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About 10,000 papers for “Algorithmic efficiency”

Dimensional Complexity and Algorithmic Efficiency

Alexander Ngu · International Journal of Modern Nonlinear Theory and Application · 2022

This paper uses the concept of algorithmic efficiency to present a unified theory of intelligence. Intelligence is defined informally, formally, and computationally. We introduce the concept of dimensional complexity in algorithmic efficiency and deduce that an optimally efficient algorithm has zero time complexity, zero space complexity, and an infinite dimensional complexity. This algorithm is used to generate the number line.

Cited by 4Open access

Dimensional Complexity and Algorithmic Efficiency

Ngu, Alexander · arXiv (Cornell University) · 2021

This paper uses the concept of algorithmic efficiency to present a unified theory of intelligence. Intelligence is defined informally, formally, and computationally. We introduce the concept of Dimensional complexity in algorithmic efficiency and deduce that an optimally efficient algorithm has zero Time complexity, zero Space complexity, and an infinite Dimensional complexity. This algorithm is used to generate the number line.

Cited by 0Open access

ALGORITHMIC ADVERTISING AND ITS ECONOMIC EFFICIENCY

Elena V. Shalamova, Margarita B. Kapelyush · EKONOMIKA I UPRAVLENIE PROBLEMY RESHENIYA · 2025

This article defines categories related to algorithmic advertising and its economic efficiency (digital economy, artificial intelligence, algorithmic advertising, economic efficiency). It describes the performance indicators of algorithmic advertising for consumers, including formulas. The stages and methods of algorithmic advertising placement are defined. The dynamics of advertiser budget expenditures on algorithmic advertising are illustrated. The advantages and potential challenges of algorithmic advertising are identified.

Cited by 0

ALGORITHMIC EFFICIENCY IN COMPUTING ISOGENY PATHS

Mohammed El Baraka, Siham Ezzouak · SPIRE - Sciences Po Institutional REpository · 2024

<div> This paper presents highly detailed techniques for optimizing the computation of isogeny paths between supersingular elliptic curves, a problem essential for cryptographic protocols like Supersingular Isogeny Diffie-Hellman (SIDH). We provide rigorous stepby-step mathematical proofs, deeply analyze the efficiency of each algorithm, and offer comprehensive security proofs, especially focusing on post-quantum cryptography. All mathematical results are fully justified with proofs at every level of detail, alongside empirical results demonstrating algorithmic improvements. </div>

Cited by 0Open access

Efficiency of Algorithmic Trading in Modern Markets

Swayam Salecha · Zenodo (CERN European Organization for Nuclear Research) · 2023

Algorithmic trading (AT) is the use of<br> computer programs to execute trades in financial<br> markets. AT has become increasingly popular in recent<br> years, as it offers a number of advantages over<br> traditional manual trading, such as speed, accuracy, and<br> consistency.<br> This research paper examines the efficiency of AT<br> in modern markets. It begins by providing a brief<br> overview of AT and its benefits. It then discusses the<br> different types of AT algorithms and how they are used.<br> The paper then reviews the literature on the efficiency of<br> AT, and presents the findings of a data analysis on the<br> impact of AT on market quality.<br> The findings of the study suggest that AT has a<br> number of positive effects on market efficiency. For<br> example, AT can help to reduce bid-ask spreads,<br> improve liquidity, and enhance price discovery.<br> However, the study also finds that AT can have some<br> negative effects on market quality, such as increasing<br> market volatility and facilitating market manipulation.<br> Overall, the study concludes that AT is a generally<br> efficient and beneficial trading method. However, it is<br> important to be aware of the potential negative effects of<br> AT on market quality, and to take steps to mitigate these<br> risks.

Cited by 0Open access

Algorithmic Analysis of Blockchain Efficiency with Communication Delay

Carlos Pinzón, Camilo Rocha, Jorge Finke · Lecture notes in computer science · 2020

A blockchain is a distributed hierarchical data structure. Widely-used applications of blockchain include digital currencies such as Bitcoin and Ethereum. This paper proposes an algorithmic approach to analyze the efficiency of a blockchain as a function of the number of blocks and the average synchronization delay. The proposed algorithms consider a random network model that characterizes the growth of a tree of blocks by adhering to a standard protocol. The model is parametric on two probability distribution functions governing block production and communication delay. Both distributions determine the synchronization efficiency of the distributed copies of the blockchain among the so- called workers and, therefore, are key for capturing the overall stochastic growth. Moreover, the algorithms consider scenarios with a fixed or an unbounded number of workers in the network. The main result illustrates how the algorithms can be used to evaluate different types of blockchain designs, e.g., systems in which the average time of block production can match the average time of message broadcasting required for synchronization. In particular, this algorithmic approach provides insight into efficiency criteria for identifying conditions under which increasing block production has a negative impact on the stability of a blockchain. The model and algorithms are agnostic of the blockchain’s final use, and they serve as a formal framework for specifying and analyzing a variety of non-functional properties of current and future blockchains.

Cited by 4Open access

Improving Algorithmic Efficiency using Cryptography: Trapdoored Matrices and Applications

Vinod Vaikuntanathan, Or Zamir · Society for Industrial and Applied Mathematics eBooks · 2026

Cryptographic primitives have been used for various non-cryptographic objectives, such as eliminating or reducing randomness and interaction. We show how to use cryptography to improve the time complexity of solving computational problems. Specifically, we show that under standard cryptographic assumptions, we can design algorithms that are asymptotically faster than existing ones while maintaining correctness.

Cited by 0

Algorithmic rationality: Epistemology and efficiency in the data sciences

Ian Lowrie · Big Data & Society · 2017

Recently, philosophers and social scientists have turned their attention to the epistemological shifts provoked in established sciences by their incorporation of big data techniques. There has been less focus on the forms of epistemology proper to the investigation of algorithms themselves, understood as scientific objects in their own right. This article, based upon 12 months of ethnographic fieldwork with Russian data scientists, addresses this lack through an investigation of the specific forms of epistemic attention paid to algorithms by data scientists. On the one hand, algorithms are unlike other mathematical objects in that they are not subject to disputation through deductive proof. On the other hand, unlike concrete things in the world such as particles or organisms, algorithms cannot be installed as the objects of experimental systems directly. They can only be evaluated in their functioning as components of extended computational assemblages; on their own, they are inert. As a consequence, the epistemological coding proper to this evaluation does not turn on truth and falsehood but rather on the efficiency of a given algorithmic assemblage. This article suggests that understanding the forms of algorithmic rationality employed in such inquiry is crucial for charting the place of data science within the contemporary academy and knowledge economy more generally.

Cited by 65Open access

Algorithmic efficiency indicator for the optimization of route size

Ciro Rodríguez, Miguel Sifuentes, Freddy Kaseng et al. · 3C Tecnología_Glosas de innovación aplicadas a la pyme · 2020

In software development, sometimes experienced programmers have difficulty determining before performing their tests, which algorithm will work best to solve a particular problem, and the answer to the question will always depend on the type of problem and nature of the data to be used, in this situation, it is necessary to identify which indicator is the most useful for a specific type of problem and especially in route optimization. Currently, there are techniques and algorithms used in Artificial Intelligence, which, however, cannot display their potential without a well-defined data set. The paper seeks to explain and propose an algorithm selection indicator to build a consistent data set, given the lack of availability of data that allows the best route size decision to be made.

Cited by 7Open access

Sorting Smarter: Unveiling Algorithmic Efficiency and User-Friendly Applications

Navya Tiwari · 2023

&lt;p&gt;This research delves into a comprehensive analysis of sorting algorithms, aiming to shed light on their runtime efficiency, space complexity, parallelizability, and stability. Focusing on five fundamental sorting algorithms—Quicksort, Mergesort, Bubble Sort, Selection Sort, and Insertion Sort—the study employs Python implementations validated against various datasets. The efficiency and running time analysis cover diverse scenarios, including pre-sorted, reverse-sorted, and randomly ordered datasets. The time complexity, evaluated for best-case, worst-case, and average-case scenarios, unveils distinct performance characteristics. Space complexity is measured to assess the memory efficiency of each algorithm.&lt;/p&gt; &lt;p&gt;A key aspect of the research is the comparative analysis, exploring how these algorithms perform concerning input sizes and distributions. Notably, the study evaluates the potential speedup through parallel implementations, revealing substantial efficiency gains. Surprisingly, Insertion Sort exhibits outstanding efficiency in parallelization, challenging conventional expectations. The stability analysis classifies Quick Sort as unstable, while Merge Sort, Insertion Sort, Bubble Sort, and Selection Sort demonstrate stability.&lt;/p&gt; &lt;p&gt;The project's unique contribution lies in the development of a user-friendly web-based application accessible at https://sortdata.w3spaces.com/index.html. This interactive platform translates algorithmic recommendations into a practical user experience. Users dynamically input datasets, receiving real-time suggestions on the most suitable sorting algorithm based on dataset size. The sorted data is visually presented in both tabular and graphical formats, enhancing user comprehension. In conclusion, this research not only contributes valuable insights into algorithmic behaviors but also bridges the gap between theoretical analyses and practical applications, providing a robust foundation for understanding sorting algorithms and their real-world implications.&lt;/p&gt;

Cited by 0Open access

Enhancing HyperAttention: A Novel Approach for Improved Algorithmic Efficiency

Xuefeng Xie, Yi Ding, Chao Jiang et al. · 2024

HyperAttention is a new attention mechanism proposed to address the inevitability of quadratic time complexity and the conditionality of linear time when dealing with extended contexts, which allows the algorithm to maintain the computational complexity in linear time despite having large entries or a large stability rank in the matrix. HyperAttention improves the speed of inference and training of the large language model, with only a slight performance degradation and a significant speedup compared to FlashAttention. In order to further improve the accuracy of HyperAttention while maintaining its speed, we optimised each module for the original algorithm to improve its efficiency. Our main contribution is to change the locally sensitive hash algorithm used in HyperAttention for detecting large values and instead use k-means for indexing, further reducing the model's perplexity. In addition, we add applications on chatglm3-6b and baichuan-7b as well as chatglm3-6b-32k which supports long contexts in practice to make up for the lack of informativeness of HyperAttention in terms of practical applications, and test results are given. The experiments reveal that although the improved algorithm has a longer and relatively stable time complexity, it significantly reduces perplexity. Notably, with 10 to 15 replacement layers, the increase in time complexity is less than the reduction in perplexity, achieving a favourable balance between performance and efficiency. Moreover, we also analyse the feasibility of other sampling methods to improve accuracy and discuss the strategy for selecting the patch layers to apply the fast algorithm.

Cited by 0Open access

Public attitudes toward algorithmic decision-making, fairness, and efficiency

Paulson, Elisabeth, Bansak, Kirk · Open Science Framework · 2022

This study aims to better understand public views and preferences on human versus algorithmic decision-makers in high-stakes settings. Specifically, we seek to better understand the public’s views and preferences with respect to fairness and efficiency in the decision-making process, and how these views and preferences differ depending on whether the decision-maker is a human or algorithm. For example, does the public place higher weight on fairness for algorithm decision-makers versus human decision-makers?

Cited by 0Open access

Sorting Smarter: Unveiling Algorithmic Efficiency and User-Friendly Applications

Navya Tiwari · 2023

&lt;p&gt;This research delves into a comprehensive analysis of sorting algorithms, aiming to shed light on their runtime efficiency, space complexity, parallelizability, and stability. Focusing on five fundamental sorting algorithms—Quicksort, Mergesort, Bubble Sort, Selection Sort, and Insertion Sort—the study employs Python implementations validated against various datasets. The efficiency and running time analysis cover diverse scenarios, including pre-sorted, reverse-sorted, and randomly ordered datasets. The time complexity, evaluated for best-case, worst-case, and average-case scenarios, unveils distinct performance characteristics. Space complexity is measured to assess the memory efficiency of each algorithm.&lt;/p&gt; &lt;p&gt;A key aspect of the research is the comparative analysis, exploring how these algorithms perform concerning input sizes and distributions. Notably, the study evaluates the potential speedup through parallel implementations, revealing substantial efficiency gains. Surprisingly, Insertion Sort exhibits outstanding efficiency in parallelization, challenging conventional expectations. The stability analysis classifies Quick Sort as unstable, while Merge Sort, Insertion Sort, Bubble Sort, and Selection Sort demonstrate stability.&lt;/p&gt; &lt;p&gt;The project's unique contribution lies in the development of a user-friendly web-based application accessible at https://sortdata.w3spaces.com/index.html. This interactive platform translates algorithmic recommendations into a practical user experience. Users dynamically input datasets, receiving real-time suggestions on the most suitable sorting algorithm based on dataset size. The sorted data is visually presented in both tabular and graphical formats, enhancing user comprehension. In conclusion, this research not only contributes valuable insights into algorithmic behaviors but also bridges the gap between theoretical analyses and practical applications, providing a robust foundation for understanding sorting algorithms and their real-world implications.&lt;/p&gt;

Cited by 1Open access