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Initialize the industrial profiler\nprofiler = ArpraxProfiler(mode=\"min\", repeats=5)\n\n# 2. Run a doubling test (OHPV2 Analysis)\n# This measures how Merge Sort scales as data size (N) doubles\nresults = profiler.run_doubling_test(\n    algorithms.sorting.merge_sort, \n    generators.random_array,\n    start_n=500,\n    rounds=5\n)\n\n# 3. Print the performance analysis\nprofiler.print_analysis(\"Merge Sort\", results)\n```\n\n## 🏗️ The Arprax Philosophy\n\n\u003e **Applied Data Intelligence requires more than just code—it requires proof.**\n\n* **Zero-Magic:** Every algorithm is written for clarity and performance. We don't hide logic behind obscure abstractions or hidden standard library calls.\n* **Empirical Evidence:** We don't just guess Big O complexity; we measure it using high-resolution timers and controlled environments.\n* **Industrial Scale:** Our tools are designed to filter out background CPU noise, providing reliable benchmarks for real-world software engineering.\n\n## 📚 Citation\n\n**To cite the Software:**\nSee the \"Cite this repository\" button on our [GitHub](https://github.com/arprax/arprax-algorithms).\n\n**To cite the Handbook (Documentation):**\n\n```bibtex\n@manual{arprax_handbook,\n  title        = {The Algorithm Engineering Handbook},\n  author       = {Chowdhury, Tanmoy},\n  organization = {Arprax LLC},\n  year         = {2026},\n  url          = {https://algorithms.arprax.com/book},\n  note         = {Accessed: 2026-02-01}\n}\n```\n\n---\n\n**© 2026 Arprax Lab** *A core division of Arprax dedicated to Applied Data Intelligence.*","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farprax%2Farprax-algorithms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farprax%2Farprax-algorithms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farprax%2Farprax-algorithms/lists"}