{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"Cotengra optimizes the contraction order of large tensor networks and einsum expressions, providing drop-in replacements for einsum and ncon with advanced slicing and path-finding strategies.","skillfed_tags":["tensor-networks","scientific-computing","optimization"],"use_cases":["Optimize contraction order for quantum circuit simulations or tensor network states in physics.","Find efficient einsum paths for large matrix chain multiplications in machine learning pipelines.","Parallelize and reduce memory footprint of tensor contractions via dynamic slicing.","Integrate optimized contraction strategies into existing code using einsum or ncon without rewriting.","Benchmark and compare contraction performance across different tensor libraries via autoray."],"what_it_does":"Cotengra is a Python library for optimizing the contraction of large tensor networks and einsum expressions. It addresses a core computational challenge: when contracting many tensors together, the order in which you combine them dramatically affects memory use and runtime. Cotengra provides multiple strategies\u2014including a hyper optimizer that samples and tunes contraction trees, simulated annealing, and dynamic slicing\u2014to find efficient contraction orders. It works as a drop-in replacement for numpy.einsum and ncon, and can generate paths compatible with opt_einsum, quimb, and other libraries.\n\nThe package is built on autoray, which abstracts tensor operations across different backends, so you can optimize contractions for tensors from many libraries even if they don't natively support einsum. It's designed for scientific and engineering workflows involving large hypergraph tensor networks, particularly in physics and machine learning applications where contraction cost dominates.","worth_installing":"Yes, if you work with large tensor networks or complex einsum expressions where contraction order matters. The library is actively maintained, has no known vulnerabilities, installs with minimal friction, and is permissively licensed. It's most valuable in scientific computing and physics simulations; less critical for small-scale tensor operations where contraction cost is negligible."},"id":"cotengra","links":{"html":"https://skillfed.io/packages/cotengra","md":"https://skillfed.io/packages/cotengra.md","pypi":"https://pypi.org/project/cotengra/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-22","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"cotengra","python_support":"supports_current","summary":"Hyper optimized contraction trees for large tensor networks and einsums."},"popularity":{"monthly_downloads":177800,"position":10206,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.2"}