Chook -- A comprehensive suite for generating binary optimization problems with planted solutions

Autor:	Perera, Dilina, Akpabio, Inimfon, Hamze, Firas, Mandra, Salvatore, Rose, Nathan, Aramon, Maliheh, Katzgraber, Helmut G.
Rok vydání:	2020
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks Computer Science - Other Computer Science
Druh dokumentu:	Working Paper
Popis:	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)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.14344 Zobrazit plný text záznamu View this record from Arxiv