{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Provides fast implementations of core algorithms for multi-objective optimization, including nondominated set generation, dominance filtering, hypervolume and quality metrics, and empirical attainment function computation.","skillfed_tags":["multi-objective-optimization","performance-metrics","pareto-analysis"],"use_cases":["Evaluate and compare multi-objective optimization algorithms by computing hypervolume and other quality indicators on their solution sets.","Filter dominated solutions from a candidate set to identify the Pareto frontier in bi- or many-objective problems.","Analyze stochastic optimization algorithm performance using empirical attainment functions and summary attainment surfaces.","Build decision-support tools that need fast computation of dominance relationships and quality metrics.","Research and benchmark evolutionary multi-objective optimization methods with standardized performance measures."],"what_it_does":"moocore is a Python library that implements fast algorithms for multi-objective optimization problems. It provides core mathematical functions for working with Pareto-optimal solutions, including generation and transformation of nondominated sets, identification of dominated vectors, and computation of quality metrics like hypervolume, epsilon, and IGD. The library also implements the empirical attainment function, which characterizes the probabilistic distribution of outcomes from stochastic optimization algorithms.\n\nThe package is built on compiled C/C++ backends for performance and wraps them with a Python interface using cffi, numpy, and platformdirs. It targets researchers and practitioners in operations research, multi-criteria decision-making, and evolutionary optimization who need efficient implementations of these core algorithms. The library supports current Python versions (3.10 through 3.14) and is actively maintained.","worth_installing":"Yes, if you work in multi-objective optimization or need fast implementations of dominance filtering and quality metrics. The library is actively maintained, has no known vulnerabilities, supports modern Python versions, and offers prebuilt wheels for common platforms. The LGPL-2.1-or-later license requires attention if you plan proprietary use. Medium install friction is acceptable for a compiled package with broad platform coverage."},"id":"moocore","links":{"html":"https://skillfed.io/packages/moocore","md":"https://skillfed.io/packages/moocore.md","pypi":"https://pypi.org/project/moocore/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-12","license_spdx":"LGPL-2.1-or-later","license_treatment":"copyleft","name":"moocore","python_support":"supports_current","summary":"Core Algorithms for Multi-Objective Optimization"},"popularity":{"monthly_downloads":1019593,"position":4495,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.2"}