MPS dimensions (d2db65e1) · Commits · Alvarez, Gonzalo / merapp

src/Engine/DimensionSrep.h

+25 −1

Original line number	Diff line number	Diff line
		@@ -23,6 +23,10 @@ public:
		{
		dStoSymm(symmLocal_.qOne().size());
		alterFrees(symmLocal_.qOne().size());
		const bool mpsMode = (dsrep_(0).fullName()[0] == 'a');
		if (mpsMode)
		alterSummedMPS();
		else
		alterSummed();
		}

		@@ -73,6 +77,26 @@ private:
		dsrep_.refresh();
		}

		void alterSummedMPS()
		{
		for (SizeType i = 0; i < dsrep_.size(); ++i) {
		TensorStanzaType ts = dsrep_(i);
		if (ts.type() == TensorStanzaType::TENSOR_TYPE_ERASED)
		continue;

		const SizeType legs = ts.legs();

		for (SizeType j = 0; j < legs; ++j) {
		TensorStanzaType::IndexTypeEnum t = ts.legType(j);
		if (t != TensorStanzaType::INDEX_TYPE_SUMMED) continue;
		dsrep_.legTypeChar(i, j) = 'D';
		dsrep_.legTag(i, j) = m_;
		}
		}

		dsrep_.refresh();
		}

		void alterSummed()
		{
		for (SizeType i = 0; i < dsrep_.size(); ++i) {

src/Engine/MeraSolver.h

+13 −9

Original line number	Diff line number	Diff line
		@@ -64,7 +64,6 @@ public:
		isMeraPeriodic_(false),
		iterMera_(1),
		iterTensor_(1),
		indexOfRootTensor_(0),
		nameToIndexLut_(0),
		model_(paramsForMera_.model, paramsForMera_.hamiltonianConnection),
		paramsForLanczos_(0)
		@@ -110,6 +109,7 @@ public:

		nameToIndexLut_ = new NameToIndexLut<TensorType>(tensors_);
		bool rootTensorFound = false;
		bool mpsMode = false;
		while (true) {
		PsimagLite::String str("");
		try {
		@@ -149,9 +149,11 @@ public:
		continue;
		}

		if (name != "u" && name != "w" && name != "r")
		if (name != "u" && name != "w" && name != "r" && name != "a")
		continue;

		if (name == "a") mpsMode = true;

		SizeType id = atoi(tokens[1].c_str());
		tensorOptimizer_.push_back(new TensorOptimizerType(io,
		name,
		@@ -162,14 +164,12 @@ public:
		paramsForMera_,
		symmLocal_));

		if (name == "r") {
		if (rootTensorFound) {
		if (name == "r" \|\| name == "a") {
		if (rootTensorFound && !mpsMode) {
		PsimagLite::String msg("FATAL: File " + filename);
		throw PsimagLite::RuntimeError(msg + " more than one root found\n");
		}

		assert(tensorOptimizer_.size() > 0);
		indexOfRootTensor_ = tensorOptimizer_.size() - 1;
		rootTensorFound = true;
		}
		}
		@@ -233,13 +233,13 @@ private:
		SizeType id = tensorOptimizer_[i]->nameId().second;

		SizeType firstOfLayer = tensorOptimizer_[i]->firstOfLayer();
		if (optimizeOnlyFirstOfLayer && firstOfLayer != id && name != "r") {
		if (optimizeOnlyFirstOfLayer && firstOfLayer != id && !isRootTensor(name)) {
		tensorOptimizer_[i]->copyFirstOfLayer(name, firstOfLayer);
		continue;
		}

		if (!seenRoot) {
		if (name != "r")
		if (!isRootTensor(name))
		continue;
		else
		seenRoot = true;
		@@ -265,6 +265,11 @@ private:
		}
		}

		static bool isRootTensor(PsimagLite::String name)
		{
		return (name == "a" \|\| name == "r");
		}

		class ParallelEnergyHelper {

		public:
		@@ -447,7 +452,6 @@ private:
		bool isMeraPeriodic_;
		SizeType iterMera_;
		SizeType iterTensor_;
		SizeType indexOfRootTensor_;
		VectorTensorType tensors_;
		NameToIndexLut<TensorType>* nameToIndexLut_;
		ModelType model_;

src/Engine/TensorOptimizer.h

+9 −4

Original line number	Diff line number	Diff line
		@@ -142,7 +142,7 @@ public:
		for (SizeType iter = 0; iter < iters; ++iter) {

		RealType e = optimizeInternal(iter, upIter, evaluator);
		if (tensorToOptimize_.first == "r") {
		if (isRootTensor(tensorToOptimize_.first)) {
		std::cout<<"energy="<<e<<"\n";
		break;
		}
		@@ -269,7 +269,7 @@ private:
		MatrixType m = parallelEnvironHelper.matrix();
		MatrixType mSrc = m;
		VectorRealType s(m.n_row(),0);
		if (tensorToOptimize_.first == "r") { // diagonalize
		if (isRootTensor(tensorToOptimize_.first)) { // diagonalize
		std::cout<<"MATRIX_MAY_FOLLOW\n";
		if (!isHermitian(m,true)) {
		if (m.n_row() < 512) std::cout<<m;
		@@ -359,7 +359,7 @@ private:
		void topTensorFoldVector(MatrixType& t,
		const MatrixType& eigenvector) const
		{
		assert(tensorToOptimize_.first == "r");
		assert(isRootTensor(tensorToOptimize_.first));
		SizeType rows = t.n_row();
		SizeType cols = t.n_col();
		for (SizeType i = 0; i < rows; ++i)
		@@ -370,7 +370,7 @@ private:
		void lanczosDiag(MatrixType& t, VectorRealType& s, const MatrixType& src) const
		{
		SizeType n = src.n_row();
		assert(tensorToOptimize_.first == "r");
		assert(isRootTensor(tensorToOptimize_.first));
		SizeType rows = t.n_row();
		SizeType cols = t.n_col();
		assert(n == rows*cols);
		@@ -418,6 +418,11 @@ private:
		return sum;
		}

		static bool isRootTensor(PsimagLite::String name)
		{
		return (name == "a" \|\| name == "r");
		}

		TensorOptimizer(const TensorOptimizer&);

		TensorOptimizer& operator=(const TensorOptimizer&);