Efficient Algorithm Research Articles

Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects

We introduce a consumer choice model where each consumer’s utility is affected by their neighbors’ purchase probabilities in a network. We first characterize the choice probabilities and then consider the associated personalized assortment optimization problem. Although this problem is NP-hard, we show that for star networks, the optimal assortment to the central consumer cannot be strictly larger than that without network effects; and the optimal assortment to each peripheral consumer must be a revenue-ordered assortment. Then, we propose a novel idea in which the sellers are allowed to offer “randomized assortments” to each node in the network by viewing each node as a consumer group. We show that randomized assortments may further increase the revenue, and for general network setting, under certain conditions, the optimal assortment must be a combination of two adjacent revenue-ordered assortments. Finally, we study the associated optimal pricing problem and develop structural properties and efficient algorithms.

Comprehensive Geometric Parameterization and Computationally Efficient 3D Shape Matching Optimization of Realistic Stents

Abstract Flexible and compact shape representation schemes are essential for design optimization problems. Current shape representation schemes for coronary stent designs concern predominantly idealized or independent ring (IR) designs, which are outdated and only consider a small number of core design variables (such as strut width, height, and thickness) and ignore clinically critical design characteristics such as the number of connectors. No reports exist on the geometry parameterization of the latest helical stents (HS) that have more complex geometric designs than IR stents. Here, we present two new shape parameterization schemes to fully capture the 3D designs of contemporary IR and double-helix HS stents. We developed a 3D stent geometry builder based on 17 (IR) and 18 (HS) design variables, including strut width, thickness, height, number of connectors and rings, stent length, and strut centerline shape. The shape of the strut centerline was derived via a combination of NURBS, PARSEC, quarter circle, and straight line segments. Shape matching for complex 3D geometries, such as the contemporary stents within limited function evaluations, is not trivial and requires efficient parameterization and optimization algorithms. We used shape matching optimization with a limited function evaluation budget to test the proposed parameterization and two surrogate-assisted optimization algorithms relying on predictor believer and an expected improvement maximization formulation. The performance of these algorithms is objectively compared with a gradient-based optimization method to highlight their strengths. Our work paves the way for more realistic, full-fledged stent design optimization with structural and hemodynamic objectives in the future.

Overview on Design and Synthesis of Sulfonamide Derivative through Condensation of Amino Group Containing Drug

In medicinal and non-pharmacological chemistry, sulfonamides (SN) are an advisory functional group that forms the basis of many drugs. As such, they are very important. Recently, very significant techniques for the synthesis of sulfonamides have been developed. Numerous pharmacologic activities, including anti-dydropteroate synthetase and anti-carbonic anhydrase, are displayed by sulfonamides. Derivatives of sulfonamides can be used to treat a range of medical conditions, including glaucoma, hypoglycemia, stasis, diarrhea, and inflammation. Our current work has concentrated on creating and synthesizing sulfonamide derivatives via condensation reaction between amino group-containingdrugs.The functionality of sulfonamideinthe clinical trial for the treatment of various medical conditions. For these reasons, development of an efficient process for the synthesis of sulfonamides has always been in the focus for research in organic field synthesis. The most typical method for the synthesis involves reaction between primary or secondary amines and sulfonyl chloride in presence of organic or inorganic bases. Although this method is effective, but the nucleophilicity of amines may vary depending on the groups attached to it. In general, primary amines are highly reactive, whereas secondary amines show very low to almost nil reactivity. In this study, we have reviewed past and recent biological effects of some sulfonamide derivatives and some advances efficient synthetic procedures for some types of sulfonamides. A sulfonamide is a functional group that is the basis of several sulfa drugs and thereby are very much important scaffolds in medicinal as well as in synthetic organic chemistry. Recently very important methodologies have been developed for the synthesis of sulfonamide. This complex review article covers the recent developments (mainly in period 2013- 2019) of powerful methodologies for the synthesis of sulfonamide compounds, particularly where SO2N(R) moiety is not present in a cyclic structure and their applications in various fields during this period. A critical view of the mechanisms of the developed methodologies together with the scope and limitation of these methods adds an extra dimension to the text

Data Collection and Preprocessing in Web Usage Mining: Implementation and Analysis

Data collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining.

An Efficient Algorithm for the 2-Central Path Problem

In this paper we consider the following [Formula: see text]-Central Path Problem (2CPP): Given a set of [Formula: see text] polygonal curves [Formula: see text] in the plane, find two curves [Formula: see text] and [Formula: see text], called [Formula: see text]-central paths, that best represent all curves in [Formula: see text]. Despite its theoretical interest and a wide range of practical applications, 2CPP has not been well studied. In this paper, we first establish criteria that [Formula: see text] and [Formula: see text] ought to meet in order for them to best represent [Formula: see text]. In particular, we require that there exists parametrizations [Formula: see text] and [Formula: see text] ([Formula: see text]) of [Formula: see text] and [Formula: see text] respectively such that the maximum distance from [Formula: see text] to curves in [Formula: see text] is minimized. Then an efficient algorithm is presented to solve 2CPP under certain realistic assumptions. Our algorithm constructs [Formula: see text] and [Formula: see text] in [Formula: see text] time, where [Formula: see text] is the total complexity of [Formula: see text] (i.e., the total number of vertices and edges), [Formula: see text] is the number of curves in [Formula: see text], and [Formula: see text] is the inverse Ackermann function. Our algorithm uses parametric search technique and is considerably faster than arrangement-related algorithms (i.e. [Formula: see text]) when [Formula: see text] as in most real applications.

Game Pathfinding System Design based on A-Star Pathfinding Algorithm

Abstract. Pathfinding systems are used in many applications today, including navigation systems, autonomous driving systems, and games. The core part of the pathfinding system is the pathfinding algorithm, A Star pathfinding algorithm is a kind of efficient pathfinding algorithm, which is especially suitable for the design of game pathfinding. In this paper, based on the A Star algorithm, Godot game engine and C Sharp language are used to write a pathfinding program. After comparing different pathfinding methods and the estimated function, the A Star algorithm is improved according to the needs of the actual game pathfinding system, so that the pathfinding program can avoid crossing the obstacles through the diagonal by searching the surrounding obstacles and excluding unavailable nodes, and find a shorter path by using the Euclidean distance estimated function. The program applies the improved A Star algorithm in practice, and finally can complete the basic pathfinding needed in the game, which is practical for the design of the game's pathfinding system.

The Optimal Arrangement of Boats in a Coastal Maritime Passenger Lines System Using Graph Theory

This paper presents research in the field of optimization in maritime passenger traffic that can ensure the long-term sustainability of coastal maritime passenger lines system. For the purpose of the research contained in this paper, it has been hypothesized that the optimal arrangement of boats within a coastal maritime passenger lines system will reduce the consumption of propulsion energy, the emission of harmful gasses and operating costs. The aim of this paper is to present an efficient algorithm for a reduction in propulsion energy consumption in coastal maritime passenger lines systems by reassigning boats to lines that they service. The problem is modeled using a bipartite graph and the solution is obtained by searching for optimal matching using Edmonds’ algorithm. The authors apply, for the first time, Edmonds’ algorithm to the problems of the optimization of assignments of boats to lines. The research results were confirmed by tests on a representative example. The optimization results on only 10 ships in the given example show yearly savings of 91,097.30 L of fuel (lowering costs by EUR 69,233.95) and reducing CO2 by 243.59 tons, which proves that this algorithm found a much more efficient arrangement that could result in a significant reduction in propulsion energy consumption, thus providing economic and ecological benefits.

Joint Modeling of Longitudinal and Survival Data

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that are observed intermittently during the follow-up period. For various reasons, marginal approaches to model the event time, corresponding to separate approaches for survival data/longitudinal data, tend to induce bias and lose efficiency. Instead, a joint modeling approach that brings the two types of data together can reduce or eliminate the bias and yield a more efficient estimation procedure. A well-established avenue for joint modeling is the joint likelihood approach that often produces semiparametric efficient estimators for the finite-dimensional parameter vectors in both models. Through a transformation survival model with an unspecified baseline hazard function, this review introduces joint modeling that accommodates both baseline covariates and time-varying covariates. The focus is on the major challenges faced by joint modeling and how they can be overcome. A review of available software implementations and a brief discussion of future directions of the field are also included.

Effective automatic classification methods via deep learning for myopic maculopathy

BackgroundPathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.MethodsThis study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures—ResNet50, EfficientNet-B0, Vision Transformer (ViT), Contrastive Language-Image Pre-Training (CLIP), and RETFound—were utilized. An ensemble learning approach with weighted voting was employed to enhance model performance. The models were trained on a dataset of 2,159 annotated images from Shenzhen Eye Hospital, with performance evaluated using accuracy, sensitivity, specificity, F1-Score, Cohen’s Kappa, and area under the receiver operating characteristic curve (AUC).ResultsThe ensemble model achieved superior performance across all metrics, with an accuracy of 95.4% (95% CI: 93.0–97.0%), sensitivity of 95.4% (95% CI: 86.8–97.5%), specificity of 98.9% (95% CI: 97.1–99.5%), F1-Score of 95.3% (95% CI: 93.2–97.2%), Kappa value of 0.976 (95% CI: 0.957–0.989), and AUC of 0.995 (95% CI: 0.992–0.998). The voting ensemble method demonstrated robustness and high generalization ability in classifying complex lesions, outperforming individual models.ConclusionThe ensemble deep learning system significantly enhances the accuracy and reliability of MM classification. This system holds potential for assisting ophthalmologists in early detection and precise diagnosis, thereby improving patient outcomes. Future work could focus on expanding the dataset, incorporating image quality assessment, and optimizing the ensemble algorithm for better efficiency and broader applicability.

Simulation-based, Finite-sample Inference for Privatized Data

Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based “repro sample” approach to produce statistically valid confidence intervals and hypothesis tests, which builds on the work of Xie and Wang (2022). We show that this methodology is applicable to a wide variety of private inference problems, appropriately accounts for biases introduced by privacy mechanisms (such as by clamping), and improves over other state-of-the-art inference methods such as the parametric bootstrap in terms of the coverage and type I error of the private inference. We also develop significant improvements and extensions for the repro sample methodology for general models (not necessarily related to privacy), including 1) modifying the procedure to ensure guaranteed coverage and type I errors, even accounting for Monte Carlo error, and 2) proposing efficient numerical algorithms to implement the confidence intervals and p-values.

Investigation of efficiency of DoA algorithm on the base of experimental data and numerical simulations in automotive distributed system of incoherent radars

Investigation of efficiency of proposed DoA estimation algorithm for system of distributed incoherent automotive radars is performed on the base of experimental data and numerical simulation. It is shown that the proposed algorithm correctly recognizes the position of targets in considered experimental scenarios. Comparative numerical simulations show the efficiency of the proposed algorithm compared to the characteristics of single radar.

Flight tracking control of mini-drone: Flower pollination algorithm based model-free control

This paper presents a novel model-free control approach, Flower Pollination Algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization.

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization. Compared to previous active learning approaches, our algorithm adds two key features: a modified delta method incorporating force information to enhance efficiency in uncertainty estimation, and a quasi-Newton approach based upon a Hessian matrix calculated from the neural network; the later improving stability of optimization near critical points. We benchmarked our optimizer against commonly used optimization algorithms using systems including bulk metal, metal surface, metal hydride, and an oxide cluster. The results demonstrate that our optimizer effectively reduces the number of DFT force calls by 2-3 times in all test systems.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure.

Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets

In recent years, there has been a notably increased interest in the study of multivariate interactions and emergent higher-order dependencies. This is particularly evident in the context of identifying synergistic sets, which are defined as combinations of elements whose joint interactions result in the emergence of information that is not present in any individual subset of those elements. The scalability of frameworks such as partial information decomposition (PID) and those based on multivariate extensions of mutual information, such as O-information, is limited by combinational explosion in the number of sets that must be assessed. In order to address these challenges, we propose a novel approach that utilises stochastic search strategies in order to identify synergistic triplets within datasets. Furthermore, the methodology is extensible to larger sets and various synergy measures. By employing stochastic search, our approach circumvents the constraints of exhaustive enumeration, offering a scalable and efficient means to uncover intricate dependencies. The flexibility of our method is illustrated through its application to two epidemiological datasets: The Young Finns Study and the UK Biobank Nuclear Magnetic Resonance (NMR) data. Additionally, we present a heuristic for reducing the number of synergistic sets to analyse in large datasets by excluding sets with overlapping information. We also illustrate the risks of performing a feature selection before assessing synergistic information in the system.

Enhancing Rate-Splitting-Based Distributed Edge Computing via Multi-Group Information Recycling

To address the straggling effect in distributed edge computing, existing methods often introduce extra computation or communication costs. Recently, information recycling has emerged as an efficient solution that avoids such extra overhead. Nonetheless, the performance of the existing information recycling-assisted rate-splitting-based distributed edge computing scheme significantly hinges on the single common stream, whose rate is limited by the edge node (EN) with the worst channel quality. To this end, a multi-group information recycling scheme for rate-splitting-based distributed edge computing is proposed, where the ENs are clustered into multiple groups, each with its own common stream. In this way, the proposed scheme alleviates the communication limitation of a single common stream, thereby boosting the information recycling mechanism for promoted computation collaboration. A K-medoids-based grouping algorithm is designed for the general multi-antenna case, and a more efficient continuous partitioning-based grouping algorithm is proposed for the special case of single-antenna. Besides, a convex–concave procedure-based algorithm is developed to solve the corresponding latency optimization problem. Simulations demonstrate that the proposed scheme offers significant and robust advantages across various communication and computation conditions. In the considered scenarios, the proposed scheme can substantially reduce the processing latency by up to 51.0% compared to the conventional information recycling scheme.

Exploration and Development of Quantum Computing Algorithms for Optimization, Cryptography, and Machine Learning Applications

Quantum computing, with its potential to revolutionize computation, relies fundamentally on the development of efficient algorithms to leverage its unparalleled processing capabilities. This research delves into the creation, exploration, and refinement of quantum computing algorithms, focusing on their applications in optimization, cryptography, and machine learning. Quantum algorithms such as Shor's and Grover's have demonstrated remarkable advantages in factorization and search problems, while more recent innovations are tackling complex challenges in global optimization and data analysis. By integrating principles of quantum mechanics—such as superposition, entanglement, and interference—these algorithms promise exponential speed-ups over classical counterparts in specific domains. This study critically analyzes existing quantum algorithms, proposes advancements in hybrid quantum-classical frameworks, and explores their implications for practical problem-solving. Through simulations and theoretical evaluations, it aims to bridge the gap between quantum theory and real-world applications, contributing to the evolution of quantum computing as a transformative technology.

Bad Local Minima Exist in the Stochastic Block Model

AbstractWe study the disassortative stochastic block model with three communities, a well-studied model of graph partitioning and Bayesian inference for which detailed predictions based on the cavity method exist (Decelle et al. in Phys Rev E 84:066106, 2011). We provide strong evidence that for a part of the phase where efficient algorithms exist that approximately reconstruct the communities, inference based on maximum a posteriori (MAP) fails. In other words, we show that there exist modes of the posterior distribution that have a vanishing agreement with the ground truth. The proof is based on the analysis of a graph colouring algorithm from Achlioptas and Moore (J Comput Syst Sci 67:441–471, 2003).

The German Tank Problem with Multiple Factories

During the Second World War, estimates of the number of tanks deployed by Germany were critically needed. The Allies adopted a successful statistical approach to estimate this information: assuming that the tanks are sequentially numbered starting from 1, if we observe k tanks from an unknown total of N, then the best linear unbiased estimator for N is M(1+1/k)-1 where M is the maximum observed serial number. However, in many situations, the original German Tank Problem is insufficient, since typically there are l > 1 factories, and tanks produced by different factories may have serial numbers in disjoint ranges that are often far separated. Clark, Gonye and Miller presented an unbiased estimator for N when the minimum serial number is unknown. Provided one identifies which samples correspond to which factory, one can then estimate each factory's range and summing the sizes of these ranges yields an estimate for the rival's total productivity. We construct an efficient procedure to estimate the total productivity and prove that it is effective when log l/log k is sufficiently small. In the final section, we show that given information about the gaps, we can make an estimator that performs orders of magnitude better when we have a small number of samples.