AlgOS: Algorithm Operating System
- URL: http://arxiv.org/abs/2504.04909v1
- Date: Mon, 07 Apr 2025 10:36:46 GMT
- Title: AlgOS: Algorithm Operating System
- Authors: Llewyn Salt, Marcus Gallagher,
- Abstract summary: AlgOS is an unopinionated, modular framework for algorithmic implementations.<n>It is designed to reduce the overhead of implementing new algorithms and to standardise the comparison of algorithms.
- Score: 2.5352713493505785
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation [54.707460684650584]
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention.
Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG)
RAGLAB is a modular and research-oriented open-source library that reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms.
arXiv Detail & Related papers (2024-08-21T07:20:48Z) - A General Online Algorithm for Optimizing Complex Performance Metrics [5.726378955570775]
We introduce and analyze a general online algorithm that can be used in a straightforward way with a variety of complex performance metrics in binary, multi-class, and multi-label classification problems.
The algorithm's update and prediction rules are appealingly simple and computationally efficient without the need to store any past data.
arXiv Detail & Related papers (2024-06-20T21:24:47Z) - LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics [0.023020018305241332]
This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework.<n>Given a set of criteria and a task definition (the search space), LLaMEA iteratively generates, mutates and selects algorithms.<n>We show how this framework can be used to generate novel black-box metaheuristic optimization algorithms automatically.
arXiv Detail & Related papers (2024-05-30T15:10:59Z) - Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation [27.378185644892984]
This paper introduces Large Language Models (LLMs) into algorithm selection for the first time.
LLMs not only captures the structural and semantic aspects of the algorithm, but also demonstrates contextual awareness and library function understanding.
The selected algorithm is determined by the matching degree between a given problem and different algorithms.
arXiv Detail & Related papers (2023-11-22T06:23:18Z) - ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle
Verifiers [60.6418431624873]
Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems.
We propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness.
Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT.
arXiv Detail & Related papers (2023-05-24T00:10:15Z) - FastDiagP: An Algorithm for Parallelized Direct Diagnosis [64.65251961564606]
FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without predetermining conflicts.
We propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming.
This mechanism helps to provide consistency checks with fast answers and boosts the algorithm's runtime performance.
arXiv Detail & Related papers (2023-05-11T16:26:23Z) - Accelerating ERM for data-driven algorithm design using output-sensitive techniques [26.32088674030797]
We study techniques to develop efficient learning algorithms for data-driven algorithm design.
Our approach involves two novel ingredients -- an output-sensitive algorithm for enumerating polytopes induced by a set of hyperplanes.
We illustrate our techniques by giving algorithms for pricing problems, linkage-based clustering and dynamic-programming based sequence alignment.
arXiv Detail & Related papers (2022-04-07T17:27:18Z) - Towards Large Scale Automated Algorithm Design by Integrating Modular
Benchmarking Frameworks [0.9281671380673306]
We present a first proof-of-concept use-case that demonstrates the efficiency of the algorithm framework ParadisEO with the automated algorithm configuration tool irace and the experimental platform IOHprofiler.
Key advantages of our pipeline are fast evaluation times, the possibility to generate rich data sets, and a standardized interface that can be used to benchmark very broad classes of sampling-based optimizations.
arXiv Detail & Related papers (2021-02-12T10:47:00Z) - Evolving Reinforcement Learning Algorithms [186.62294652057062]
We propose a method for meta-learning reinforcement learning algorithms.
The learned algorithms are domain-agnostic and can generalize to new environments not seen during training.
We highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games.
arXiv Detail & Related papers (2021-01-08T18:55:07Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - A Systematic Characterization of Sampling Algorithms for Open-ended
Language Generation [71.31905141672529]
We study the widely adopted ancestral sampling algorithms for auto-regressive language models.
We identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation.
We find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms.
arXiv Detail & Related papers (2020-09-15T17:28:42Z) - Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms.
We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
arXiv Detail & Related papers (2020-01-29T09:40:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.