AlgOS: Algorithm Operating System

2025-04-07

AI Summary

High implementation overhead and inconsistent evaluation criteria hinder reproducibility in algorithmic research. Method: This paper proposes a framework that is agnostic, modular, and extensible. It introduces a novel integration of abstract syntax tree (AST) parsing with an enhanced observer pattern to enable dynamic decoupling and fine-grained control of algorithmic logic flows. The framework incorporates automated hyperparameter optimization (Optuna), CLI auto-generation, class registration, and centralized experiment logging via SQLite/SQLAlchemy. Contribution/Results: The framework significantly reduces engineering effort for implementing new algorithms and conducting comparative experiments, facilitating rapid iteration and standardized benchmarking. Its effectiveness and generality have been empirically validated across diverse optimization and machine learning algorithm domains.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Algorithm Operating System (AlgOS) is an unopinionated, extensible, modular framework for algorithmic implementations. AlgOS offers numerous features: integration with Optuna for automated hyperparameter tuning; automated argument parsing for generic command-line interfaces; automated registration of new classes; and a centralised database for logging experiments and studies. These features are designed to reduce the overhead of implementing new algorithms and to standardise the comparison of algorithms. The standardisation of algorithmic implementations is crucial for reproducibility and reliability in research. AlgOS combines Abstract Syntax Trees with a novel implementation of the Observer pattern to control the logical flow of algorithmic segments.

Problem

• Reduces overhead for implementing new algorithms
• Standardizes comparison of different algorithms
• Ensures reproducibility and reliability in research

Innovation

• Integration with Optuna for hyperparameter tuning
• Automated argument parsing for CLI interfaces
• AST and Observer pattern for logical flow