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TenSolver.jl

Tensor Network-based solver for Quadratic Unconstrained Binary Optimization (QUBO) problems.

Overview

TenSolver.jl provides an efficient solver for QUBO problems using tensor network methods, specifically leveraging the Density Matrix Renormalization Group (DMRG) algorithm. The package is particularly useful for:

  • Solving large-scale binary optimization problems
  • Finding approximate solutions to NP-hard problems
  • Exploring the solution space of combinatorial optimization problems
  • GPU-accelerated optimization

Installation

using Pkg
Pkg.add("TenSolver")

Quick Start

The simplest way to use this package is passing a matrix to the solver:

using TenSolver

assets = ["Wind", "Solar", "Battery"]

# Wind and solar overlap, while storage pairs well with either source.
Q = [0.0 1.0 -1.5;
     1.0 0.0 -0.5;
     -1.5 -0.5 0.0]
E, psi = TenSolver.minimize(Q; verbosity=0)

# Verify we found the minimum
E ≈ -3.0

# output

true

The returned argument E is the calculated estimate for the minimum value, while psi is a probability distribution over all possible solutions to the problem. You can sample Boolean vectors from it:

x = TenSolver.sample(psi)

# Verify the sampled solution achieves the minimum and inspect the chosen assets
(x' * Q * x ≈ E, join(assets[findall(==(1), x)], ", "))

# output

(true, "Wind, Battery")

Features

  • Tensor Network Optimization: Uses advanced tensor network methods (DMRG) for efficient optimization
  • Probability Distribution: Returns a probability distribution over optimal solutions
  • JuMP Integration: Works seamlessly with the JuMP modeling language
  • GPU Support: Supports GPU acceleration via CUDA.jl, Metal.jl, and other accelerators
  • Flexible Configuration: Numerous parameters to control accuracy vs. performance trade-off

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