xerus issueshttps://git.hemio.de/xerus/xerus/-/issues2020-04-21T08:19:54Zhttps://git.hemio.de/xerus/xerus/-/issues/262Slice TTTensor2020-04-21T08:19:54ZNando FarchminSlice TTTensorWould be good to get a subtensor, i.e. slice of the tensor (a subtensor from dimensions (l_1,...,l_n) to dimensions (u_1,...,u_n) where 0 <= l_i <= u_i <= tensor_dim_i, see code below.)
```
def slice_tt(tt, lower, upper):
"""
Slice TTTensor in each component from lower to (not including) upper dimension.
"""
assert len(lower) == len(upper) == len(tt.dimensions)
assert np.all(np.array(lower) < np.array(upper))
assert np.all(np.array(upper) <= np.array(tt.dimensions))
diff = [u-l for l,u in zip(lower,upper)]
# TODO there should be a more elegant way to slice TTTensors!
tmp = xe.TTTensor(tt)
for pos in range(tt.order()):
tmp.move_core(pos)
cmp = np.asarray(tmp.get_component(pos))
cmp[:, :lower[pos], :] = 0
cmp[:, upper[pos]:, :] = 0
tmp.set_component(pos, xe.Tensor.from_buffer(cmp))
tmp.move_core(0)
tt_slice = xe.TTTensor.random(diff, tmp.ranks())
for pos in range(tt.order()):
cmp = np.asarray(tmp.get_component(pos))
cmp = np.array(cmp[:, lower[pos]:upper[pos], :])
tt_slice.set_component(pos, xe.Tensor.from_buffer(cmp))
return tt_slice
```Would be good to get a subtensor, i.e. slice of the tensor (a subtensor from dimensions (l_1,...,l_n) to dimensions (u_1,...,u_n) where 0 <= l_i <= u_i <= tensor_dim_i, see code below.)
```
def slice_tt(tt, lower, upper):
"""
Slice TTTensor in each component from lower to (not including) upper dimension.
"""
assert len(lower) == len(upper) == len(tt.dimensions)
assert np.all(np.array(lower) < np.array(upper))
assert np.all(np.array(upper) <= np.array(tt.dimensions))
diff = [u-l for l,u in zip(lower,upper)]
# TODO there should be a more elegant way to slice TTTensors!
tmp = xe.TTTensor(tt)
for pos in range(tt.order()):
tmp.move_core(pos)
cmp = np.asarray(tmp.get_component(pos))
cmp[:, :lower[pos], :] = 0
cmp[:, upper[pos]:, :] = 0
tmp.set_component(pos, xe.Tensor.from_buffer(cmp))
tmp.move_core(0)
tt_slice = xe.TTTensor.random(diff, tmp.ranks())
for pos in range(tt.order()):
cmp = np.asarray(tmp.get_component(pos))
cmp = np.array(cmp[:, lower[pos]:upper[pos], :])
tt_slice.set_component(pos, xe.Tensor.from_buffer(cmp))
return tt_slice
```Version 4.1Philipp TrunschkePhilipp Trunschkehttps://git.hemio.de/xerus/xerus/-/issues/258test pickling of random tensor networks2019-06-16T20:32:46ZPhilipp Trunschketest pickling of random tensor networkshttps://git.hemio.de/xerus/xerus/-/issues/216Add convenience functions for modifying the TensorNetwork graph.2019-02-15T10:43:30ZPhilipp TrunschkeAdd convenience functions for modifying the TensorNetwork graph.There are some operations on `TensorNetworks` that are not as simple as they should be.
As an example I think that a `TensorNetworks` should have the functions
- `TensorNetwork::remove_node(const size_t _nodeId)` : removes the node from the TensorNetwork and inserts the newly created external links at the end of `externalLinks` (in the order they had on the node tensor)
- `TensorNetwork::remove_link(const size_t _nodeId1, const size_t _nodeId2)` : removes the link between the given nodes and inserts the newly created external links at the end of `externalLinks`
The first function is useful when computing the gradient of a `TensorNetwork` w.r.t. the given node.
The second one will probably be used mainly as a subroutine of the first one.
Related tasks like `add_node` can be done using Einstein notation. Maybe we can find something similar for these tasks.There are some operations on `TensorNetworks` that are not as simple as they should be.
As an example I think that a `TensorNetworks` should have the functions
- `TensorNetwork::remove_node(const size_t _nodeId)` : removes the node from the TensorNetwork and inserts the newly created external links at the end of `externalLinks` (in the order they had on the node tensor)
- `TensorNetwork::remove_link(const size_t _nodeId1, const size_t _nodeId2)` : removes the link between the given nodes and inserts the newly created external links at the end of `externalLinks`
The first function is useful when computing the gradient of a `TensorNetwork` w.r.t. the given node.
The second one will probably be used mainly as a subroutine of the first one.
Related tasks like `add_node` can be done using Einstein notation. Maybe we can find something similar for these tasks.https://git.hemio.de/xerus/xerus/-/issues/194improve speed of Ax=b solve operations for matrices2017-05-09T14:42:09ZFuchsi*improve speed of Ax=b solve operations for matricesperform checks similar to matlab: https://de.mathworks.com/help/matlab/ref/mldivide.html
for dense matrices:
- [X] not square operator -> use QR or SVD
- [ ] implement check & solver for permuted triangular matrices
- [x] not Hermitian -> LU solver
- [x] all positive / all negative diagonal -> try cholesky
- [X] default to LDL solver otherwise
for sparse:
- [ ] check for diagonality -> scale input
- [ ] check bandwidth < threshold -> banded solver
- [ ] implement check & solver for permuted triangular matrices
- [ ] not Hermitian -> LU solver
- [ ] all positive / all negative diagonal -> try cholesky
- [X] default to LDL solver otherwiseperform checks similar to matlab: https://de.mathworks.com/help/matlab/ref/mldivide.html
for dense matrices:
- [X] not square operator -> use QR or SVD
- [ ] implement check & solver for permuted triangular matrices
- [x] not Hermitian -> LU solver
- [x] all positive / all negative diagonal -> try cholesky
- [X] default to LDL solver otherwise
for sparse:
- [ ] check for diagonality -> scale input
- [ ] check bandwidth < threshold -> banded solver
- [ ] implement check & solver for permuted triangular matrices
- [ ] not Hermitian -> LU solver
- [ ] all positive / all negative diagonal -> try cholesky
- [X] default to LDL solver otherwiseVersion Xhttps://git.hemio.de/xerus/xerus/-/issues/130more example systems2019-03-04T12:56:09ZFuchsi*more example systemsWhen writing example systems, consider adding them to the library and / or homepage as examples.
possible examples include:
- Hennon-Heiles potential in schredinger eq
- masters equation
- some tensor completion / recovery
- standard hamiltonians (spin systems)
- simple fem system (diffusion?) with qtt
- uq systemWhen writing example systems, consider adding them to the library and / or homepage as examples.
possible examples include:
- Hennon-Heiles potential in schredinger eq
- masters equation
- some tensor completion / recovery
- standard hamiltonians (spin systems)
- simple fem system (diffusion?) with qtt
- uq systemVersion Xhttps://git.hemio.de/xerus/xerus/-/issues/118new contraction heuristics2017-07-13T14:42:00ZFuchsi*new contraction heuristics- [ ] implement brute force search for small number of nodes
- [ ] write more elaborate heuristics
- [ ] statistics about which heuristics were the best how often- [ ] implement brute force search for small number of nodes
- [ ] write more elaborate heuristics
- [ ] statistics about which heuristics were the best how oftenVersion Xhttps://git.hemio.de/xerus/xerus/-/issues/69SparseTensor aware contraction heuristics2019-03-04T13:31:16ZSebastian WolfSparseTensor aware contraction heuristicsThe complexity of sparse contractions is significantly different to the dense one and therefore requires new heuristics to detemrine the contraction order.The complexity of sparse contractions is significantly different to the dense one and therefore requires new heuristics to detemrine the contraction order.Version Xhttps://git.hemio.de/xerus/xerus/-/issues/50Kill all the TODOs in the sources2018-04-23T01:03:35ZSebastian WolfKill all the TODOs in the sourcesHeroicshttps://git.hemio.de/xerus/xerus/-/issues/42templatized value_t to allow calculations with complex numbers2019-03-04T13:54:41ZFuchsi*templatized value_t to allow calculations with complex numbersThis would also simplify issue #39 and is required for most quantum calculations.
- [ ] overloaded wrapper functions for lapacke, blas and suitesparse
- [ ] prob. two template types: value_t and real_t (return value of forb_norm eg. should not be complex)
- [ ] optionally interaction between different templatized versions (eg. Tensor<double> with Tensor<float>?)This would also simplify issue #39 and is required for most quantum calculations.
- [ ] overloaded wrapper functions for lapacke, blas and suitesparse
- [ ] prob. two template types: value_t and real_t (return value of forb_norm eg. should not be complex)
- [ ] optionally interaction between different templatized versions (eg. Tensor<double> with Tensor<float>?)Version Xhttps://git.hemio.de/xerus/xerus/-/issues/40coding conventions & instructions on how to create a pull request2019-04-01T00:36:43ZFuchsi*coding conventions & instructions on how to create a pull requestto allow others to contribute we should explain our coding standards, eg:
naming schemes arguments with _, function_names, variableNames, Classes, etc.etc.to allow others to contribute we should explain our coding standards, eg:
naming schemes arguments with _, function_names, variableNames, Classes, etc.etc.Version 4.0Sebastian WolfSebastian Wolf