학술논문

Chook -- A comprehensive suite for generating binary optimization problems with planted solutions
Document Type
Working Paper
Source
Subject
Quantum Physics
Condensed Matter - Disordered Systems and Neural Networks
Computer Science - Other Computer Science
Language
Abstract
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.
Comment: 8 pages, 2 figures, 3 tables. Python source code under ancillary files (v 0.2 uses an updated k-local scheme)