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Chook -- A comprehensive suite for generating binary optimization problems with planted solutions
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We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions. Chook provides a cross-platform unified environment for solution planting using a number of techniques, such as tile planting, Wishart planting, equation planting, and deceptive cluster loop planting. Chook also incorporates planted solutions for higher-order (beyond quadratic) binary optimization problems. The support for various planting schemes and the tunable hardness allows the user to generate problems with a wide range of complexity on different graph topologies ranging from hypercubic lattices to fully-connected graphs.
Forward citations
Cited by 3 Pith papers
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Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization
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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines
A tensor-network branch-and-bound solver is slower and slightly less accurate than Ising machines on large random Pegasus and Zephyr spin glasses, but beats them on planted-instance energy.
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Continuous Approximation of the Ising Hamiltonian: Exact Ground States and Applications to Fidelity Assessment in Ising Machines
For couplings J_ij=(i^d+j^d)/N^d, the ground state is a two-block configuration whose boundary is set by an algebraic equation, giving an exact benchmark for Ising machines.
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