ArianitHalimi/AIN_25
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Algorithm DevelopmentAlgorithm OptimizationData ProcessingFile I/OProblem SolvingPython Scripting
Lirim Islami
PROFILE
Lirim Islami
During May 2025, Lirim Islami developed an advanced solver scoring framework for the ArianitHalimi/AIN_25 repository, focusing on scalable experimentation and benchmarking. He implemented three new scoring algorithms—time-aware, greedy, and library-only—within the Python-based Solver, optimizing algorithm performance and upper bound estimation. Lirim extended the application’s core logic to process multiple input files from a directory, enabling batch evaluation across datasets and supporting rapid comparison of scoring strategies. His work emphasized algorithm development, data processing, and file I/O, establishing a robust foundation for data-driven solver configuration and facilitating more accurate, efficient experimentation with diverse problem instances.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
161
Activity Months1
Your Network
16 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for AIN_25 (ArianitHalimi/AIN_25): Implemented Advanced Solver Scoring with multi-instance evaluation and upper bound refinement, enabling scalable experimentation across datasets and more accurate upper-bound estimates. Delivered three new scoring algorithms (time_aware_score, greedy_score, library_only_score) and extended the application to process multiple input instances from a directory, with app.py __main__ wired to iterate over .txt/.in instances. This work establishes a solid foundation for rapid benchmarking of scoring strategies and data-driven decision making in solver configuration.
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for AIN_25 (ArianitHalimi/AIN_25): Implemented Advanced Solver Scoring with multi-instance evaluation and upper bound refinement, enabling scalable experimentation across datasets and more accurate upper-bound estimates. Delivered three new scoring algorithms (time_aware_score, greedy_score, library_only_score) and extended the application to process multiple input instances from a directory, with app.py __main__ wired to iterate over .txt/.in instances. This work establishes a solid foundation for rapid benchmarking of scoring strategies and data-driven decision making in solver configuration.
May 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm DevelopmentAlgorithm OptimizationData ProcessingFile I/OProblem SolvingPython Scripting
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