham Operators on GraphsChain and cochain complexes on 2-dimensional clique complex of graph G
C 0(G )∂T1−−−−→ C 1(G )
∂T2−−−−→ C 2(G )y y yC0(G )
∂1←−−−− C1(G )∂2←−−−− C2(G )
∆0 = ∂1 ∂T1
∆1 = ∂T1 ∂1 + ∂2 ∂T2
∆2 = ∂T2 ∂2
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Hodge Decomposition on GraphsUseful for least squares ranking on graphs
ω = ∂T1 α + ∂2 β + h
∂1 ∂T1 a = ∂1 ω ∂T2 ∂2 b = ∂T2 ω
∆0 a = ∂1 ω ∆2 b = ∂T2 ω
∂T1 a ' ω∂2 b ' ω
mina‖r‖2 such that r = ω − ∂T1 a
minb‖s‖2 such that s = ω − ∂2 b
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Hodge Decomposition on GraphsUseful for least squares ranking on graphs
ω = ∂T1 α + ∂2 β + h
∂1 ∂T1 a = ∂1 ω ∂T2 ∂2 b = ∂T2 ω
∆0 a = ∂1 ω ∆2 b = ∂T2 ω
∂T1 a ' ω∂2 b ' ω
mina‖r‖2 such that r = ω − ∂T1 a
minb‖s‖2 such that s = ω − ∂2 b
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Laplace-deRham Operators on MeshesDiscretization of weak form
(∆ u, v) = (d u, d v) + (δ u, δ v)
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Laplace-deRham Operators on MeshesDiscretization of weak form
(∆ u, v) = (d u, d v) + (δ u, δ v)
C 0 d0−−−−→ C 1 d1−−−−→ C 2y∗0 y∗1 y∗2D2 dT0←−−−− D1 dT1←−−−− D0
∆0 = dT0 ∗1 d0
∆1 = dT1 ∗2 d1 + ∗1 d0 ∗−10 dT
0 ∗1∆2 = − ∗2 d1 ∗−11 dT
1 ∗2
Highlighted part problematic unless inverse Hodge star can behandled easily
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Laplace-deRham Operators on MeshesDiscretization of weak form
(∆ u, v) = (d u, d v) + (δ u, δ v)
C 0 d0−−−−→ C 1 d1−−−−→ C 2y∗0 y∗1 y∗2D2 dT0←−−−− D1 dT1←−−−− D0
∆0 = dT0 ∗1 d0
∆1 = dT1 ∗2 d1 + ∗1 d0 ∗−10 dT
0 ∗1∆2 = − ∗2 d1 ∗−11 dT
1 ∗2
Highlighted part problematic unless inverse Hodge star can behandled easily
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Laplace-deRham Operators on MeshesDiscretization of weak form
(∆ u, v) = (d u, d v) + (δ u, δ v)
C 0 d0−−−−→ C 1 d1−−−−→ C 2 d2−−−−→ C 3y∗0 y∗1 y∗2 y∗3D3 dT0←−−−− D2 dT1←−−−− D1 dT2←−−−− D0
∆0 = dT0 ∗1 d0
∆1 = dT1 ∗2 d1 + ∗1 d0 ∗−10 dT
0 ∗1∆2 = dT
2 ∗3 d2 + ∗2 d1 ∗−11 dT1 ∗2
∆3 = ∗3 d2 ∗−12 dT2 ∗3
Highlighted part problematic unless inverse Hodge star can behandled easily
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Laplace-deRham Operators on MeshesDiscretization of weak form
(∆ u, v) = (d u, d v) + (δ u, δ v)
C 0 d0−−−−→ C 1 d1−−−−→ C 2 d2−−−−→ C 3y∗0 y∗1 y∗2 y∗3D3 dT0←−−−− D2 dT1←−−−− D1 dT2←−−−− D0
∆0 = dT0 ∗1 d0
∆1 = dT1 ∗2 d1 + ∗1 d0 ∗−10 dT
0 ∗1∆2 = dT
2 ∗3 d2 + ∗2 d1 ∗−11 dT1 ∗2
∆3 = ∗3 d2 ∗−12 dT2 ∗3
Highlighted part problematic unless inverse Hodge star can behandled easily
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Naive Hodge Decomposition on MeshesLinear system for curl (β) part has inverse Hodge star
ω = dα + δ β + h
δ ω = δ d α
∗−1 dT ∗ d α = ∗−1 dT ∗ ωdT ∗ d α = dT ∗ ω
d ω = d δ β
d ∗−1 dT ∗β = d ω
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Strategy for Computing Hodge DecompositionDifferent for graphs and meshes
Graphs (no metric involved – Hodge star is Identity):
I Use least squares for gradient and curl
I Find harmonic component by subtraction
Manifold simplicial complexes (Hodge star is not Identity):
I Use weighted least squares for gradient part. In addition:
I Find harmonic basis as eigenvectors and project
OR
I Find harmonic basis by discrete Hodge-deRham and project
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Eigenvector Method for Harmonic BasisUse of weak form to avoid inverse Hodge stars
Consider the linear system for σ and u :
(σ, τ)− (dp−1 τ, u) = 0 ,
(dp−1 σ, v) + (dp u, dp v) = 0 ,
for all τ and v . Then (σ, u) is a solution if and only if σ = 0 and uis a harmonic p-form. [
∗p−1 − dTp−1 ∗p
∗p dp−1 dTp ∗p+1 dp
]Eigenvectors of 0 eigenvalue are harmonic cochains.
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Harmonic cochains using eigenvector method
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Discrete Hodge-deRham Method for Harmonic BasisAvoids inverse Hodge stars and in addition provides topological control
I Find a homology basis and its dual cohomology basis
I Find harmonic cochains cohomologous to cohomology basis
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Chain a homologous to b if a− b ∈ im ∂
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Cochain h cohomologous to ω if h − ω ∈ im d
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Poincare-Lefschetz duality:M manifold of dimension n
If ∂M = ∅, then Hp(M;R) ∼= Hn−p(M;R)
If ∂M 6= ∅, then Hp(M;R) ∼= Hn−p(M, ∂M;R)
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Naive Cohomologous Harmonic Cochain
Given [ω] ∈ Hp(M;R). Find α such that
∆ (ω + d α) = 0
Since ω ∈ ker d, above is equivalent to
d δ d α = − d δ ω
d ∗−1 dT ∗ d α = − d ∗−1 dT ∗ ω
Again, the inverse Hodge stars could be a problem
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Naive Cohomologous Harmonic Cochain
Given [ω] ∈ Hp(M;R). Find α such that
∆ (ω + d α) = 0
Since ω ∈ ker d, above is equivalent to
d δ d α = − d δ ω
d ∗−1 dT ∗ d α = − d ∗−1 dT ∗ ω
Again, the inverse Hodge stars could be a problem
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Naive Cohomologous Harmonic Cochain
Given [ω] ∈ Hp(M;R). Find α such that
∆ (ω + d α) = 0
Since ω ∈ ker d, above is equivalent to
d δ d α = − d δ ω
d ∗−1 dT ∗ d α = − d ∗−1 dT ∗ ω
Again, the inverse Hodge stars could be a problem
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Discrete Hodge-deRham MethodSmallest cochain in each cohomology class is harmonic
Given [ω] ∈ Hp(M;R). Find α such that
minα∈Cp−1
(ω + dα , ω + dα)
minα∈Cp−1
(ω + dα
)T ∗ (ω + dα)
dTp−1 ∗p dp−1 α = − dT
p−1 ∗p ω
Note : no inverse Hodge starsInspired by a result in the smooth case that smallest differentialform cohomologous to a given form is harmonic. Precise statementof theorem in discrete case given later.
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Discrete Hodge-deRham MethodSmallest cochain in each cohomology class is harmonic
Given [ω] ∈ Hp(M;R). Find α such that
minα∈Cp−1
(ω + dα , ω + dα)
minα∈Cp−1
(ω + dα
)T ∗ (ω + dα)
dTp−1 ∗p dp−1 α = − dT
p−1 ∗p ω
Note : no inverse Hodge stars
Inspired by a result in the smooth case that smallest differentialform cohomologous to a given form is harmonic. Precise statementof theorem in discrete case given later.
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Discrete Hodge-deRham MethodSmallest cochain in each cohomology class is harmonic
Given [ω] ∈ Hp(M;R). Find α such that
minα∈Cp−1
(ω + dα , ω + dα)
minα∈Cp−1
(ω + dα
)T ∗ (ω + dα)
dTp−1 ∗p dp−1 α = − dT
p−1 ∗p ω
Note : no inverse Hodge stars
Inspired by a result in the smooth case that smallest differentialform cohomologous to a given form is harmonic. Precise statementof theorem in discrete case given later.
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Sample Code for Harmonic Cochain ComputationUsing discrete Hodge-deRham method
from numpy import zeros , loadtxt
from numpy.linalg import inv
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import cg
from dlnyhdg.simplicial_complex import simplicial_complex
from pydec import whitney_innerproduct
tol = 1e-8; scale = 5; width = 0.001
vertices = loadtxt(’vertices.txt’)
triangles = loadtxt(’triangles.txt’, dtype=int)
sc = simplicial_complex ((vertices , triangles ))
edge_list , edge_orientation = loadtxt(’12 cocycle.txt’, dtype=int)
omega = zeros(sc[1]. num_simplices)
for i, e in enumerate(edge_list ):
omega[e] += edge_orientation[i]
d0 = sc[0].d
hodge1 = whitney_innerproduct(sc , 1)
A = d0.T * hodge1 * d0; b = -d0.T * hodge1 * omega
alpha = cg(A, b, tol=tol )[0]
harmonic = omega + d0 * alpha
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Cohomologous harmonic cochains using discrete Hodge-deRham method
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Cohomologous harmonic cochains using discrete Hodge-deRham method
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Cohomologous harmonic cochains using discrete Hodge-deRham method
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Compare with harmonic cochains using eigenvector method
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Cohomologous harmonic cochains using discrete Hodge-deRham method
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Isomorphism Theorem in Smooth CaseInspiration for the discrete Hodge-deRham method
Theorem (Hodge-deRham Isomorphism)
For a boundaryless manifold M (∂M = ∅),Hp(M;R) ∼= Hp(M) = ker ∆p. For manifolds with boundary,Hp(M;R) ∼= Hp
N(M) and Hp(M, ∂M;R) ∼= HpD(M).
Harmonic forms : ker ∆Harmonic fields : H(M) = ker d∩ ker δHarmonic Neumann fields : HN(M) normal component 0Harmonic Dirichlet fields : HD(M) tangential component 0
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Isomorphism Theorem in Discrete Case
Theorem (Discrete Hodge-deRham Isomorphism, H-K-W-W)
Let [ω] ∈ Hp(K ;R). Then
1. There exists a cochain α ∈ Cp−1(K ;R), not necessarilyunique, such that δp (ω + dp−1 α) = 0;
2. There is a unique cochain dp−1 α satisfyingδp (ω + dp−1 α) = 0 ; and
3. δp (ω + dp−1 α) = 0⇒ ∆p (ω + dp−1 α) = 0.
Theorem (H-Demlow)
Let [ω] ∈ Hp(Th;R) and let α ∈ Cp−1 be such that(ω + dp−1 α, dp−1 τ) = 0 for all τ ∈ Cp−1. ThenW(ω + dp−1 α) ∈ Hp
h.
If Whitney Hodge star is used, the method produces a solution tothe finite element exterior calculus equations for harmoniccochains.
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Isomorphism Theorem in Discrete Case
Theorem (Discrete Hodge-deRham Isomorphism, H-K-W-W)
Let [ω] ∈ Hp(K ;R). Then
1. There exists a cochain α ∈ Cp−1(K ;R), not necessarilyunique, such that δp (ω + dp−1 α) = 0;
2. There is a unique cochain dp−1 α satisfyingδp (ω + dp−1 α) = 0 ; and
3. δp (ω + dp−1 α) = 0⇒ ∆p (ω + dp−1 α) = 0.
Theorem (H-Demlow)
Let [ω] ∈ Hp(Th;R) and let α ∈ Cp−1 be such that(ω + dp−1 α, dp−1 τ) = 0 for all τ ∈ Cp−1. ThenW(ω + dp−1 α) ∈ Hp
h.
If Whitney Hodge star is used, the method produces a solution tothe finite element exterior calculus equations for harmoniccochains.
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Back to GraphsComparison of algebraic multigrid and conjugate gradient
I Results from experiments on graphs will be shown next
I 2-dimensional clique complexes for Erdos-Renyi randomgraphs and Barabasi-Albert scale-free graphs were used
I Np is number of p-simplices
I Conjugate gradient easily beats algebraic multigrid in theseexperiments
I Smoothed aggregation and Lloyd aggregation were used
I Entries for algebraic multigrid show setup time, solve time,and total time
I Pictures of system matrices hint at reason for poorperformance of multigrid
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Back to GraphsComparison of algebraic multigrid and conjugate gradient
I Results from experiments on graphs will be shown next
I 2-dimensional clique complexes for Erdos-Renyi randomgraphs and Barabasi-Albert scale-free graphs were used
I Np is number of p-simplices
I Conjugate gradient easily beats algebraic multigrid in theseexperiments
I Smoothed aggregation and Lloyd aggregation were used
I Entries for algebraic multigrid show setup time, solve time,and total time
I Pictures of system matrices hint at reason for poorperformance of multigrid
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Numerical experiments on Erdos-Renyi graphs
Case N0 N1 N2Edge Triangle
Density Densitya 100 1212 2359 2.45e-01 1.46e-02b 100 2530 21494 5.11e-01 1.33e-01c 100 3706 67865 7.49e-01 4.20e-01d 500 1290 21 1.03e-02 1.01e-06e 500 12394 20315 9.94e-02 9.81e-04
CaseAlgebraic Multigrid Conjugate Gradientα β α β
a0.0001 0.5708
0.0023 0.01910.0078 0.05510.0079 0.6259
b0.0030 0.866
0.0017 0.10330.0111 1.2360.0141 2.102
c0.0303 5.66
0.0015 0.47590.0386 11.080.0689 16.74
d0.1353 0.0071
0.0072 0.00070.5760 0.02890.7113 0.0360
e0.0001 0.49
0.0030 0.21550.3303 2.220.3305 2.71
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Numerical experiments on Barabasi-Albert graphs
Case N0 N1 N2Edge Triangle
Density Densitya 100 900 1701 1.82e-01 1.05e-02b 100 1600 8105 3.23e-01 5.01e-02c 100 2400 24497 4.85e-01 1.51e-01d 500 9600 25016 7.70e-02 1.21e-03e 1000 19600 37365 3.92e-02 2.25e-04
CaseAlgebraic Multigrid Conjugate Gradientα β α β
a0.0005 0.0242
0.0033 0.02810.0012 0.07530.0017 0.0995
b0.0001 0.1842
0.0032 0.09670.0008 0.17200.0009 0.3562
c0.0005 1.111
0.0030 0.24350.0012 1.5210.0017 2.632
d0.0002 1.018
0.0056 1.0430.0140 3.6080.0142 4.626
e0.0174 2.40
0.0088 2.5210.0219 8.210.0393 10.61
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Pictures of system matrices for graph experiments
ER ∆0
ER ∆2
BA ∆0
BA ∆2
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Meshes versus GraphsA mesh, its ∆0, and ∆0 for a graph with same number of vertices and same edge density
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Experiments with Random Clique ComplexesGraph Hodge decomposition code can be used to formulate conjectures
0.00 0.05 0.10 0.15 0.20 0.25
Edge Density0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
||h||
Erdos-Renyi graph with 250 nodes
0.00 0.05 0.10 0.15 0.20 0.25
Edge Density0
200
400
600
800
1000
1200
dim
ker
∆1
Erdos-Renyi graph with 250 nodes
Dashed lines are Kahle’s bounds [Kahle, Discrete Math., Vol. 309,pp. 1658–1671]. Betti number is almost always zero before thefirst bound and after third bound. It is almost always nonzerobetween first and second bounds. Theory is silent about the regionbetween second and third bounds and about harmonic norm.
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Optimal (Co)homologous (Co)chainsCommon threads
Cohomologous harmonic cochains:
minα∈Cp−1
(ω + dα , ω + dα)
minα‖h‖∗p subject to h = ω + dα (all vectors real)
Least squares ranking on graphs:
minα‖r‖2 subject to r = ω − ∂T1 α (all vectors real)
minβ‖s‖2 subject to s = ω − ∂2 β (all vectors real)
Optimal homologous chains:[See Dey-Hirani-Krishnamoorthy and Dunfield-Hirani]
minx ,y‖x‖1 subject to x = c + ∂ y (all vectors integer)
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Optimal (Co)homologous (Co)chainsCommon threads
Cohomologous harmonic cochains:
minα∈Cp−1
(ω + dα , ω + dα)
minα‖h‖∗p subject to h = ω + dα (all vectors real)
Least squares ranking on graphs:
minα‖r‖2 subject to r = ω − ∂T1 α (all vectors real)
minβ‖s‖2 subject to s = ω − ∂2 β (all vectors real)
Optimal homologous chains:[See Dey-Hirani-Krishnamoorthy and Dunfield-Hirani]
minx ,y‖x‖1 subject to x = c + ∂ y (all vectors integer)
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1. Hirani, A. N., Kalyanaraman, K., Wang, H., andWatts, S. Cohomologous harmonic cochains, 2011.arXiv:1012.2835
2. Hirani, A. N., Kalyanaraman, K., and Watts, S.Least squares ranking on graphs, 2011. arXiv:1011.1716
3. Dey, T. K., Hirani, A. N., and Krishnamoorthy, B.Optimal homologous cycles, total unimodularity, and linearprogramming. SIAM Journal on Computing 40, 4 (2011),1026–1044. doi:10.1137/100800245
4. Dunfield, N. M., and Hirani, A. N. The least spanningarea of a knot and the optimal bounding chain problem. InACM Symposium on Computational Geometry (SoCG 2011)(2011), pp. 135–144. doi:10.1145/1998196.1998218
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Acknowledgement
NSF Grant DMS-0645604Algebraic Topology and Exterior Calculus in Numerical Analysis
NSF Grant CCF 10-64429Optimality in Homology – Algorithms and Applications
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