Skip to content

GitLab

Commit 4709054d authored by Dmitry I. Lyakh's avatar Dmitry I. Lyakh
Browse files

The initial guess preoptimization does not work properly, turned off for now. 


Signed-off-by: default avatarDmitry I. Lyakh <quant4me@gmail.com>
parent edeef6e7
Hide whitespace changes
Inline Side-by-side
src/exatn/optimizer.cpp
View file @ 4709054d
/** ExaTN:: Variational optimizer of a closed symmetric tensor network expansion functional
REVISION: 2021/11/17
REVISION: 2021/11/19
Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
......@@ -478,6 +478,14 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
{
assert(tensor_operator_ && vector_expansion_);
assert(guess_dim > 1);
unsigned int num_procs = 1;
if(parallel_) num_procs = process_group.getSize();
if(TensorNetworkOptimizer::focus >= 0){
if(getProcessRank() != TensorNetworkOptimizer::focus) TensorNetworkOptimizer::debug = 0;
}
bool success = true;
//Generate a random non-orthogonal tensor network basis:
auto tn_builder = exatn::getTensorNetworkBuilder("MPS");
......@@ -489,16 +497,21 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
std::vector<std::shared_ptr<TensorExpansion>> basis_bra(guess_dim);
success = exatn::sync(process_group); assert(success);
for(unsigned int i = 0; i < guess_dim; ++i){
basis_ket[i]->rename("_BasisVector"+std::to_string(i));
basis_ket[i] = makeSharedTensorExpansion("_BasisVector"+std::to_string(i));
auto basis_vector = makeSharedTensorNetwork("_VectorNet"+std::to_string(i),ket_tensor,*tn_builder,false);
success = basis_ket[i]->appendComponent(basis_vector,std::complex<double>{1.0,0.0}); assert(success);
basis_bra[i] = makeSharedTensorExpansion(*(basis_ket[i]));
basis_bra[i]->conjugate();
basis_bra[i]->rename("_ConjVectorNet"+std::to_string(i));
success = exatn::createTensors(process_group,*basis_vector,elem_type); assert(success);
success = exatn::initTensorsRnd(*basis_vector); assert(success);
}
success = exatn::sync(process_group); assert(success);
//Normalize the non-orthogonal tensor network basis:
for(unsigned int i = 0; i < guess_dim; ++i){
success = normalizeNorm2Sync(process_group,*(basis_ket[i]),1.0); assert(success);
basis_bra[i] = makeSharedTensorExpansion(*(basis_ket[i]));
basis_bra[i]->conjugate();
basis_bra[i]->rename("_ConjBasisVector"+std::to_string(i));
}
success = exatn::sync(process_group); assert(success);
//Build the operator matrix:
std::vector<std::complex<double>> oper_matrix(guess_dim*guess_dim);
std::vector<std::shared_ptr<Tensor>> oper_scalar(guess_dim*guess_dim);
......@@ -510,7 +523,7 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
oper_scalar[j*guess_dim + i]->rename("_OperScalarElem_"+std::to_string(i)+"_"+std::to_string(j));
success = exatn::createTensor(process_group,oper_scalar[j*guess_dim + i],elem_type); assert(success);
success = exatn::initTensor(oper_scalar[j*guess_dim + i]->getName(),0.0); assert(success);
success = exatn::evaluate(process_group,*(oper_elems[j*guess_dim + i]),oper_scalar[j*guess_dim + i]); assert(success);
success = exatn::evaluate(process_group,*(oper_elems[j*guess_dim + i]),oper_scalar[j*guess_dim + i],num_procs); assert(success);
}
}
//Build the metric matrix:
......@@ -524,7 +537,7 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
metr_scalar[j*guess_dim + i]->rename("_MetrScalarElem_"+std::to_string(i)+"_"+std::to_string(j));
success = exatn::createTensor(process_group,metr_scalar[j*guess_dim + i],elem_type); assert(success);
success = exatn::initTensor(metr_scalar[j*guess_dim + i]->getName(),0.0); assert(success);
success = exatn::evaluate(process_group,*(metr_elems[j*guess_dim + i]),metr_scalar[j*guess_dim + i]); assert(success);
success = exatn::evaluate(process_group,*(metr_elems[j*guess_dim + i]),metr_scalar[j*guess_dim + i],num_procs); assert(success);
}
}
success = exatn::sync(process_group); assert(success);
......@@ -561,19 +574,21 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
}
}
//Print matrices (debug):
std::cout << "#DEBUG(exatn::TensorNetworkOptimizer::getInitialGuess): Operator matrix:\n" << std::scientific;
for(unsigned int i = 0; i < guess_dim; ++i){
for(unsigned int j = 0; j < guess_dim; ++j){
std::cout << " " << oper_matrix[j*guess_dim + i];
if(TensorNetworkOptimizer::debug > 0){
std::cout << "#DEBUG(exatn::TensorNetworkOptimizer::computeInitialGuess): Operator matrix:\n" << std::scientific;
for(unsigned int i = 0; i < guess_dim; ++i){
for(unsigned int j = 0; j < guess_dim; ++j){
std::cout << " " << oper_matrix[j*guess_dim + i];
}
std::cout << std::endl;
}
std::cout << std::endl;
}
std::cout << "#DEBUG(exatn::TensorNetworkOptimizer::getInitialGuess): Metric matrix:\n" << std::scientific;
for(unsigned int i = 0; i < guess_dim; ++i){
for(unsigned int j = 0; j < guess_dim; ++j){
std::cout << " " << metr_matrix[j*guess_dim + i];
std::cout << "#DEBUG(exatn::TensorNetworkOptimizer::computeInitialGuess): Metric matrix:\n" << std::scientific;
for(unsigned int i = 0; i < guess_dim; ++i){
for(unsigned int j = 0; j < guess_dim; ++j){
std::cout << " " << metr_matrix[j*guess_dim + i];
}
std::cout << std::endl;
}
std::cout << std::endl;
}
//Solve the projected eigen-problem:
int info = 0;
......@@ -595,6 +610,14 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
(void*)work_space.data(),&lwork,
(void*)rwork_space.data(),&info);
if(info == 0){
//Print eigenvalues (debug):
if(TensorNetworkOptimizer::debug > 0){
std::cout << "#DEBUG(exatn::TensorNetworkOptimizer::computeInitialGuess): Eigenvalues:\n" << std::scientific;
for(unsigned int i = 0; i < guess_dim; ++i){
std::cout << alpha[i] << " / " << beta[i] << " = ";
if(std::abs(beta[i]) != 0.0) std::cout << (alpha[i]/beta[i]) << std::endl;
}
}
//Find the min/max eigenvalue:
int min_entry = 0, max_entry = 0;
for(int i = 0; i < matrix_dim; ++i){
......@@ -624,15 +647,18 @@ void TensorNetworkOptimizer::computeInitialGuess(const ProcessGroup & process_gr
success = root_expansion->appendComponent(net->network,coef*(net->coefficient)); assert(success);
}
}
success = normalizeNorm2Sync(process_group,*root_expansion,1.0); assert(success);
//Reconstruct the eigen-expansion as an initial guess:
vector_expansion_->conjugate();
TensorNetworkReconstructor::resetDebugLevel(1,0); //debug
TensorNetworkReconstructor reconstructor(root_expansion,vector_expansion_,DEFAULT_GUESS_TOLERANCE);
reconstructor.resetMaxIterations(10);
success = exatn::sync(process_group); assert(success);
double residual_norm, fidelity;
bool reconstructed = reconstructor.reconstruct(process_group,&residual_norm,&fidelity);
success = exatn::sync(process_group); assert(success);
vector_expansion_->conjugate();
//success = balanceNormalizeNorm2Sync(process_group,*vector_expansion_,1.0,1.0,true); assert(success); //debug
}
//Destroy temporaries:
for(unsigned int j = 0; j < guess_dim; ++j){
......
src/exatn/optimizer.hpp
View file @ 4709054d
/** ExaTN:: Variational optimizer of a closed symmetric tensor network expansion functional
REVISION: 2021/11/17
REVISION: 2021/11/19
Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
......@@ -39,10 +39,10 @@ public:
static constexpr const double DEFAULT_LEARN_RATE = 0.5;
static constexpr const unsigned int DEFAULT_MAX_ITERATIONS = 1000;
static constexpr const unsigned int DEFAULT_MICRO_ITERATIONS = 1;
static constexpr const bool PREOPTIMIZE_INITIAL_GUESS = true;
static constexpr const bool PREOPTIMIZE_INITIAL_GUESS = false;
static constexpr const unsigned int DEFAULT_KRYLOV_GUESS_DIM = 8;
static constexpr const unsigned int DEFAULT_GUESS_MAX_BOND_DIM = DEFAULT_KRYLOV_GUESS_DIM;
static constexpr const double DEFAULT_GUESS_TOLERANCE = 1e-2;
static constexpr const double DEFAULT_GUESS_TOLERANCE = 1e-3;
TensorNetworkOptimizer(std::shared_ptr<TensorOperator> tensor_operator, //in: hermitian tensor network operator
std::shared_ptr<TensorExpansion> vector_expansion, //inout: tensor network expansion forming the bra/ket vectors
......
src/exatn/reconstructor.cpp
View file @ 4709054d
/** ExaTN:: Reconstructs an approximate tensor network expansion for a given tensor network expansion
REVISION: 2021/10/22
REVISION: 2021/11/19
Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
......@@ -414,6 +414,7 @@ bool TensorNetworkReconstructor::reconstruct_sd(const ProcessGroup & process_gro
}
done = destroyTensorSync("_scalar_norm"); assert(done);
//Check the necessity to restart iterations:
if(iteration >= max_iterations_) break;
if(converged && fidelity_ < acceptable_fidelity && tolerance_ > 1e-6){
if(TensorNetworkReconstructor::debug > 0){
std::cout << "#DEBUG(exatn::TensorNetworkReconstructor): Insufficient fidelity, iterations will be restarted\n";
......
src/exatn/tests/NumServerTester.cpp
View file @ 4709054d
......@@ -3607,7 +3607,7 @@ TEST(NumServerTester, ExcitedMCVQE) {
const std::string tn_type = "TTN"; //MPS or TTN
const double accuracy = 1e-4;
//exatn::resetLoggingLevel(1,2); //debug
exatn::resetLoggingLevel(1,2); //debug
bool success = true;
bool root = (exatn::getProcessRank() == 0);
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment