Tensors And Matrices

2y ago
21 Views
2 Downloads
1.10 MB
149 Pages
Last View : 20d ago
Last Download : 3m ago
Upload by : Madison Stoltz
Transcription

Tensors and MatricesShmuel FriedlandUniv. Illinois at ChicagoWest Canada Linear Algebra Meeting, May 7-9, 2010Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensorsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplicationShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensorsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.2Perron-Frobenius theoremShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.2Perron-Frobenius theorem3Rank (R1 , R2 , R3 ) approximationsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.2Perron-Frobenius theorem3Rank (R1 , R2 , R3 ) approximations4CUR approximationsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.2Perron-Frobenius theorem3Rank (R1 , R2 , R3 ) approximations4CUR approximationsDiagonal scaling of nonnegative tensors to tensors with given rows,columns and depth sumsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

OverviewRanks of 3-tensors1Basic facts.2Complexity.3Matrix multiplication4Results and conjecturesApproximations of tensors1Rank one approximation.2Perron-Frobenius theorem3Rank (R1 , R2 , R3 ) approximations4CUR approximationsDiagonal scaling of nonnegative tensors to tensors with given rows,columns and depth sumsCharacterization of tensor in C4 4 4 of border rank 4Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Tensor τ U1 U2 U3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Tensor τ U1 U2 U3HISTORY: Tensors-as now W. Voigt 1898Tensor calculus 1890 G. Ricci-Curbastro: absolute differential calculus,T. Levi-Civita: 1900, A. Einstein: General relativity 1915Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Tensor τ U1 U2 U3HISTORY: Tensors-as now W. Voigt 1898Tensor calculus 1890 G. Ricci-Curbastro: absolute differential calculus,T. Levi-Civita: 1900, A. Einstein: General relativity 1915Rank one tensor ti,j,k xi yj zk , (i, j, k ) (1, 1, 1), . . . , (m1 , m2 , m3 )or decomposable tensor x y zShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Tensor τ U1 U2 U3HISTORY: Tensors-as now W. Voigt 1898Tensor calculus 1890 G. Ricci-Curbastro: absolute differential calculus,T. Levi-Civita: 1900, A. Einstein: General relativity 1915Rank one tensor ti,j,k xi yj zk , (i, j, k ) (1, 1, 1), . . . , (m1 , m2 , m3 )or decomposable tensor x y zbasis of Uj :basis of U:[u1,j , . . . , umj ,j ] j 1, 2, 3ui1 ,1 ui2 ,2 ui3 ,3 , ij 1, . . . , mj , j 1, 2, 3,Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic notionsscalar a F, vector x (x1 , . . . , xn ) Fn , matrix A [aij ] Fm n ,3-tensor T [ti,j,k ] Fm n l , p-tensor T [ti1 ,.,ip ] Fn1 . npAbstractly U : U1 U2 U3 dim Ui mi , i 1, 2, 3, dim U m1 m2 m3Tensor τ U1 U2 U3HISTORY: Tensors-as now W. Voigt 1898Tensor calculus 1890 G. Ricci-Curbastro: absolute differential calculus,T. Levi-Civita: 1900, A. Einstein: General relativity 1915Rank one tensor ti,j,k xi yj zk , (i, j, k ) (1, 1, 1), . . . , (m1 , m2 , m3 )or decomposable tensor x y zbasis of Uj : [u1,j , . . . , umj ,j ] j 1, 2, 3basisPof U: ui1 ,1 ui2 ,2 ui3 ,3 , ij 1, . . . , mj , j 1, 2, 3,1 ,m2 ,m3τ mi1 i2 i3 1 ti1 ,i2 ,i2 ui1 ,1 ui2 ,2 ui3 ,3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FPm1T i 1 Ti,1 ei,1 (convenient notation)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FPm1T i 1 Ti,1 ei,1 (convenient notation)R1 : dim span(T1,1 , . . . , Tm1 ,1 ).Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FPm1T i 1 Ti,1 ei,1 (convenient notation)R1 : dim span(T1,1 , . . . , Tm1 ,1 ).Similarly, unfolding in directions 2, 3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FPm1T i 1 Ti,1 ei,1 (convenient notation)R1 : dim span(T1,1 , . . . , Tm1 ,1 ).Similarly, unfolding in directions 2, 3rank T minimal r :T fr (x1 , y1 , z1 , . . . , xr , yr , zr ) : Shmuel Friedland Univ. Illinois at Chicago ()Pri 1 xiTensors and Matrices yi zi ,West Canada Linear Algebra Meeting, May 7-9/ 24

Ranks of tensorsUnfolding tensor: in direction 1:T [ti,j,k ] view as a matrix A1 [ti,(j,k ) ] Fm1 (m2 ·m3 )R1 : rank A1 :dimension of row or column subspace spanned in direction 1m2 ,m3m2 m3 , i 1, . . . , mTi,1 : [ti,j,k ]j,k1 1 FPm1T i 1 Ti,1 ei,1 (convenient notation)R1 : dim span(T1,1 , . . . , Tm1 ,1 ).Similarly, unfolding in directions 2, 3rank T minimal r :T fr (x1 , y1 , z1 , . . . , xr , yr , zr ) : (CANDEC, PARFAC)Shmuel Friedland Univ. Illinois at Chicago ()Pri 1 xiTensors and Matrices yi zi ,West Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsFACT I: rank T max(R1 , R2 , R3 )Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsFACT I: rank T max(R1 , R2 , R3 )Reason U2 U3 Fm2 m3 Fm2 m3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsFACT I: rank T max(R1 , R2 , R3 )Reason U2 U3 Fm2 m3 Fm2 m3Note:R1 , R2 , R3 are easily computableIt is possible that R1 6 R2 6 R3Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsFACT I: rank T max(R1 , R2 , R3 )Reason U2 U3 Fm2 m3 Fm2 m3Note:R1 , R2 , R3 are easily computableIt is possible that R1 6 R2 6 R3FACT II : For τ T [ti,j,k ] letm1 m2 , k 1, . . . , m . Then rank T 1 ,m2Tk ,3 : [ti,j,k ]m3i,j 1 Fminimal dimension of subspace L Fm1 m2 spanned by rank onematrices containing T1,3 , . . . , Tm3 ,3 .Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Basic factsFACT I: rank T max(R1 , R2 , R3 )Reason U2 U3 Fm2 m3 Fm2 m3Note:R1 , R2 , R3 are easily computableIt is possible that R1 6 R2 6 R3FACT II : For τ T [ti,j,k ] letm1 m2 , k 1, . . . , m . Then rank T 1 ,m2Tk ,3 : [ti,j,k ]m3i,j 1 Fminimal dimension of subspace L Fm1 m2 spanned by rank onematrices containing T1,3 , . . . , Tm3 ,3 .COR rank T min(mn, ml, nl)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Complexity of rank of 3-tensorShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Complexity of rank of 3-tensorHastad 1990: Tensor rank is NP-complete for any finite field andNP-hard for rational numbersShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Complexity of rank of 3-tensorHastad 1990: Tensor rank is NP-complete for any finite field andNP-hard for rational numbersPRF: 3-sat with n variables m clausessatisfiable iff rank T 4n 2m, T F(2n 3m) (3n) (3n m) )otherwise rank is largerShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank: F R, CShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank: F R, CRr (m, n, l) Fm n l : all tensors of rank rShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank: F R, CRr (m, n, l) Fm n l : all tensors of rank rRr (m, n, l) not closed variety for r 2Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank: F R, CRr (m, n, l) Fm n l : all tensors of rank rRr (m, n, l) not closed variety for r 2Border rank of T the minimum k s.t. T is a limit of Tj , j N, rank Tj k .Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank: F R, CRr (m, n, l) Fm n l : all tensors of rank rRr (m, n, l) not closed variety for r 2Border rank of T the minimum k s.t. T is a limit of Tj , j N, rank Tj k .generic rank border rank typical rank of Fm n l : grankF (m, n, l) the rank of a random tensor T Fm n lShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lDimension count for F C and 2 m n l (m 1)(n 1) 1:Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lDimension count for F C and 2 m n l (m 1)(n 1) 1:fr : (Cm Cn Cl )r Cm n l , x y z (ax) (by) ((ab) 1 z)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lDimension count for F C and 2 m n l (m 1)(n 1) 1:fr : (Cm Cn Cl )r Cm n l , x y z (ax) (by) ((ab) 1 z)mnlgrankC (m, n, l)(m n l 2) mnl grankC (m, n, l) d (m n l 2)eShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lDimension count for F C and 2 m n l (m 1)(n 1) 1:fr : (Cm Cn Cl )r Cm n l , x y z (ax) (by) ((ab) 1 z)mnlgrankC (m, n, l)(m n l 2) mnl grankC (m, n, l) d (m n l 2)emnlConjecture grankC (m, n, l) d (m n l 2)efor 2 m n l (m 1)(n 1) and (3, n, l) 6 (3, 2p 1, 2p 1)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank of Fm n lTHM: grankC (m, n, l) min(l, mn) for (m 1)(n 1) 1 l.Reason: For l (m 1)(n 1) 1 a generic subspace of matrices ofdimension l in Cm n intersect the variety of rank one matrices in Cm nat least at l lines which contain l linearly independent matricesCOR: grankC (2, n, l) min(l, 2n) for 2 n lDimension count for F C and 2 m n l (m 1)(n 1) 1:fr : (Cm Cn Cl )r Cm n l , x y z (ax) (by) ((ab) 1 z)mnlgrankC (m, n, l)(m n l 2) mnl grankC (m, n, l) d (m n l 2)emnlConjecture grankC (m, n, l) d (m n l 2)efor 2 m n l (m 1)(n 1) and (3, n, l) 6 (3, 2p 1, 2p 1)2Fact: grankC (3, 2p 1, 2p 1) d 3(2p 1)4p 3 e 1Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V WShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, WShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) Pk 1 ti,j,k wk ,Shmuel Friedland Univ. Illinois at Chicago ()T : [ti,j,k ] Fm n lTensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PrPa 1 xak 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank TShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PPra 1 xaφ(c, d) Prk 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank Ta 1 (c x)(d y)z ,aShmuel Friedland Univ. Illinois at Chicago ()c Pmi 1 ci ui , dTensors and Matrices Pnj 1 dj vjWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PrPa 1 xak 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank TPPPnφ(c, d) ra 1 (c x)(d y)za , c mi 1 ci ui , d j 1 dj vjComplexity: r -productsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PrPa 1 xak 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank TPPPnφ(c, d) ra 1 (c x)(d y)za , c mi 1 ci ui , d j 1 dj vjComplexity: r -productsMatrix product τ : FM N FN L FM L , (A, B) 7 ABShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PrPa 1 xak 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank TPPPnφ(c, d) ra 1 (c x)(d y)za , c mi 1 ci ui , d j 1 dj vjComplexity: r -productsMatrix product τ : FM N FN L FM L , (A, B) 7 AB4 4 4M N L 2, grankC (4, 4, 4) d 4 4 4 2e d6.4e 7Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Bilinear maps and product of matricesbilinear map: φ : U V W[u1 , . . . , um ], [v1 , . . . , vn ], [w1 , . . . , wl ] bases in U, V, Wφ(ui , vj ) T PrPa 1 xak 1 ti,j,k wk ,T : [ti,j,k ] Fm n l ya za , r rank TPPPnφ(c, d) ra 1 (c x)(d y)za , c mi 1 ci ui , d j 1 dj vjComplexity: r -productsMatrix product τ : FM N FN L FM L , (A, B) 7 AB4 4 4M N L 2, grankC (4, 4, 4) d 4 4 4 2e d6.4e 7Product of two 2 2 matrices is done by 7 multiplicationsShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjectureShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 4Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerEasy to compute grankC (m, n, l):Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerEasy to compute grankC (m, n, l):Pick at random wr : (x1 , y1 , z1 , . . . , xr , yr , zr ) (Rm Rn Rl )rShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerEasy to compute grankC (m, n, l):Pick at random wr : (x1 , y1 , z1 , . . . , xr , yr , zr ) (Rm Rn Rl )rmnle s.t. rank J(fr )(wr ) mnlThe minimal r d (m n l 2)is grankC (m, n, l) (Terracini Lemma 1915)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerEasy to compute grankC (m, n, l):Pick at random wr : (x1 , y1 , z1 , . . . , xr , yr , zr ) (Rm Rn Rl )rmnle s.t. rank J(fr )(wr ) mnlThe minimal r d (m n l 2)is grankC (m, n, l) (Terracini Lemma 1915)Avoid round-off error:wr (Zm Zn Zl )r find rank J(fr )(wr ) exact arithmeticShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Known cases of rank conjecture2212pgrank(3, 2p, 2p) d 4p 1e and grank(3, 2p 1, 2p 1) d 3(2p 1)4p 1 e 1(n, n, n 2) if n 6 2 (mod 3),(n 1, n, n) if n 0 (mod 3),(4, m, m) if m 4,(n, n, n) if n 42lp(l, 2p, 2q) if l 2p 2q and and l 2p 2q 2is integerEasy to compute grankC (m, n, l):Pick at random wr : (x1 , y1 , z1 , . . . , xr , yr , zr ) (Rm Rn Rl )rmnle s.t. rank J(fr )(wr ) mnlThe minimal r d (m n l 2)is grankC (m, n, l) (Terracini Lemma 1915)Avoid round-off error:wr (Zm Zn Zl )r find rank J(fr )(wr ) exact arithmeticI checked the conjecture up to m, n, l 14Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.c(m,n,l)Closure( i 1) Rm n lShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.c(m,n,l)Closure( i 1) Rm n lrank T grankC (m, n, l) for each T V1Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.c(m,n,l)Closure( i 1) Rm n lrank T grankC (m, n, l) for each T V1rank T ρi for each T ViShmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.c(m,n,l)Closure( i 1) Rm n lrank T grankC (m, n, l) for each T V1rank T ρi for each T Viρi grankC (m, n, l) for i 2, . . . , c(m, n, l)Shmuel Friedland Univ. Illinois at Chicago ()Tensors and MatricesWest Canada Linear Algebra Meeting, May 7-9/ 24

Generic rank III - the real caseFor mn l grankR (m, n, l) mn.For 2 m n l mn 1, there exist V1 , . . . , Vc(m,n,l) Rm n lpairwise distinct open connected semi-algebraic sets s.t.c(m,n,l)Closure( i 1) Rm n lrank T grankC (m, n, l) for each T

Overview Ranks of3-tensors 1 Basic facts. 2 Complexity. 3 Matrix multiplication 4 Results and conjectures Approximations of tensors 1 Rank one approximation. 2 Perron-Frobenius theorem 3 Rank (R1;R2;R3) approximations 4 CUR approximations Diagonal scaling of nonnegative tensors to tensors with given rows, columns and depth sums

Related Documents:

22 Dense matrices over the Real Double Field using NumPy435 23 Dense matrices over GF(2) using the M4RI library437 24 Dense matrices over F 2 for 2 16 using the M4RIE library447 25 Dense matrices over Z/ Z for 223 using LinBox’s Modular double 455 26 Dense matrices over Z/ Z for 211 using LinBox’s Modular&l

Introduction to vectors and tensors Instructor: Prof. Marcial Gonzalez Spring, 2015 ME 612 -Continuum Mechanics. Lecture 4 -Introduction to tensors and vectors . (vector) 2-order tensor Symmetric, positive-definite 2-order tensor , 4 Tensor analysis Tensor fields-In continuum mechanics we encounter tensors as spatially and .

formerly tensors and tensor fields (mappings whose values are tensors) were not distinguished, and tensor fields were discussed without defining tensors in advance. ( ) In fact, readers should be aware that sometimes tensor fields are simply called tensors in the literature. In any case, it is important

of freedom involve spectral analysis of matrices. The discrete Fourier transform, including the fast Fourier transform, makes use of Toeplitz matrices. Statistics is widely based on correlation matrices. The generalized inverse is involved in least-squares approximation. Symmetric matrices are inertia, deformation, or viscous tensors in

Class XII – NCERT – Maths Chapter 3 - Matrices 3.Matrices . Exercise 3.1 . Question 1: In the matrix 2 5 19 7 5 35 2 12 2 3 1 5 17. A . As the given matrices are equal, their corresponding elements are also equal. Class XII – NCERT – Maths . Chapter 3 - Matrices . 3.Matrices . Comparing the corresponding elements, we get: .

Introduction Problem Vectors Tensors Matrices Conclusion Context TowardslinearalgebrasupportinC P0009: mdspan: ANon-OwningMultidimensionalArrayReference

Matrices and Determinants (i) Matrices Concept, notation, order, equality, types of matrices, zero and identity matrix, transpose of a matrix, symmetric and skew symmetric matrices. Operation on matrices: Addition and multiplication and multiplication with a . Further Reduced ISC Class 12 Maths Syllabus 2020-21

Artificial intelligence (AI) is a significant step forward in the digitalisation and transformation of modern businesses. In short, it refers to computers’ capability to acquire and apply knowledge without programmers’ intervention. Investors are lining up to be part of the imminent change. AI attracted USD 24 bn in investments globally in 2018, a twelvefold increase since 2013. US start .