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generictensornetworks.jl's Introduction

GenericTensorNetworks

CI codecov Docs

This package implements generic tensor networks to compute solution space properties of a class of hard combinatorial optimization problems. The solution space properties include

  • The maximum/minimum solution sizes,
  • The number of solutions at certain sizes,
  • The enumeration/sampling of solutions at certain sizes.

The types of problems that can be solved using this package include Independent set problem, Maximal independent set problem, Spin-glass problem, Cutting problem, Vertex matching problem, Binary paint shop problem, Coloring problem, Dominating set problem, Set packing problem, Satisfiability problem and Set covering problem.

Installation

GenericTensorNetworks is a   Julia Language   package. To install GenericTensorNetworks, please open Julia's interactive session (known as REPL) and press the ] key in the REPL to use the package mode, and then type: 

pkg> add GenericTensorNetworks

To update, just type up in the package mode.

We recommend that you use Julia version >= 1.7; otherwise, your program may suffer from significant (exponential in the tensor dimension) overheads when permuting the dimensions of a large tensor. If you have to use an older version of Julia, you can overwrite the LinearAlgebra.permutedims! by adding the following patch to your own project.

# only required when your Julia version is < 1.7
using TensorOperations, LinearAlgebra
function LinearAlgebra.permutedims!(C::Array{T,N}, A::StridedArray{T,N}, perm) where {T,N}
    if isbitstype(T)
        TensorOperations.tensorcopy!(A, ntuple(identity,N), C, perm)
    else
        invoke(permutedims!, Tuple{Any,AbstractArray,Any}, C, A, perm)
    end
end

Supporting and Citing

Much of the software in this ecosystem was developed as a part of an academic research project. If you would like to help support it, please star the repository. If you use our software as part of your research, teaching, or other activities, we would like to request you to cite our work. The CITATION.bib file in the root of this repository lists the relevant papers.

Questions and Contributions

You can

  • Post a question on Julia Discourse forum and ping the package maintainer with @1115.
  • Discuss in the #graphs channel of the Julia Slack and ping the package maintainer with @JinGuo Liu.
  • Open an issue if you encounter any problems, or have any feature request.

generictensornetworks.jl's People

Contributors

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Stargazers

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Watchers

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Forkers

fanerst deyh2020 johnzl-777 pitmonticone yuxianggu arrogantgao yijiawwang

generictensornetworks.jl's Issues

Type Variables declared but not used

Using GenericTensorNetworks in Julia 1.8.2 produces a number of warnings on import indicating type variables in methods that are not used, restricted to the MdK4V/src/interfaces.jl and MdK4V/src/arithematics.jl files

Change Spinglass APIs

The current interfaces are difficult to read. I would prefer abandoning the problem reduction approach in the next version.

  • fix the SingleConfigMin property.

GenericTensorNetworks crashes without throwing any error messages

Environment

julia> versioninfo()
Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 8 × Apple M1
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, apple-m1)
  Threads: 4 on 4 virtual cores
Environment:
  JULIA_NUM_THREADS = 4

(...) pkg> st GenericTensorNetworks
Project ... v0.1.0
Status `.../Project.toml`
  [3521c873] GenericTensorNetworks v1.3.4

using GenericTensorNetworks

nv = 534
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8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018, 8977.042613709018]

problem = HyperSpinGlass(nv, hyperedges; weights)
solution = solve(problem, SingleConfigMin())

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Notes on polishing documentation

  • make plotting multiple images concise
  • polish performance tips and topics
  • move sampling to topic
  • talk about related works, e.g. papers citing it.
  • remember update CITATION.bib
  • introduce fixvertices feature.

compute ground state

from my point of view, computing ground state with gradients method such as backpropagation will cost much memory, which need cache every imtermediate contraction tensor. Also I am not familar with the details of the gradient method with Fowarddiff or Nilang. maybe they will save memory and compute faster. Here I just offer another method presented in below figureimage
image
Obviously, we only need to store the index when doing max operation, for a spin glass problem on a 3-regular graph with $n$ vertex corresponding to a tensornetwork with $n$ index and $3/2*n$ tensors; if we want to get the ground state, we at most store $n$ index tensor from contraction operation with max (when some contraction only involve sum operation, it won't creat index tensor , when some indices are merged, we will store indermidate index tensor less that $n$ )
Above figure is a very naive contraction order, because only involving 4 index, we can write the contraction result into formula and index tensor also can be written simply. Also I am not sure whether the method has advantage and whether you have implemented this method, Even maybe this method is conncted with gradient method. Look forward to your answer!

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