Greedy Algorithm Research Articles

Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation

Recent code large language models (LLMs) have shown promising performance in generating standalone functions. However, they face limitations in repository-level code generation due to their lack of awareness of repository-level dependencies ( e.g., user-defined attributes), resulting in dependency errors such as undefined-variable and no-member errors. In this work, we introduce ToolGen , an approach that integrates autocompletion tools into the code LLM generation process to address these dependencies. ToolGen comprises two main phases: Trigger Insertion and Model Fine-tuning (Offline), and Tool-integrated Code Generation (Online). During the offline phase, ToolGen augments functions within a given code corpus with a special mark token, indicating positions to trigger autocompletion tools. These augmented functions, along with their corresponding descriptions, are then used to fine-tune a selected code LLM. In the online phase, ToolGen iteratively generates functions by predicting tokens step-by-step using the fine-tuned LLM. Whenever a mark token is encountered, ToolGen invokes the autocompletion tool to suggest code completions and selects the most appropriate one through constrained greedy search. We conduct comprehensive experiments to evaluate ToolGen ’s effectiveness in repository-level code generation across three distinct code LLMs: CodeGPT, CodeT5, and CodeLlama. To facilitate this evaluation, we create a benchmark comprising 671 real-world code repositories and introduce two new dependency-based metrics: Dependency Coverage and Static Validity Rate . The results demonstrate that ToolGen significantly improves Dependency Coverage by 31.4% to 39.1% and Static Validity Rate by 44.9% to 57.7% across the three LLMs, while maintaining competitive or improved performance in widely recognized similarity metrics such as BLEU-4, CodeBLEU, Edit Similarity, and Exact Match. On the CoderEval dataset, ToolGen achieves improvements of 40.0% and 25.0% in test pass rate (Pass@1) for CodeT5 and CodeLlama, respectively, while maintaining the same pass rate for CodeGPT. ToolGen also demonstrates high efficiency in repository-level code generation, with latency ranging from 0.63 to 2.34 seconds for generating each function. Furthermore, our generalizability evaluation confirms ToolGen ’s consistent performance when applied to diverse code LLMs, encompassing various model architectures and scales.

Trustworthiness analysis of heuristic routing algorithm with special constraints for autonomous vehicles in surveillance and coverage operations

This article aims to develop a fast, trustworthy routing algorithm for autonomous vehicles without requiring learning data, where the routing problem with special constraints and features is an extended version of the orienteering problem. For this, firstly, the method for selecting trustworthiness requirements is discussed to map relevant trustworthiness methods to stages in the system lifecycle. Then, a heuristic approach is proposed to automate mission planning within a limited time. Routing is handled through a greedy algorithm that selects tasks based on their distance to a baseline path and score. B-spline path smoothing is implemented for smooth and continuous routes. The results are examined in terms of robustness, explainability, transparency, reproducibility, and non-technical requirements. Scenarios with extreme parameters are generated for validation and testing. The algorithm demonstrates promising performance and fulfils the selected trustworthiness requirements, indicating that classical techniques remain significant candidates for fast, reliable, and unsupervised routing solutions.

Feature selection for high-dimensional data using a multivariate search space reduction strategy based scatter search

In feature selection, the increasing of the dimensionality and the complexity of feature interactions make the problem challenging. Furthermore, searching for an optimal subset of features from a high-dimensional feature space is known to be an NP-hard problem. To improve the efficiency and effectiveness of the search algorithm, feature grouping has emerged as a way to reduce the search space by clustering features according to a measure. In this work we propose to reduce the search space by applying a greedy algorithm, called Multivariate Greedy Predominant Groups Generator (MGPGG). MGPGG extends the idea of the Greedy Predominant Groups Generator (GPGG) algorithm by taking into account feature interaction among three or more features. For this purpose, MGPGG uses the Multivariate Symmetrical Uncertainty (MSU) to group features that share information about the class label. We also propose a Scatter Search strategy that integrates MGPGG to find small subsets of features with high predictive power. The proposed algorithm, called Multivariate Predominant Group-based Scatter Search (MPGSS), is tested on high-dimensional data from biomedical and text-mining fields. The proposal is compared with state-of-the-art feature selection strategies. Results show that MPGSS is competitive since it is capable of finding small subsets of features while keeping high predictive classification models.

Variable-Length Coding with Zero and Non-Zero Privacy Leakage

A private compression design problem is studied, where an encoder observes useful data Y, wishes to compress them using variable-length code, and communicates them through an unsecured channel. Since Y are correlated with the private attribute X, the encoder uses a private compression mechanism to design an encoded message C and sends it over the channel. An adversary is assumed to have access to the output of the encoder, i.e., C, and tries to estimate X. Furthermore, it is assumed that both encoder and decoder have access to a shared secret key W. In this work, the design goal is to encode message C with the minimum possible average length that satisfies certain privacy constraints. We consider two scenarios: 1. zero privacy leakage, i.e., perfect privacy (secrecy); 2. non-zero privacy leakage, i.e., non-perfect privacy constraint. Considering the perfect privacy scenario, we first study two different privacy mechanism design problems and find upper bounds on the entropy of the optimizers by solving a linear program. We use the obtained optimizers to design C. In the two cases, we strengthen the existing bounds: 1. |X|≥|Y|; 2. The realization of (X,Y) follows a specific joint distribution. In particular, considering the second case, we use two-part construction coding to achieve the upper bounds. Furthermore, in a numerical example, we study the obtained bounds and show that they can improve existing results. Finally, we strengthen the obtained bounds using the minimum entropy coupling concept and a greedy entropy-based algorithm. Considering the non-perfect privacy scenario, we find upper and lower bounds on the average length of the encoded message using different privacy metrics and study them in special cases. For achievability, we use two-part construction coding and extended versions of the functional representation lemma. Lastly, in an example, we show that the bounds can be asymptotically tight.

Glucagon-like Peptide-1 Receptor Agonist Impact on Chronic Ocular Disease Including Age-Related Macular Degeneration.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have risen exponentially in usage and have been shown to exert neuroprotective and anti-inflammatory effects across multiple organ systems. This study investigates whether GLP-1RAs influence the risk for age-related ocular diseases. Retrospective cohort study. This study utilized an electronic health records platform of patients in the United States. Patients older than 60 years of age with at least five years of ophthalmology follow-up and medication prescription documentation were included. Patients were categorized into five medication groups: GLP-1RAs, metformin, insulin, statins, or aspirin users. Cohorts were propensity-matched on demographics and chronic health conditions using a greedy matching algorithm. Outcomes of cataract, ocular hypertension, primary open angle glaucoma, non-exudative AMD, and exudative AMD were compared five years following initial medication prescription. We then examined earlier timepoints within the five-year period. Significance was defined as p<0.05 and HR threshold > 1.1 or < 0.9 to improve signal to noise ratio. Of the 9,669 patients taking GLP-1RAs, 84.4 percent were diabetic with an average BMI of 36.2. Propensity matched cohorts demonstrated GLP-1RAs were associated with reduced hazard of non-exudative AMD compared to metformin (HR 0.68, 95%CI: 0.56-0.84), insulin (HR 0.72, 95%CI: 0.58-0.89), and statins (HR 0.7, 95%CI: 0.57-0.87). These findings were validated compared to aspirin and in an independent older cohort of patients. This significant reduction appeared after three years compared to metformin (HR 0.69, 95%CI: 0.52-0.91), insulin (HR 0.66, 95%CI: 0.5-0.87), and statins (HR 0.67, 95%CI: 0.51-0.88). Time course results were validated using independent cohorts of propensity matched patients taking medications for three years. Notably, GLP-1RAs also significantly reduced the risk of exudative AMD (HR 0.7, 95%CI: 0.58-0.84) and POAG (HR 0.58, 95% CI 0.45-0.76) compared to insulin after three years. Usage of GLP-1RAs showed no persistent significant impact on the risk of cataract formation nor ocular hypertension after five years compared other medications. This study suggests GLP-1RAs may reduce the risk of multiple age-related ocular diseases and suggests the need for future prospective studies to validate these findings.

PROACTION: Profitable Transactions Selection Greedy Algorithm in Rational Proof-of-Work Mining

Despite the many consensus algorithms being used in blockchains, proof of work (PoW) is still the most common nowadays. The state-of-the-art mining strategy for PoW-based blockchain protocols consists of including as many transactions as possible in a block to maximize the block reward. Unfortunately, this strategy maximizes the block orphaning probability too. Recently, we proposed a rational mining strategy aimed at carefully balancing the trade-off between the block reward and the risk of block orphaning. In this work, we present PROACTION, a PROfitable transACTions selectION greedy algorithm that implements such a strategy. We evaluate the algorithm both analytically and experimentally on Bitcoin by assuming a variable random percentage of winning miners adopting PROACTION. Experiments show that when executing PROACTION, miners gain higher long-term rewards than when using the state-of-the-art strategy. The gain is in the order of the block orphaning probability. This result is particularly relevant for those PoW-based blockchain protocols in which such a probability is significant.

Greedy Algorithm Based Travel Route Planning under Multi-objective Decision Making

In this paper, greedy algorithm is used to optimize the planning of tourism routes based on multi-objective decision theory. The main goal of the research is to design the best travel route for foreign tourists by considering various factors such as time, cost and number of attractions. Firstly, this paper establishes a dataset including information of cities, attraction ratings, transportation time and cost through data integration and processing. Secondly, this paper used hierarchical analysis to evaluate 352 cities in multiple dimensions and identified the 50 cities that are most desirable to foreign tourists. Finally, combined with the greedy algorithm, this paper designed multiple optimization strategies under different constraints. The greedy algorithm used in the study can quickly provide tourist routes that meet the needs of tourists under multi-objective decision-making, and at the same time provides a new way of thinking for the planning of tourist routes.

Optimization of Crop Planting Strategies Based on Monte Carlo Simulation and Greedy Algorithm

Based on Monte Carlo simulation and greedy algorithm, this paper explores the optimization scheme of crop planting under different conditions. First, for the planting scheme under stable market conditions, this paper establishes an objective planning model to optimize the planting plan from 2024 to 2030 by maximizing the annual planting revenue and combining the constraints of various economic indicators. Further, considering that part of the crop may be stagnant or sold at a discounted price, this paper explores the impacts of different sales scenarios on the planting strategy. Then, for the planting strategy under uncertainty, this paper uses Monte Carlo simulation and time series regression analysis to predict various economic parameters of crops, and based on this prediction result, the greedy algorithm is applied to optimize the planting plan. Finally, the planting scheme is further optimized by considering the complementarity and substitutability between crops and combining the correlation between the indicators of crops.

Travel Route Planning Based on Genetic Algorithm and Greedy Algorithm

Travel Route Planning Based on Genetic Algorithm and Greedy Algorithm

Real-time deformable structure tracking in 3D ultrasound sequences using deformable convolutional layers.

Ultrasound imaging can provide 3D images of soft tissue structures in real-time without harmful radiation. Due to its high level of availability and low-cost characteristics, it is becoming more and more interesting for therapy guidance purposes like in radiotherapy. However, for usage in radiotherapy a robust and real-time image analysis method is required to be able to track the target during the treatment session. Soft tissue structures move in high dimensional motion patterns, including deformation especially due to breathing which makes the tracking task challenging. To overcome the deformation complexity, a novel real-time capable tracking approach in 3D ultrasound is proposed in this study. Deformable convolution layers are included in a 2D convolutional autoencoder architecture to learn deformation-invariant representations from ultrasound patches. For this, a novel 3D to 2D ultrasound patch reduction strategy is proposed. The tracking procedure is performed in the representation space of the autoencoder. We therefore implemented a greedy local search tracking algorithm and evaluated it in a preliminary study on the basis of nine expert-labeled landmarks in 18 in-vivo 3D ultrasound liver sequences. Four different autoencoder architectures with different deformable convolution arrangements are compared in the evaluation. The results show that using deformable convolution layers is beneficial compared to conventional convolution layers. A mean tracking error of 1.58 ± 0.87 mm was measured using deformable convolution, which is an improvement by 10.7% compared to conventional convolution. Additionally, evaluating the runtime shows that the tracking algorithm is real-time capable as the mean runtime per 3D ultrasound frame was around 4 ms. It could be shown that using deformable convolution layers is beneficial for learning meaningful representations of deformable structures from ultrasound patches. A tracking error similar to state-of-the-art methods could be achieved but in a runtime up to 100 times faster.

An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling

Heterogeneity among factories in distributed manufacturing significantly expands the solution space, complicating optimisation. Traditional centralised scheduling methods lack the scalability to adapt to varying factory scales. This paper proposes an end-to-end decentralised scheduling framework based on deep reinforcement learning (DRL) for dynamic distributed heterogeneous permutation flowshop scheduling problem (DDHPFSP) with random job arrivals. The framework utilises a multi-agent architecture, where each factory operates as an independent agent, enabling efficient, robust, and scalable scheduling. Specifically, the DDHPFSP is formulated as a partially observable Markov decision process (POMDP), with a state space reflecting heterogeneity and permutation characteristics and a new tailored reward function addressing sparse rewards and high reward variance. An end-to-end policy network with dual-layer architecture is developed, incorporating a feature extraction network to capture intrinsic relationships between jobs and heterogeneous factories, enhancing the agent's self-learning and policy evolution. Moreover, a backward swap search (BSS) method based on greedy heuristics optimises the pre-scheduling plan during the online phase with minimal computation time. Experimental results demonstrate the framework outperforms the best comparison methods by 39.76% on 540 baseline instances and 59.95% on 2430 generalisation instances. Furthermore, the framework's effectiveness improves by 68.9% with the introduction of the BSS method.

Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading

This paper deals with the optimization of the operational efficiency of a fleet of mobile robots, assigned with delivery-like missions in complex outdoor scenarios. The robots, due to limited onboard computation resources, need to offload some complex computing tasks to an edge/cloud server, requiring artificial intelligence and high computation loads. The mobile robots also need reliable and efficient radio communication with the network hosting edge/cloud servers. The resource assignment aims at minimizing the total latency and delay caused by the use of radio links and computation nodes. This minimization is a nonlinear integer programming problem, with high complexity. In this paper, we present reduced-complexity algorithms that allow to jointly optimize the available radio and computation resources. The original problem is reformulated and simplified, so that it can be solved by also selfish and greedy algorithms. For comparison purposes, a genetic algorithm (GA) is used as the baseline for the proposed optimization techniques. Simulation results in several scenarios show that the proposed sequential minimization (SM) algorithm achieves an almost optimal solution with significantly reduced complexity with respect to GA.

Application of Graph Theory and Variants of Greedy Graph Coloring Algorithms for Optimization of Distributed Peer-to-Peer Blockchain Networks

This paper investigates the application of graph theory and variants of greedy graph coloring algorithms for the optimization of distributed peer-to-peer networks, with a special focus on private blockchain networks. The graph coloring problem, as an NP-hard problem, presents a challenge in determining the minimum number of colors needed to efficiently allocate resources within the network. The paper deals with the influence of different graph density, i.e., the number of links, on the efficiency of greedy algorithms such as DSATUR, Descending, and Ascending. Experimental results show that increasing the number of links in the network contributes to a more uniform distribution of colors and increases the resistance of the network, whereby the DSATUR algorithm achieves the most uniform color saturation. The optimal configuration for a 100-node network has been identified at around 2000 to 2500 links, which achieves stability without excessive redundancy. These results are applied in the context of a private blockchain network that uses optimal connectivity to achieve high resilience and efficient resource allocation. The research findings suggest that adapting network configuration using greedy algorithms can contribute to the optimization of distributed systems, making them more stable and resilient to loads.

A Two-Stage Greedy Genetic Algorithm for Simultaneous Delivery and Monitoring Tasks with Time Windows

With advancements in drone driving technology, drones can now collaborate with trucks to execute tasks. However, existing drone–truck collaborative systems are limited to single-task objectives and lack efficiency in large-scale multi-task scenarios. Enhancing the efficiency of drone–truck cooperative systems necessitates the coordination of drone and truck paths to execute multiple tasks simultaneously. Addressing time conflicts in such scenarios remains a significant challenge. This study proposes an innovative drone–truck collaborative system enabling the concurrent execution of delivery and monitoring tasks within specified time windows. To minimize travel costs, a two-stage greedy genetic algorithm (TGGA) is introduced. The methodology initially separates tasks, processes them in batches, and subsequently recombines them to determine the final route. The simulation results indicate that TGGA outperforms existing heuristic algorithms.

Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles.

This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules that are true across multiple decision trees. Experiments are performed, taking into account knowledge representation and discovery perspectives. They show that, as the value of α, 0≤α<1, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models.

A robust single-machine scheduling problem with scenario-dependent processing times and release dates

Many uncertainties arise during the manufacturing process, such as changes in the working environment, traffic transportation delays, machine breakdowns, and worker performance instabilities. These factors can cause job processing times and ready times to change. In this study, we address a scheduling model for a single machine where both job release dates and processing times are scenario dependent. The objective is to minimize the total completion time across the worst-case scenarios. Even without the uncertainty factor, this problem is NP-hard. To solve it, we derive several properties and a lower bound used in a branch-and-bound method to find an optimal solution. We propose nine heuristics based on a linear combination of scenario-dependent processing times and release times for approximate solutions. Additionally, we offer an iterated greedy population-based algorithm that efficiently solves this problem by taking advantage of the diversity of solutions. We evaluate the performance of the proposed nine heuristics and the iterated greedy population-based algorithm.

Approximation algorithm of maximizing non-submodular functions under non-submodular constraint

Nowadays, maximizing the non-negative and non-submodular objective functions under Knapsack constraint or Cardinality constraint is deeply researched. Nevertheless, few studies study the objective functions with non-submodularity under the non-submodular constraint. And there are many practical applications of the situations, such as Epidemic transmission, and Sensor Placement and Feature Selection problem. In this paper, we study the maximization of the non-submodular objective functions under the non-submodular constraint. Based on the non-submodular constraint, we discuss the maximization of the objective functions with some specific properties, which includes the property of negative, and then, we obtain the corresponding approximate ratios by the greedy algorithm. What is more, these approximate ratios could be improved when the constraint becomes tight.

An optimized approach for container deployment driven by a two-stage load balancing mechanism.

Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests. The subsequent stage utilizes a genetic algorithm for fine-grained resource allocation, ensuring an equitable distribution of resources to each container service on a single virtual machine. This two-stage optimization enhances load balancing and resource utilization throughout the system. Empirical results indicate that this approach is more efficient and adaptable in comparison to the Grey Wolf Optimization (GWO) Algorithm, the Simulated Annealing (SA) Algorithm, and the GWO-SA Algorithm, significantly improving both resource utilization and load balancing performance on virtual machines.

Enhancing distributed blocking flowshop group scheduling: Theoretical insight and application of an iterated greedy algorithm with idle time insertion and rapid evaluation mechanisms

Enhancing distributed blocking flowshop group scheduling: Theoretical insight and application of an iterated greedy algorithm with idle time insertion and rapid evaluation mechanisms

Randomized greedy methods for weak submodular sensor selection with robustness considerations

We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We propose a pair of stochastic greedy algorithms, namely, Modified Randomized Greedy (MRG) and Dual Randomized Greedy (DRG) to approximately solve the budget- and performance-constrained problems, respectively. For both algorithms, we derive approximation guarantees that hold with high probability. We then examine the use of DRG in robust optimization problems wherein the objective is to maximize the worst-case of a number of weak submodular objectives and propose the Randomized Weak Submodular Saturation Algorithm (Random-WSSA). We further derive a high-probability guarantee for when Random-WSSA successfully constructs a robust solution. Finally, we showcase the effectiveness of these algorithms in a variety of relevant uses within the context of Earth-observing LEO constellations which estimate atmospheric weather conditions and provide Earth coverage.