This package performs a faster tensor train (TT) decomposition for large sparse data. It can be several times to several tens time faster than the widely-used TT-SVD algorithm while keeping same accuracy. The speedup ratio depends on the sparsity of the data.
If you used this package, please cite the following paper:
[1] Lingjie Li, Wenjian Yu, and Kim Batselier, "Faster tensor train decomposition for sparse data," arXiv#1908.02721
- g++ version >= 7 or latest Clang
- Xerus: Please use FastTT-paper-version for correct API. Compile with Intel® Math Kernel Library for better reproduction.
- Boost
- cxxopts
TTTensor sptensor2tt(Tensor b, int vpos, int max_rank, double eps);
Transform tensor b into TT-tensor.
vpos: Parameter p in FastTT.
max_rank: The max rank of the result TT-tensor. Priority over eps.
eps: Max tolerated relative error.
❯ ./test -h
Test fast T2TT.
Usage:
test [OPTION...]
-f, --file arg Input file name
-t, --type arg Input file type: graph / image / tensor (default:
unspecific)
-U, --undirected If input graph is undirected
-O, --obeserved arg The obeservation ratio of the image (default: 0.01)
-R, --random Use random generated n^d tesors as input instead
-n, arg Parameter n of the tensor (default: 4)
-d, arg Parameter d of the tensor (default: 10)
-l, --n_list arg Use a list of n instead of n^d
-N, --nnz arg The number of nonzero elements of the random
generated tensor (default: 500)
-F, --fixed_rank arg Generate fixed-rank tesors (default: 0)
-s, --sparsity arg The sparsity of generated cores (default: 0.02)
-p, arg Parameter p of FastTT (default: -1)
-r, --max_rank arg Max ranks of the target tensor train (default: 0)
-e, --epsilon arg Desired tolerated relative error (default: 1e-14)
--ttsvd Test TT-SVD
--rttsvd arg Test Randomized TT-SVD for given target rank
(default: 10)
--nofasttt Do not test FastTT
-S, --simple Output simple result
--save arg Save the tensor as a tsv file (default: backup.tsv)
A random 100^3 tensor with nnz=500:
❯ ./test -R -n 100 -d 3 -N 500 -p 1
A random 20^5 tensor with TT-rank=20 and sparsity=0.1:
❯ ./test -R -n 20 -d 5 -F 20 -s 0.1 -p 2 -e 0.1
An image as a 10x20x20x10x15x20x3 tensor:
❯ ./test -f dolphin-4000x3000.txt -t image -O 0.001 -l 10,20,20,10,15,20,3 -r 100 -p 6
An FDM problem as a 1600^3 tensor:
❯ ./test -f fdm.tsv -t tensor -n 1600 -d 3 -p 1
An undirected graph as a 10^5 tensor:
❯ ./test -f roadNet-PA.txt -t graph -U -n 10 -d 5 -p 3 -e 1e-2
fdm.tsv: generated by
gen_fdm.pyroadNet-PA from SNAP