Table of Contents
New
ResponseFunction.New(Table) creates a new response function object according to the values in Table. Response functions can be of 4 different types (ListOfPoles, Tri, And, or Nat) and single-valued or matrix-valued. Below 8 examples for creating each of these response functions by hand at some arbitrary values.
ListOfPoles representation
Response functions stored as list of poles are defined via $$ G(\omega,\Gamma) = A_0 + \sum_{i=1}^{n} \frac{B_{i-1}}{\omega + \mathrm{i}\Gamma/2 - a_i} $$
Single valued functions
- Example.Quanty
a = {10, -1,-0.5, 0, 0.5, 1, 1.5} b = { 0.1, 0.1, 0.1, 0.1, 0.2, 0.3} G = ResponseFunction.New( {a,b,mu=0,type="ListOfPoles", name="A"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ",G(omega,Gamma))
Generates the output
The resposne function definition is { { 10 , -1 , -0.5 , 0 , 0.5 , 1 , 1.5 } , { 0.1 , 0.1 , 0.1 , 0.1 , 0.2 , 0.3 } , name = A , type = ListOfPoles , mu = 0 } Evaluated at omega = 1.1 and Gamma = 0.001 yields (11.617645834991 - 0.011148328755289 I)
Matrix valued functions
- Example.Quanty
A0 = Matrix.New( {{0,0,0},{0,0,0},{0,0,0}} ) a1 = -1 a2 = 1/2 a3 = 1 B1s = Matrix.New( {{1,1,3},{1,5,6},{3,6,9}} ) B1 = B1s * B1s B2s = Matrix.New( {{2,0,3},{0,5,6},{3,6,9}} ) B2 = B2s * B2s B3s = Matrix.New( {{3,0,3},{0,5,6},{3,6,9}} ) B3 = B3s * B3s G = ResponseFunction.New( { {A0,a1,a2,a3}, {B1,B2,B3}, mu=0, type="ListOfPoles", name="ML"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
{ { { { 0 , 0 , 0 } , { 0 , 0 , 0 } , { 0 , 0 , 0 } } , -1 , 0.5 , 1 } , { { { 11 , 24 , 36 } , { 24 , 62 , 87 } , { 36 , 87 , 126 } } , { { 13 , 18 , 33 } , { 18 , 61 , 84 } , { 33 , 84 , 126 } } , { { 18 , 18 , 36 } , { 18 , 61 , 84 } , { 36 , 84 , 126 } } } , type = ListOfPoles , name = ML , mu = 0 } Evaluated at omega = 1.1 and Gamma = 0.001 yields { { (206.90024667403 - 0.91928020904165 I) , (221.42405005987 - 0.9276985714825 I) , (432.13381820162 - 1.8498699350513 I) } , { (221.42405005987 - 0.9276985714825 I) , (741.17515429623 - 3.1416753933531 I) , (1021.4074723828 - 4.3264255332922 I) } , { (432.13381820162 - 1.8498699350513 I) , (1021.4074723828 - 4.3264255332922 I) , (1529.9683515529 - 6.4891280958856 I) } }
Tridiagonal representation
Response functions stored in tridiagonal format are defined via $$ G(\omega,\Gamma) = A_0 + B_0^* \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_1 - B_{1}^{\phantom{\dagger}} \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_2 - B_{2}^{\phantom{\dagger}} \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_2 - B_{3}^{\phantom{\dagger}} \frac{...}{\omega + \mathrm{i}\Gamma/2 - A_{n-1} - B_{n-1}^{\phantom{\dagger}} \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_n } B_{n-1}^{\dagger}} B_{3}^{\dagger} } B_{2}^{\dagger} } B_{1}^{\dagger} } B_0^T $$
Single valued functions
- Example.Quanty
a = {0, 1, 1, 1, 1, 1, 1} b = { 1, 0.5, 0.5, 0.5, 0.5, 0.5} G = ResponseFunction.New( {a,b,mu=0,type="Tri", name="GT"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
{ { 0 , 1 , 1 , 1 , 1 , 1 , 1 } , { 1 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 } , name = GT , type = Tri , mu = 0 } Evaluated at omega = 1.1 and Gamma = 0.001 yields (-1.4800882525182 - 0.010904814637879 I)
Matrix valued functions
- Example.Quanty
A0 = Matrix.New( {{0,0,0},{0,0,0},{0,0,0}} ) A1 = Matrix.New( {{1,2,3},{2,5,6},{3,6,9}} ) A2 = Matrix.New( {{2,2,3},{2,5,6},{3,6,9}} ) A3 = Matrix.New( {{3,2,3},{2,5,6},{3,6,9}} ) B0s = Matrix.New( {{1,0,0},{0,1,0},{0,0,1}} ) B0 = B0s * B0s B1s = Matrix.New( {{1,1,3},{1,5,6},{3,6,9}} ) B1 = B1s * B1s B2s = Matrix.New( {{2,0,3},{0,5,6},{3,6,9}} ) B2 = B2s * B2s B3s = Matrix.New( {{3,0,3},{0,5,6},{3,6,9}} ) B3 = B3s * B3s G = ResponseFunction.New( { {A0,A1,A2,A3}, {B0,B1,B2}, mu=0, type="Tri", name="MT"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
{ { { { 0 , 0 , 0 } , { 0 , 0 , 0 } , { 0 , 0 , 0 } } , { { 1 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } , { { 2 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } , { { 3 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } } , { { { 1 , 0 , 0 } , { 0 , 1 , 0 } , { 0 , 0 , 1 } } , { { 11 , 24 , 36 } , { 24 , 62 , 87 } , { 36 , 87 , 126 } } , { { 13 , 18 , 33 } , { 18 , 61 , 84 } , { 33 , 84 , 126 } } } , type = Tri , mu = 0 , BlockSize = { 3 , 3 , 3 , 3 } , name = MT } Evaluated at omega = 1.1 and Gamma = 0.001 yields { { (0.82041346528466 - 0.0005287731551253 I) , (-0.10773626514221 + 0.00047135055668369 I) , (-0.18782055050369 - 0.00021908813997981 I) } , { (-0.10773626514222 + 0.00047135055668371 I) , (0.9906660606812 - 0.0018715007457328 I) , (-0.67958409204703 + 0.0013049040629232 I) } , { (-0.18782055050369 - 0.00021908813997982 I) , (-0.67958409204703 + 0.0013049040629232 I) , (0.51775011277794 - 0.00095267059250443 I) } }
Anderson representation
Response functions stored in Anderson format are defined via $$ G(\omega,\Gamma) = A_0 + B_0^* \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_1 - \sum_{i=2}^{n} B_{i-1}^{\phantom{\dagger}} \frac{1}{\omega + \mathrm{i}\Gamma/2 - A_{i} } B_{i-1}^{\dagger} } B_0^T $$
Single valued functions
- Example.Quanty
a = {0, 1, 1.5, 2, 2.5, 3, 3.5} b = { 1, 0.5, 0.5, 0.5, 0.5, 0.5} G = ResponseFunction.New( {a,b,mu=0,type="And", name="GA"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
The resposne function definition is { { 0 , 1 , 1.5 , 2 , 2.5 , 3 , 3.5 } , { 1 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 } , type = And , name = GA , mu = 0 } Evaluated at omega = 1.1 and Gamma = 0.001 yields (0.70566877797716 - 0.00077467678957667 I)
Matrix valued functions
- Example.Quanty
A0 = Matrix.New( {{0,0,0},{0,0,0},{0,0,0}} ) A1 = Matrix.New( {{1,2,3},{2,5,6},{3,6,9}} ) A2 = Matrix.New( {{2,2,3},{2,5,6},{3,6,9}} ) A3 = Matrix.New( {{3,2,3},{2,5,6},{3,6,9}} ) B0s = Matrix.New( {{1,0,0},{0,1,0},{0,0,1}} ) B0 = B0s * B0s B1s = Matrix.New( {{1,1,3},{1,5,6},{3,6,9}} ) B1 = B1s * B1s B2s = Matrix.New( {{2,0,3},{0,5,6},{3,6,9}} ) B2 = B2s * B2s B3s = Matrix.New( {{3,0,3},{0,5,6},{3,6,9}} ) B3 = B3s * B3s G = ResponseFunction.New( { {A0,A1,A2,A3}, {B0,B1,B2}, mu=0, type="And", name="MA"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
{ { { { 0 , 0 , 0 } , { 0 , 0 , 0 } , { 0 , 0 , 0 } } , { { 1 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } , { { 2 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } , { { 3 , 2 , 3 } , { 2 , 5 , 6 } , { 3 , 6 , 9 } } } , { { { 1 , 0 , 0 } , { 0 , 1 , 0 } , { 0 , 0 , 1 } } , { { 11 , 24 , 36 } , { 24 , 62 , 87 } , { 36 , 87 , 126 } } , { { 13 , 18 , 33 } , { 18 , 61 , 84 } , { 33 , 84 , 126 } } } , mu = 0 , name = MA , type = And } Evaluated at omega = 1.1 and Gamma = 0.001 yields { { (0.079202023515427 - 0.00018271816123949 I) , (0.019672301598063 - 0.00021050428128743 I) , (-0.033329362936266 + 0.00019529208541705 I) } , { (0.019672301598062 - 0.00021050428128743 I) , (-0.028178571653589 - 0.00029307470840254 I) , (0.014801870346139 + 0.00026254621032145 I) } , { (-0.033329362936266 + 0.00019529208541705 I) , (0.014801870346139 + 0.00026254621032145 I) , (-0.0017775335507766 - 0.00023672956892774 I) } }
Natural impurity orbital representation
Response functions stored in Natural impurity format are defined via $$ G(\omega,\Gamma) = A_0 + B_0^* \left( G_{val}(\omega,\Gamma) + G_{con}(\omega,\Gamma) \right) B_0^T$$, with $G_{val}(\omega,\Gamma)$ and $G_{con}(\omega,\Gamma)$ as response functions with poles either at positive energy ($G_{con}(\omega,\Gamma)$) or poles at negative energy ($G_{val}(\omega,\Gamma)$).
Single valued functions
- Example.Quanty
acon = {0, 1, 1, 1, 1, 1, 1} bcon = { sqrt(1/2), 0.5, 0.5, 0.5, 0.5, 0.5} Gcon = ResponseFunction.New( {acon,bcon,mu=0,type="Tri"} ) aval = {0, -1, -1, -2, -1, -1, -1} bval = { sqrt(1/2), 0.5, 0.5, 0.5, 0.5, 0.5} Gval = ResponseFunction.New( {aval,bval,mu=0,type="Tri"} ) a0=0 b0=1 G = ResponseFunction.New( {{a0,b0},val=Gval,con=Gcon,mu=0,type="Nat", name="GD"} ) print("The resposne function definition is") print(G) omega = 1.1 Gamma = 0.001 print() print("Evaluated at omega =",omega," and Gamma =",Gamma," yields ") print(G(omega,Gamma))
Generates the output
The resposne function definition is { { 0 , 1 } , type = NaturalImpurityOrbital , name = GD , mu = 0 , con = { { 0 , 1 , 1 , 1 , 1 , 1 , 1 } , { 0.70710678118655 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 } , mu = 0 , type = Tri , name = Matrix } , epsilon = 0 , val = { { 0 , -1 , -1 , -2 , -1 , -1 , -1 } , { 0.70710678118655 , 0.5 , 0.5 , 0.5 , 0.5 , 0.5 } , mu = 0 , type = Tri , name = Matrix } } Evaluated at omega = 1.1 and Gamma = 0.001 yields (-0.48700605787262 - 0.0055204934115643 I)