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ABACUS/src/HEIS/ln_Overlap_XXX.cc

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/**********************************************************
This software is part of J.-S. Caux's ABACUS library.
Copyright (c) J.-S. Caux.
-----------------------------------------------------------
File: ln_Overlap_XXX.cc
Purpose: compute the overlap between an on-shell and an off-shell states
***********************************************************/
#include "ABACUS.h"
using namespace std;
using namespace ABACUS;
namespace ABACUS {
inline complex<DP> ln_Fn_F (XXX_Bethe_State& B, int k, int beta, int b)
{
complex<DP> ans = 0.0;
for (int j = 0; j < B.chain.Nstrings; ++j) {
for (int alpha = 0; alpha < B.base.Nrap[j]; ++alpha) {
for (int a = 1; a <= B.chain.Str_L[j]; ++a) {
if (!((j == k) && (alpha == beta) && (a == b)))
ans += log(B.lambda[j][alpha] - B.lambda[k][beta]
+ 0.5 * II * (B.chain.Str_L[j] - B.chain.Str_L[k] - 2.0 * (a - b)));
}
}
}
return(ans);
}
inline complex<DP> ln_Fn_G (XXX_Bethe_State& A, XXX_Bethe_State& B, int k, int beta, int b)
{
complex<DP> ans = 0.0;
for (int j = 0; j < A.chain.Nstrings; ++j) {
for (int alpha = 0; alpha < A.base.Nrap[j]; ++alpha) {
for (int a = 1; a <= A.chain.Str_L[j]; ++a) {
ans += log(A.lambda[j][alpha] - B.lambda[k][beta]
+ 0.5 * II * (A.chain.Str_L[j] - B.chain.Str_L[k] - 2.0 * (a - b)));
}
}
}
return(ans);
}
inline complex<DP> Fn_K (XXX_Bethe_State& A, int j, int alpha, int a, XXX_Bethe_State& B, int k, int beta, int b)
{
return(1.0/((A.lambda[j][alpha] - B.lambda[k][beta]
+ 0.5 * II * (A.chain.Str_L[j] - B.chain.Str_L[k] - 2.0 * (a - b)))
* (A.lambda[j][alpha] - B.lambda[k][beta]
+ 0.5 * II * (A.chain.Str_L[j] - B.chain.Str_L[k] - 2.0 * (a - b - 1.0)) )));
}
inline complex<DP> Fn_L (XXX_Bethe_State& A, int j, int alpha, int a, XXX_Bethe_State& B, int k, int beta, int b)
{
return ((2.0 * (A.lambda[j][alpha] - B.lambda[k][beta]
+ 0.5 * II * (A.chain.Str_L[j] - B.chain.Str_L[k] - 2.0 * (a - b - 0.5))
))
* pow(Fn_K (A, j, alpha, a, B, k, beta, b), 2.0));
}
complex<DP> ln_Overlap (XXX_Bethe_State& A, XXX_Bethe_State& B)
{
// This function returns the overlap of states A and B.
// The A and B states can contain strings.
// IMPORTANT ASSUMPTIONS:
// - State B is an eigenstate of the model on which the overlap measure is defined
// Check that A and B are compatible: same Mdown
if (A.base.Mdown != B.base.Mdown) return(complex<DP>(-300.0)); // overlap vanishes
// Some convenient arrays
Lambda re_ln_Fn_F_B_0(B.chain, B.base);
Lambda im_ln_Fn_F_B_0(B.chain, B.base);
Lambda re_ln_Fn_G_0(B.chain, B.base);
Lambda im_ln_Fn_G_0(B.chain, B.base);
Lambda re_ln_Fn_G_2(B.chain, B.base);
Lambda im_ln_Fn_G_2(B.chain, B.base);
complex<DP> ln_prod3 = 0.0;
complex<DP> ln_prod4 = 0.0;
// Define the F ones earlier...
for (int j = 0; j < B.chain.Nstrings; ++j) {
for (int alpha = 0; alpha < B.base.Nrap[j]; ++alpha) {
re_ln_Fn_F_B_0[j][alpha] = real(ln_Fn_F(B, j, alpha, 0));
im_ln_Fn_F_B_0[j][alpha] = imag(ln_Fn_F(B, j, alpha, 0));
re_ln_Fn_G_0[j][alpha] = real(ln_Fn_G(A, B, j, alpha, 0));
im_ln_Fn_G_0[j][alpha] = imag(ln_Fn_G(A, B, j, alpha, 0));
re_ln_Fn_G_2[j][alpha] = real(ln_Fn_G(A, B, j, alpha, 2));
im_ln_Fn_G_2[j][alpha] = imag(ln_Fn_G(A, B, j, alpha, 2));
}
}
// Define regularized products in prefactors
for (int j = 0; j < A.chain.Nstrings; ++j)
for (int alpha = 0; alpha < A.base.Nrap[j]; ++alpha)
for (int a = 1; a <= A.chain.Str_L[j]; ++a)
ln_prod3 += ln_Fn_F (A, j, alpha, a - 1);
// ln_prod3 -= A.base.Mdown * log(abs(sin(A.chain.zeta)));
for (int k = 0; k < B.chain.Nstrings; ++k)
for (int beta = 0; beta < B.base.Nrap[k]; ++beta)
for (int b = 1; b <= B.chain.Str_L[k]; ++b) {
if (b == 1) ln_prod4 += re_ln_Fn_F_B_0[k][beta];
else if (b > 1) ln_prod4 += ln_Fn_F(B, k, beta, b - 1);
}
// Now proceed to build the Hm2P matrix
SQMat_CX Hm2P(0.0, A.base.Mdown);
int index_a = 0;
int index_b = 0;
complex<DP> sum1 = 0.0;
complex<DP> prod_num = 0.0;
complex<DP> Fn_K_0_G_0 = 0.0;
complex<DP> Prod_powerN = 0.0;
complex<DP> Fn_K_1_G_2 = 0.0;
for (int j = 0; j < A.chain.Nstrings; ++j) {
for (int alpha = 0; alpha < A.base.Nrap[j]; ++alpha) {
for (int a = 1; a <= A.chain.Str_L[j]; ++a) {
index_b = 0;
for (int k = 0; k < B.chain.Nstrings; ++k) {
for (int beta = 0; beta < B.base.Nrap[k]; ++beta) {
for (int b = 1; b <= B.chain.Str_L[k]; ++b) {
if (B.chain.Str_L[k] == 1) {
// use simplified code for one-string here: original form of Hm2P matrix
Fn_K_0_G_0 = Fn_K (A, j, alpha, a, B, k, beta, 0) *
exp(re_ln_Fn_G_0[k][beta] + II * im_ln_Fn_G_0[k][beta] - re_ln_Fn_F_B_0[k][beta]);
Fn_K_1_G_2 = Fn_K (A, j, alpha, a, B, k, beta, 1) *
exp(re_ln_Fn_G_2[k][beta] + II * im_ln_Fn_G_2[k][beta] - re_ln_Fn_F_B_0[k][beta]);
Prod_powerN = pow((B.lambda[k][beta] + 0.5 * II)/(B.lambda[k][beta] - 0.5 * II),
complex<DP> (A.chain.Nsites)); // careful !
Hm2P[index_a][index_b] = Fn_K_0_G_0 - Prod_powerN * Fn_K_1_G_2
;
}
else {
if (b <= B.chain.Str_L[k] - 1) Hm2P[index_a][index_b] = Fn_K(A, j, alpha, a, B, k, beta, b);
else if (b == B.chain.Str_L[k]) {
Vect_CX ln_FunctionF(B.chain.Str_L[k] + 2);
for (int i = 0; i < B.chain.Str_L[k] + 2; ++i) ln_FunctionF[i] = ln_Fn_F (B, k, beta, i);
Vect_CX ln_FunctionG(B.chain.Str_L[k] + 2);
for (int i = 0; i < B.chain.Str_L[k] + 2; ++i) ln_FunctionG[i] = ln_Fn_G (A, B, k, beta, i);
sum1 = 0.0;
sum1 += Fn_K (A, j, alpha, a, B, k, beta, 0)
* exp(ln_FunctionG[0] + ln_FunctionG[1] - ln_FunctionF[0] - ln_FunctionF[1]);
sum1 += Fn_K (A, j, alpha, a, B, k, beta, B.chain.Str_L[k])
* exp(ln_FunctionG[B.chain.Str_L[k]] + ln_FunctionG[B.chain.Str_L[k] + 1]
- ln_FunctionF[B.chain.Str_L[k]] - ln_FunctionF[B.chain.Str_L[k] + 1]);
for (int jsum = 1; jsum < B.chain.Str_L[k]; ++jsum)
sum1 -= Fn_L (A, j, alpha, a, B, k, beta, jsum) *
exp(ln_FunctionG[jsum] + ln_FunctionG[jsum + 1] - ln_FunctionF[jsum] - ln_FunctionF[jsum + 1]);
prod_num = exp(II * im_ln_Fn_F_B_0[k][beta] + ln_FunctionF[1] - ln_FunctionG[B.chain.Str_L[k]]);
for (int jsum = 2; jsum <= B.chain.Str_L[k]; ++jsum)
prod_num *= exp(ln_FunctionG[jsum] - real(ln_Fn_F(B, k, beta, jsum - 1)));
// include all string contributions F_B_0 in this term
Hm2P[index_a][index_b] = prod_num * sum1;
} // else if (b == B.chain.Str_L[k])
} // else
index_b++;
}}} // sums over k, beta, b
index_a++;
}}} // sums over j, alpha, a
complex<DP> det = lndet_LU_CX_dstry(Hm2P);
complex<DP> ln_overlap = 0.5 * (-ln_prod3 + ln_prod4) + det - 0.5 * (A.lnnorm + B.lnnorm);
cout << "ln_overlap: " << endl << -ln_prod3 << "\t" << ln_prod4 << "\t" << 2.0 * det
<< "\t" << -A.lnnorm << "\t" << -B.lnnorm << endl;
return(ln_overlap);
}
} // namespace ABACUS
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