AI4Finance-Foundation
Tensor Chain Contraction Demo: REINFORCE and Brute Force
We have developed a training demo that utilizes REINFORCE and a brute force baseline to find the best contraction order for a tensor chain. We welcome any suggestions or feedback on this demo and environment!
Update Jan 10, 2023, Extend the environment design from tensor train to tensor networks.
@spicywei Wei, @Yonv1943 Jiahao, and Shixun extend the formulation of the tensor train environment to the tensor network. classical_simulation_01102023.pptx
Update Jan 09, 2023
- #7 Thanks to Wei @spicywei and Shixun @shixun404 developed a tensor train demo that achieves optimal for the tensor train with 4 tensors.
Update Jan 06, 2023
- REINFORCE single file demo
- Brute force baseline
- Environment design draft: 01032023_classical_simulation.pptx
We should confirm the formulation of the tensor chain ... It makes me confused about the difference between the implementation of this demo and the order file @spicywei provided with me. ( In the order file, we can see the tensor contraction between tensors that are not adjacent, while only the adjacent tensors are contracted in this demo......)
We currently address the optimal contraction order solution for tensor networks in the form of tensor-train, where contraction is performed only between adjacent tensors, and we will subsequently implement large-scale complex networks. @XiaoYangLiu-FinRL @ZhangAIPI @shixun404
okok !!! It means that I make some mistakes in my implementations...I will fix it...
@spicywei Would you please test the tensor train demo for cases with 6, 8, and 10 tensors?
@shixun404 Okay! ! I will test it on a tensor train containing 6, 8, and 10 as soon as possible!
@Yonv1943 Thank Jiahao for his dedicated efforts in creating the parallel setup for or_gym. This REINFORCE demo for the tensor train task may be beneficial for your development.
I have developed a training demo that utilizes REINFORCE and a brute force baseline to find the best contraction order for a tensor chain. I would greatly appreciate it if you could check the code and provide any comments. @ZhangAIPI @spicywei
Update Jan 10, 2023
@spicywei Wei, @Yonv1943 Jiahao, and Shixun extend the formulation of the tensor train environment to the tensor network. classical_simulation_01102023.pptx
Update Jan 09, 2023
- Fine tuned tensor_train demo for tensor train N=4 #7 Thanks to Wei @spicywei and Shixun @shixun404 developed a tensor train demo that achieves optimal for the tensor train with 4 tensors.
Update Jan 06, 2023
- REINFORCE single file demo
- Brute force baseline
- Environment design draft: 01032023_classical_simulation.pptx
I can download the "01032023_classical_simulation.pptx"
