Online Algorithm Research Articles

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

An improved upper bound for the online graph exploration problem on unicyclic graphs

The online graph exploration problem, which was proposed by Kalyanasundaram and Pruhs (Theor Comput Sci 130(1):125–138, 1994), is defined as follows: Given an edge-weighted undirected connected graph and a specified vertex (called the origin), the task of an algorithm is to compute a path from the origin to the origin which contains all the vertices of the given graph. The goal of the problem is to find such a path of minimum weight. At each time, an online algorithm knows only the weights of edges each of which consists of visited vertices or vertices adjacent to visited vertices. Fritsch (Inform Process Lett 168:1006096, 2021) showed that the competitive ratio of an online algorithm is at most three for any unicyclic graph. On the other hand, Brandt et al. (Theor Comput Sci 839:176–185, 2020) showed a lower bound of two on the competitive ratio for any unicyclic graph. In this paper, we showed the competitive ratio of an online algorithm is at most 5/2 for any unicyclic graph.

Monitoring the lactation-related behaviors of sows and their piglets in farrowing crates using deep learning

Pig farming is a major sector of livestock production. The preweaning stage is a critical period in the pig farming process, where lactation-related behaviors between sows and their piglets directly influence the preweaning survivability of the piglets. Lactation-related behaviors are mutual interactions that require the combined monitoring of both the sow and her piglets. Conventional naked-eye observation is discontinuous and labor-intensive and may result in undetected abnormal behavior and economic losses. Thus, this study proposed to monitor the lactation-related behaviors of sows and their piglets simultaneously and continuously using computer vision. Videos were recorded from farrowing crates using embedded systems equipped with regular RGB cameras. The sow posture recognition model (SPRM), comprising a convolutional neural network (CNN) of the architecture EfficientNet and a long short-term memory network, was trained to identify seven postures of sows. The piglet localization and tracking model (PLTM), comprising a CNN of the architecture YOLOv7 and a simple online and realtime tracking algorithm, was trained to localize and track piglets in the farrowing crate. The sow posture information was then combined with the piglet activity to detect unfed piglets. The trained SPRM and PLTM reached an accuracy of 91.36% and a multiple object tracking accuracy of 94.6%. The performance of the proposed unfed piglet detection achieved a precision of 98.4% and a recall of 90.7%. A long-term experiment was conducted to monitor lactation-related behaviors of sows and their piglets from the birth of the piglets to day 15. The overall mean daily percentages ± standard deviations (SDs) of sow postures were 6.8% ± 2.9% for feeding, 8.8% ± 6.6% for standing, 11.8% ± 4.5% for sitting, 20.6% ± 16.3% for recumbency, 14.1% ± 6.5% for lying, and 38.1% ± 7.5% for lactating. The overall mean daily percentages ± SDs of piglet activities were 38.1% ± 7.5% for suckling, 22.2% ± 5.4% for active, and 39.7% ± 10.5% for rest. The proposed approach provides a total solution for the automatic monitoring of sows and their piglets in the farrowing house. This automatic detection of abnormal lactation-related behaviors can help in preventing piglet preweaning mortality and therefore aid pig farming efficiency.

Technical Note—Near-Optimal Bayesian Online Assortment of Reusable Resources

Near-Optimal Bayesian Online Assortment of Reusable Resources Motivated by rental services in e-commerce, we consider revenue maximization in the online assortment of reusable resources for different types of arriving consumers. We design competitive online algorithms compared with the optimal online policy in the Bayesian setting, where consumer types are drawn independently from known heterogeneous distributions over time. In scenarios with large initial inventories, our main result is a near-optimal competitive algorithm for reusable resources. Our algorithm relies on an expected linear programming (LP) benchmark, solves this LP, and simulates the solution through independent randomized rounding. The main challenge is achieving inventory feasibility efficiently using these simulation-based algorithms. To address this, we design discarding policies for each resource, balancing inventory feasibility and revenue loss. Discarding a unit of a resource impacts future consumption of other resources, so we introduce postprocessing assortment procedures to design and analyze our discarding policies. Additionally, we present an improved competitive algorithm for nonreusable resources and evaluate our algorithms using numerical simulations on synthetic data.

Domain independent heuristics for online stochastic contingent planning

AbstractPartially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning (POMCP) is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known $$h_{add}$$ h add heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.

Almost tight bounds for online hypergraph matching

In the online hypergraph matching problem, hyperedges of size k over a common ground set arrive online in adversarial order. The goal is to obtain a maximum matching (disjoint set of hyperedges). A naïve greedy algorithm for this problem achieves a competitive ratio of 1k. We show that no (randomized) online algorithm has competitive ratio better than 2+o(1)k. If edges are allowed to be assigned fractionally, we give a deterministic online algorithm with competitive ratio 1−o(1)ln⁡(k) and show that no online algorithm can have competitive ratio strictly better than 1+o(1)ln⁡(k). Lastly, we give a 1−o(1)ln⁡(k) competitive algorithm for the fractional edge-weighted version of the problem under a free disposal assumption.

Optimal algorithms for solving green online reheat furnace scheduling problems with deterioration effect

Optimizing reheating furnace scheduling in terms of energy saving and reheating time reduction is a vital component in improving competitive advantage of the steel enterprises. However, current reheating furnace scheduling models are still limited since (1) most of the scheduling problems involving reheating furnace suppose that the steel orders and their information are known in advance and (2) the reheating time of a slab in reheating operation is a constant in the majority of current reheating furnace scheduling models. To address these issues, three online reheat furnace scheduling models with deterioration effect are proposed in order to minimize the maximum residence time, the maximum energy consumption as well as total energy consumption of all slabs in reheating operation, respectively. Furthermore, for these online scheduling problems, deterministic online algorithms are presented and shown to be optimal, respectively. In addition, the computational experiments confirm that the three online algorithms developed in this paper are particularly efficient and effective in practice.

Online class cover problem

In this paper, we study the online class cover problem where a (finite or infinite) family F of geometric objects and a set Pr of red points in Rd are given a prior, and blue points from Rd arrives one after another. Upon the arrival of a blue point, the online algorithm must make an irreversible decision to cover it with objects from F that do not cover any points of Pr. The objective of the problem is to place a minimum number of objects. When F consists of axis-parallel unit squares in R2, we prove that the competitive ratio of any deterministic online algorithm is Ω(log⁡|Pr|), and also propose an O(log⁡|Pr|)-competitive deterministic algorithm for the problem.

Asynchronous model predictive control of LPV systems with stochastic communication protocol

This study is dedicated to exploring the challenge of asynchronous resilient robust model predictive control (RMPC) for linear parameter-varying (LPV) systems operating under a stochastic communication protocol (SCP). In contrast to the conventional Markov-based SCP, a novel sojourn probability-based SCP is introduced to regulate packet transmissions. This innovative approach simplifies the determination of transition probabilities and lessens the computational burden. Acknowledging the challenges associated with observing sojourn probability information, a mode detector is employed to describe the connection between mode discrepancies. To address parameter uncertainties, a novel detected-mode-based resilient control law is formulated to ensure the mean-square stability of the LPV system within the resilient RMPC framework. Additionally, an online algorithm for resilient RMPC is presented, grounded in an auxiliary optimization problem. Ultimately, the effectiveness of the proposed control strategy is validated via a high-purity distillation process model.

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

Scalable concept drift adaptation for stream data mining

Stream data mining aims to handle the continuous and ongoing generation of data flows (e.g. weather, stock and traffic data), which often encounters concept drift as time progresses. Traditional offline algorithms struggle with learning from real-time data, making online algorithms more fitting for mining the stream data with dynamic concepts. Among families of the online learning algorithms, single pass stands out for its efficiency in processing one sample point at a time, and inspecting it only once at most. Currently, there exist online algorithms tailored for single pass over the stream data by converting the problems of classification into minimum enclosing ball. However, these methods mainly focus on expanding the ball to enclose the new data. An excessively large ball might overwrite data of the new concept, creating difficulty in triggering the model updating process. This paper proposes a new online single pass framework for stream data mining, namely Scalable Concept Drift Adaptation (SCDA), and presents three distinct online methods (SCDA-I, SCDA-II and SCDA-III) based on that framework. These methods dynamically adjust the ball by expanding or contracting when new sample points arrive, thereby effectively avoiding the issue of excessively large balls. To evaluate their performance, we conduct the experiments on 7 synthetic and 5 real-world benchmark datasets and compete with the state-of-the-arts. The experiments demonstrate the applicability and flexibility of the SCDA methods in stream data mining by comparing three aspects: predictive performance, memory usage and scalability of the ball. Among them, the SCDA-III method performs best in all these aspects.

From Batch to Stream: Automatic Generation of Online Algorithms

From Batch to Stream: Automatic Generation of Online Algorithms

Online Unit Profit Knapsack with Predictions

A variant of the online knapsack problem is considered in the setting of predictions. In Unit Profit Knapsack, the items have unit profit, i.e., the goal is to pack as many items as possible. For Online Unit Profit Knapsack, the competitive ratio is unbounded. In contrast, it is easy to find an optimal solution offline: Pack as many of the smallest items as possible into the knapsack. The prediction available to the online algorithm is the average size of those smallest items that fit in the knapsack. 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Online scheduling problem of incompatible batch processing with deterioration effect and delivery time in the steel rolling process

As market competition intensifies, modern enterprises prioritize customer demands, leading to an increased emphasis on multi-variety and small-batch production. In this article, we study the online scheduling problem for incompatible batch processing with the deterioration effect to minimize the maximum delivery completion time in the billet rolling process, where the batch capacity and delivery capacity are unbounded. We assume that the processing time of job Jj is expressed as p j = a j ( A + Bt ) , where A > 0, B > 0, t denotes the start time of job and aj > 0 represents the job’s processing deterioration rate. For such an online problem, we first prove that the lower bound of the problem is ( 1 + B a max ) k , where k denotes the number of job families and a max represents the maximum deterioration ratio in job instance. Then we design an online algorithm Largest Delivery Time With Batch Processing and demonstrate that the competitive ratio of the algorithm is no more than 1 + ( 1 + B a max ) k . Finally, experiments on randomly generated instances prove that our algorithm is reasonable and effective.

Online Allocation with Replenishable Budgets: Worst Case and Beyond

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T^*\geq1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms

We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this problem, an online player attempts to purchase (alternatively, sell) fractional shares of an asset during a fixed time horizon with length T. At each time step, a cost function (alternatively, price function) is revealed, and the player must irrevocably decide an amount of asset to convert. The player also incurs a switching cost whenever their decision changes in consecutive time steps, i.e., when they increase or decrease their purchasing amount. We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms. We then propose learning-augmented algorithms that take advantage of untrusted black-box advice (such as predictions from a machine learning model) to achieve significantly better average-case performance without sacrificing worst-case competitive guarantees. Finally, we empirically evaluate our proposed algorithms using a carbon-aware EV charging case study, showing that our algorithms substantially improve on baseline methods for this problem.

The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting

We introduce and study the online pause and resume problem. In this problem, a player attempts to find the k lowest (alternatively, highest) prices in a sequence of fixed length T, which is revealed sequentially. At each time step, the player is presented with a price and decides whether to accept or reject it. The player incurs a switching cost whenever their decision changes in consecutive time steps, i.e., whenever they pause or resume purchasing. This online problem is motivated by the goal of carbon-aware load shifting, where a workload may be paused during periods of high carbon intensity and resumed during periods of low carbon intensity and incurs a cost when saving or restoring its state. It has strong connections to existing problems studied in the literature on online optimization, though it introduces unique technical challenges that prevent the direct application of existing algorithms. Extending prior work on threshold-based algorithms, we introduce double-threshold algorithms for both variants of this problem. We further show that the competitive ratios achieved by these algorithms are the best achievable by any deterministic online algorithm. Finally, we empirically validate our proposed algorithm through case studies on the application of carbon-aware load shifting using real carbon trace data and existing baseline algorithms.

Competitive Analysis of Algorithms for an Online Distribution Problem

We study an online distribution problem in which a producer has to send a load from an origin to a destination. At each time period before the deadline, they ask for transportation price quotes and have to decide to either accept or not accept the minimum offered price. If this price is not accepted, they have to pay a penalty cost, which may be the cost to ask for new quotes, the penalty cost for a late delivery, or the inventory cost to store the load for a certain duration. The aim is to minimize the sum of the transportation and the penalty costs. This problem has interesting real-world applications, given that transportation quotes can be obtained from professional websites nowadays. We show that the classical online algorithm used to solve the well-known Secretary problem is not able to provide, on average, effective solutions to our problem, given the trade-off between the transportation and the penalty costs. Therefore, we design two classes of online algorithms. The first class is based on a given time of acceptance, while the second is based on a given threshold price. We formally prove the competitive ratio of each algorithm, i.e., the worst-case performance of the online algorithm with respect to the optimal solution of the offline problem, in which all transportation prices are known at the beginning, rather than being revealed over time. The computational results show the algorithms’ performance on average and in the worst-case scenario when the transportation prices are generated on the basis of given probability distributions.

Assortment Optimization: An Annotated Reading Assortment

Which varieties or brands of a product should a retailer stock on its shelf? Carrying a large variety caters to more customers' needs, but could cannibalize the sales of high-end brands and also cause an inventory nightmare. Assortment optimization aims to formalize these tradeoffs, with the basic problem being as follows. There is a universe of brands j ∈ U , each with a market-accepted price r j . For any S ⊆ U , a function ϕ ( j, S ) indicates the probability that a representative customer from the population would purchase j when given the choice from assortment S , under the market prices. The optimization problem is to maximize the average revenue per customer, i.e. [EQUATION] (1) possibly with constraints on S due to shelf size. Assortment optimization started out by showing how to efficiently find the optimal S from the exponentially many possibilities, under well-established parametric forms for the function ϕ that are called ( discrete) choice models. Since then, the literature has developed choice models of its own that are specialized for assortment optimization. The basic problem has also been extended, and connected with topics such as online algorithms, machine learning, and mechanism design that are mainstream in the Economics and Computation community, with a vast horizon for future directions. This is an annotated reading list about assortment optimization, that aims to provide broad coverage while facing a "cardinality constraint" on the number of papers in the assortment.

Evaluation of a demand response online algorithm on the costs sustained by the user

The Demand Response (DR) algorithm is a technique that allows users to reduce their electricity consumption during peak hours, thereby reducing their overall energy costs. This study aims to evaluate the impact of the DR algorithm on the electrical costs sustained by the user. The research was conducted by analyzing the energy consumption patterns of a group of residential users with and without the implementation of the DR algorithm in their homes. The results of the study indicate that the implementation of the DR algorithm resulted in a significant reduction in the electrical costs sustained by the users. The study also found that the effectiveness of the DR algorithm was dependent on various factors such as the type of appliance, the time of day, and the user's behaviour. Overall, the study demonstrates that the DR algorithm is an effective tool for reducing energy costs and can be optimized through user participation and engagement. The findings of this research can help policymakers and energy providers to develop more effective demand response programs that benefit both users and the grid.