DarkLordRowan/shanks-university
Dec 2025 – Dec 2025
1 Month active
Languages Used
C++
Technical Skills
C++ developmentC++ programmingalgorithm designalgorithm developmentnumerical methodstesting frameworks
Aram
PROFILE
Aram
Worked on the DarkLordRowan/shanks-university repository to deliver core improvements in numerical series convergence, focusing on implementing Series Convergence Acceleration through Anderson Acceleration and J-Transformation. Developed new C++ classes and methods to support these algorithms, integrating them into a clean and maintainable API. Enhanced the testing framework using C++ and unit testing techniques, enabling comprehensive validation of various series and transformation schemes. Improved user interaction and tooling to allow configuration and repeatable experimentation with convergence methods. This work established a foundation for faster simulations, better scalability, and reduced computational costs in research and production environments involving numerical methods.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
2,218
Activity Months1
Your Network
8 peopleShared Repositories
8
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for DarkLordRowan/shanks-university focused on delivering core numerical convergence improvements and strengthening testing coverage. Delivered Series Convergence Acceleration using Anderson Acceleration and J-Transformation to speed up numerical series convergence, with new classes/methods and targeted tests to ensure correctness. Enhanced testing framework and user interaction for configuring and validating various series and transformations, improving reliability for researchers and production workloads. The work lays a foundation for faster simulations, better scalability, and reduced compute costs, while maintaining a clean API and high maintainability.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for DarkLordRowan/shanks-university focused on delivering core numerical convergence improvements and strengthening testing coverage. Delivered Series Convergence Acceleration using Anderson Acceleration and J-Transformation to speed up numerical series convergence, with new classes/methods and targeted tests to ensure correctness. Enhanced testing framework and user interaction for configuring and validating various series and transformations, improving reliability for researchers and production workloads. The work lays a foundation for faster simulations, better scalability, and reduced compute costs, while maintaining a clean API and high maintainability.
December 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ developmentC++ programmingalgorithm designalgorithm developmentnumerical methodstesting frameworksunit testing
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