IA Scholar Query: Breaking Iterated Knapsacks.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgThu, 01 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Math and the Mouse: Explorations of Mathematics and Science in Walt Disney World
https://scholar.archive.org/work/jtkp5zcinzb2pmrpff2wt6233q
Math and the Mouse is an intensive, collaborative, project-driven, study away course that runs during the three-week May Experience term at Furman University and has many of the attributes of a course-based undergraduate research experience in mathematics. We take twelve students to Orlando, Florida to study the behind-the-scenes mathematics employed to make Walt Disney World operate efficiently. Students learn techniques of mathematical modeling (mostly resource allocation, logistics, and scheduling models), statistical analysis (mostly probability, clustering, data collection, and hypothesis testing), and flow management (queuing theory and some beginning flow dynamics) in an applied setting. Through planned course modules, collaborative activities, conversations with guest speakers, and three group projects, one of which is of the students' choosing, this academic experience provides an engaged learning experience that shows how material from eleven academic courses comes together in connection with real-world applications.Elizabeth L. Bouzarth, John M. Harris, Kevin R. Hutsonwork_jtkp5zcinzb2pmrpff2wt6233qThu, 01 Dec 2022 00:00:00 GMT2019
https://scholar.archive.org/work/wcy47hfvvvdwvfgnwx2cuak4ze
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.CSwork_wcy47hfvvvdwvfgnwx2cuak4zeMon, 28 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/a6rcrhwkfbbhrgfmps5qbny5a4
2019- AI & MLAI & MLwork_a6rcrhwkfbbhrgfmps5qbny5a4Sat, 26 Nov 2022 00:00:00 GMTImproved Bounds for Rectangular Monotone Min-Plus Product
https://scholar.archive.org/work/ir6ylsoaevbhnj42yotwyaxe24
In a recent breakthrough paper, Chi et al. (STOC'22) introduce an Õ(n^3 + ω/2) time algorithm to compute Monotone Min-Plus Product between two square matrices of dimensions n × n and entries bounded by O(n). This greatly improves upon the previous Õ(n^12 + ω/5) time algorithm and as a consequence improves bounds for its applications. Several other applications involve Monotone Min-Plus Product between rectangular matrices, and even if Chi et al.'s algorithm seems applicable for the rectangular case, the generalization is not straightforward. In this paper we present a generalization of the algorithm of Chi et al. to solve Monotone Min-Plus Product for rectangular matrices with polynomial bounded values. We next use this faster algorithm to improve running times for the following applications of Rectangular Monotone Min-Plus Product: M-bounded Single Source Replacement Path, Batch Range Mode, k-Dyck Edit Distance and 2-approximation of All Pairs Shortest Path. We also improve the running time for Unweighted Tree Edit Distance using the algorithm by Chi et al.Anita Dürrwork_ir6ylsoaevbhnj42yotwyaxe24Fri, 25 Nov 2022 00:00:00 GMTRouting Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network Approach
https://scholar.archive.org/work/luwib7yl3vc5dbcpgdg7xorbuq
Mobile parcel lockers have been recently proposed by logistics operators as a technology that could help reduce traffic congestion and operational costs in urban freight distribution. Given their ability to relocate throughout their area of deployment, they hold the potential to improve customer accessibility and convenience. In this study, we formulate the Mobile Parcel Locker Problem (MPLP) , a special case of the Location-Routing Problem (LRP) which determines the optimal stopover location for MPLs throughout the day and plans corresponding delivery routes. A Hybrid Q Learning Network based Method (HQM) is developed to resolve the computational complexity of the resulting large problem instances while escaping local optima. In addition, the HQM is integrated with global and local search mechanisms to resolve the dilemma of exploration and exploitation faced by classic reinforcement learning methods. We examine the performance of HQM under different problem sizes (up to 200 nodes) and benchmarked it against the exact approach and Genetic Algorithm (GA). Our results indicate that HQM achieves better optimisation performance with shorter computation time than the exact approach solved by the Gurobi solver in large problem instances. Additionally, the average reward obtained by HQM is 1.96 times greater than GA, which demonstrates that HQM has a better optimisation ability. Further, we identify critical factors that contribute to fleet size requirements, travel distances, and service delays. Our findings outline that the efficiency of MPLs is mainly contingent on the length of time windows and the deployment of MPL stopovers. Finally, we highlight managerial implications based on parametric analysis to provide guidance for logistics operators in the context of efficient last-mile distribution operations.Yubin Liu, Qiming Ye, Jose Escribano-Macias, Yuxiang Feng, Eduardo Candela, Panagiotis Angeloudiswork_luwib7yl3vc5dbcpgdg7xorbuqSat, 19 Nov 2022 00:00:00 GMTSymmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization
https://scholar.archive.org/work/3gqs3amewfbqfp6ekop4hxutdm
Constrained combinatorial optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In some heuristic solvers, these are typically addressed by introducing certain Lagrange multipliers in the cost function, by relaxing them in some way, or worse yet, by generating many samples and only keeping valid ones, which leads to very expensive and inefficient searches. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric tensor networks (TNs) and leverage their applicability as quantum-inspired generative models to assist in the search of solutions to combinatorial optimization problems. This allows us to exploit the generalization capabilities of TN generative models while constraining them so that they only output valid samples. Our constrained TN generative model efficiently captures the constraints by reducing number of parameters and computational costs. We find that at tasks with constraints given by arbitrary equalities, symmetric Matrix Product States outperform their standard unconstrained counterparts at finding novel and better solutions to combinatorial optimization problems.Javier Lopez-Piqueres, Jing Chen, Alejandro Perdomo-Ortizwork_3gqs3amewfbqfp6ekop4hxutdmWed, 16 Nov 2022 00:00:00 GMTHolistic Evaluation of Language Models
https://scholar.archive.org/work/xl5k5dwfrffx5c6m2ivnwuczju
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models. First, we taxonomize the vast space of potential scenarios (i.e. use cases) and metrics (i.e. desiderata) that are of interest for LMs. Then we select a broad subset based on coverage and feasibility, noting what's missing or underrepresented (e.g. question answering for neglected English dialects, metrics for trustworthiness). Second, we adopt a multi-metric approach: We measure 7 metrics (accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency) for each of 16 core scenarios when possible (87.5% of the time). This ensures metrics beyond accuracy don't fall to the wayside, and that trade-offs are clearly exposed. We also perform 7 targeted evaluations, based on 26 targeted scenarios, to analyze specific aspects (e.g. reasoning, disinformation). Third, we conduct a large-scale evaluation of 30 prominent language models (spanning open, limited-access, and closed models) on all 42 scenarios, 21 of which were not previously used in mainstream LM evaluation. Prior to HELM, models on average were evaluated on just 17.9% of the core HELM scenarios, with some prominent models not sharing a single scenario in common. We improve this to 96.0%: now all 30 models have been densely benchmarked on the same core scenarios and metrics under standardized conditions. Our evaluation surfaces 25 top-level findings. For full transparency, we release all raw model prompts and completions publicly for further analysis, as well as a general modular toolkit. We intend for HELM to be a living benchmark for the community, continuously updated with new scenarios, metrics, and models.Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreedawork_xl5k5dwfrffx5c6m2ivnwuczjuWed, 16 Nov 2022 00:00:00 GMTBeyond Worst-Case Budget-Feasible Mechanism Design
https://scholar.archive.org/work/xhezpflmzjepjpsz4t5qyau7ya
Motivated by large-market applications such as crowdsourcing, we revisit the problem of budget-feasible mechanism design under a "small-bidder assumption". Anari, Goel, and Nikzad (2018) gave a mechanism that has optimal competitive ratio 1-1/e on worst-case instances. However, we observe that on many realistic instances, their mechanism is significantly outperformed by a simpler open clock auction by Ensthaler and Giebe (2014), although the open clock auction only achieves competitive ratio 1/2 in the worst case. Is there a mechanism that gets the best of both worlds, i.e., a mechanism that is worst-case optimal and performs favorably on realistic instances? Our first main result is the design and the analysis of a natural mechanism that gives an affirmative answer to our question above: (i) We prove that on every instance, our mechanism performs at least as good as all uniform mechanisms, including Anari, Goel, and Nikzad's and Ensthaler and Giebe's mechanisms. (ii) Moreover, we empirically evaluate our mechanism on various realistic instances and observe that it beats the worst-case 1-1/e competitive ratio by a large margin and compares favorably to both mechanisms mentioned above. Our second main result is more interesting in theory: We show that in the semi-adversarial model of budget-smoothed analysis, where the adversary designs a single worst-case market for a distribution of budgets, our mechanism is optimal among all (including non-uniform) mechanisms; furthermore our mechanism guarantees a strictly better-than-(1-1/e) expected competitive ratio for any non-trivial budget distribution regardless of the market. We complement the positive result with a characterization of the worst-case markets for any given budget distribution and prove a fairly robust hardness result that holds against any budget distribution and any mechanism.Aviad Rubinstein, Junyao Zhaowork_xhezpflmzjepjpsz4t5qyau7yaWed, 16 Nov 2022 00:00:00 GMTApproximation algorithms for Steiner Tree Augmentation Problems
https://scholar.archive.org/work/yjp5g7ivlndijku3gcz3nqyiqe
In the Steiner Tree Augmentation Problem (STAP), we are given a graph G = (V,E), a set of terminals R ⊆ V, and a Steiner tree T spanning R. The edges L := E ∖ E(T) are called links and have non-negative costs. The goal is to augment T by adding a minimum cost set of links, so that there are 2 edge-disjoint paths between each pair of vertices in R. This problem is a special case of the Survivable Network Design Problem, which can be approximated to within a factor of 2 using iterative rounding . We give the first polynomial time algorithm for STAP with approximation ratio better than 2. In particular, we achieve an approximation ratio of (1.5 + ε). To do this, we employ the Local Search approach of for the Tree Augmentation Problem and generalize their main decomposition theorem from links (of size two) to hyper-links. We also consider the Node-Weighted Steiner Tree Augmentation Problem (NW-STAP) in which the non-terminal nodes have non-negative costs. We seek a cheapest subset S ⊆ V ∖ R so that G[R ∪ S] is 2-edge-connected. Using a result of Nutov , there exists an O(log |R|)-approximation for this problem. We provide an O(log^2 (|R|))-approximation algorithm for NW-STAP using a greedy algorithm leveraging the spider decomposition of optimal solutions.R. Ravi, Weizhong Zhang, Michael Zlatinwork_yjp5g7ivlndijku3gcz3nqyiqeSat, 12 Nov 2022 00:00:00 GMTAn Asymptotic (4/3+ε)-Approximation for the 2-Dimensional Vector Bin Packing Problem
https://scholar.archive.org/work/tk2dredntzhpvbbnawuqq5eonq
In this paper we consider the 2-Dimensional Vector Bin Packing Problem (2VBP), a well-studied generalization of classic Bin Packing that is widely applicable in resource allocation and scheduling. In 2VBP we are given a set of items, where each item is associated with a two-dimensional volume vector. The objective is to partition the items into a minimal number of subsets (bins), such that the total volume of items in each subset is at most 1 in each dimension. We give an asymptotic (4/3+ε)-approximation for the problem, thus improving upon the best known asymptotic ratio of (1+ln3/2+ε)≈ 1.406 due to Bansal, Elias and Khan (SODA 2016). Our algorithm applies a novel Round Round approach which iteratively solves a configuration LP relaxation for the residual instance (from previous iterations) and samples a small number of configurations based on the solution for the configuration LP. For the analysis we derive an iteration-dependent upper bound on the solution size for the configuration LP, which holds with high probability. We also show that our Round Round approach yields an AFPTAS for classic Bin Packing, suggesting its potential applicability for other variants of Bin Packing.Ariel Kulik, Matthias Mnich, Hadas Shachnaiwork_tk2dredntzhpvbbnawuqq5eonqFri, 11 Nov 2022 00:00:00 GMTHybrid Harmony Search for Stochastic Scheduling of Chemotherapy Outpatient Appointments
https://scholar.archive.org/work/ssjiyw67rzcztjyxn7bsy7qhdu
This research deals with the same-day chemotherapy outpatient scheduling problem that is recognized as a leading strategy to pursue the objective of reducing patient waiting time. Inspired by a real-world context and different from the other studies, we modeled a multi-stage chemotherapy ward in which the pharmacy is located away from the treatment area and drugs are delivered in batches. Processes in oncology wards are characterized by several sources of uncertainty that increase the complexity of the problem; thus, a stochastic approach was preferred to study the outpatient scheduling problem. To generate effective appointment schedules, we moved in two directions. First, we adopted a late-start scheduling strategy to reduce the idle times within and among the different stages, namely medical consultation, drug preparation and infusion. Then, since the problem is NP-hard in the strong sense, we developed a hybrid harmony search metaheuristic whose effectiveness was proved through an extended numerical analysis involving another optimization technique from the relevant literature. The outcomes from the numerical experiments confirmed the efficacy of the proposed scheduling model and the hybrid metaheuristic algorithm as well.Roberto Rosario Corsini, Antonio Costa, Sergio Fichera, Vincenzo Parrinellowork_ssjiyw67rzcztjyxn7bsy7qhduThu, 10 Nov 2022 00:00:00 GMTScalable Bicriteria Algorithms for Non-Monotone Submodular Cover
https://scholar.archive.org/work/gwbnm63j7bampm5xaca4dhueqa
In this paper, we consider the optimization problem (), which is to find a minimum cost subset of a ground set U such that the value of a submodular function f is above a threshold τ. In contrast to most existing work on , it is not assumed that f is monotone. Two bicriteria approximation algorithms are presented for that, for input parameter 0 < ϵ < 1, give O( 1 / ϵ^2 ) ratio to the optimal cost and ensures the function f is at least τ(1 - ϵ)/2. A lower bound shows that under the value query model shows that no polynomial-time algorithm can ensure that f is larger than τ/2. Further, the algorithms presented are scalable to large data sets, processing the ground set in a stream. Similar algorithms developed for also work for the related optimization problem of (). Finally, the algorithms are demonstrated to be effective in experiments involving graph cut and data summarization functions.Victoria G. Crawfordwork_gwbnm63j7bampm5xaca4dhueqaWed, 09 Nov 2022 00:00:00 GMTPyCSP3: Modeling Combinatorial Constrained Problems in Python
https://scholar.archive.org/work/vuf7d7sjzjdrtic4nn2v2xafpe
In this document, we introduce PyCSP3, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP3, you can write models of constraint satisfaction and optimization problems. More specifically, you can build CSP (Constraint Satisfaction Problem) and COP (Constraint Optimization Problem) models. Importantly, there is a complete separation between the modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP3 instance (file), and you solve that problem instance by means of a constraint solver. You can also directly pilot the solving procedure in PyCSP3, possibly conducting an incremental solving strategy. In this document, you will find all that you need to know about PyCSP3, with more than 50 illustrative models.Christophe Lecoutre, Nicolas Szczepanskiwork_vuf7d7sjzjdrtic4nn2v2xafpeMon, 07 Nov 2022 00:00:00 GMTXCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems
https://scholar.archive.org/work/cxl5eqccnrd3pjjiolxdvkvh74
We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups of constraints. The number of constraints is kept under control by introducing a limited set of basic constraint forms, and producing almost automatically some of their variations through lifting, restriction, sliding, logical combination and relaxation mechanisms. As a result, XCSP3 encompasses practically all constraints that can be found in major constraint solvers developed by the CP community. A website, which is developed conjointly with the format, contains many models and series of instances. The user can make sophisticated queries for selecting instances from very precise criteria. The objective of XCSP3 is to ease the effort required to test and compare different algorithms by providing a common test-bed of combinatorial constrained instances.Frederic Boussemart and Christophe Lecoutre and Gilles Audemard and Cédric Piettework_cxl5eqccnrd3pjjiolxdvkvh74Mon, 07 Nov 2022 00:00:00 GMTA Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
https://scholar.archive.org/work/p5m77fyapncmxhv6mgc7cv4un4
Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Yandi Li, Haobo Gao, Yunxuan Gao, Jianxiong Guo, Weili Wuwork_p5m77fyapncmxhv6mgc7cv4un4Sun, 06 Nov 2022 00:00:00 GMTOn the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration
https://scholar.archive.org/work/w6ron5dtjne4bfv7yautols4yi
Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Hatim Chergui, Xavier Costa-Perez, Christos Verikoukiswork_w6ron5dtjne4bfv7yautols4yiSun, 06 Nov 2022 00:00:00 GMTAn Improved Arc Flow Model with Enhanced Bounds for Minimizing the Makespan in Identical Parallel Machine Scheduling
https://scholar.archive.org/work/6kdqv7c65bdizjjqk7o2q3yz3e
In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood search algorithm was proposed to obtain an upper bound. Three lower bounds from the literature were utilized in the improved arc flow model to improve the efficiency of the mathematical formulation. In addition, a graph compression technique was proposed to reduce the size of the graph. As a consequence, the improved arc flow model was compared with an arc flow model from the literature. The computational results on benchmark instances showed that the improved arc flow model outperformed the literature arc flow model at finding optimal solutions for 99.97% of the benchmark instances, with the overall percentage of the reduction in time reaching 87%.Anis Gharbi, Khaled Bamatrafwork_6kdqv7c65bdizjjqk7o2q3yz3eFri, 04 Nov 2022 00:00:00 GMTSoft Masking for Cost-Constrained Channel Pruning
https://scholar.archive.org/work/g2wxjifocrhqhpecvecv5j737i
Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of network accuracy. Recent works permanently zero these channels during training, which we observe to significantly hamper final accuracy, particularly as the fraction of the network being pruned increases. We propose Soft Masking for cost-constrained Channel Pruning (SMCP) to allow pruned channels to adaptively return to the network while simultaneously pruning towards a target cost constraint. By adding a soft mask re-parameterization of the weights and channel pruning from the perspective of removing input channels, we allow gradient updates to previously pruned channels and the opportunity for the channels to later return to the network. We then formulate input channel pruning as a global resource allocation problem. Our method outperforms prior works on both the ImageNet classification and PASCAL VOC detection datasets.Ryan Humble, Maying Shen, Jorge Albericio Latorre, Eric Darve1, Jose M. Alvarezwork_g2wxjifocrhqhpecvecv5j737iFri, 04 Nov 2022 00:00:00 GMTPersonalized route planning : on finding your way in theory and practice
https://scholar.archive.org/work/xi3okj2q5vhzrl64ewxairuhjm
Personalisierung ist ein wichtiger Trend in der heutigen digitalen Welt. Im Bereich der Routenplanung hatte dieser jedoch noch keine starken Auswirkungen. Diese Dissertation beschäftigt sich mit dem multikriteriellen Routenplanungsansatz, der "personalized route planning model" genannt wird. Das Modell optimiert Konvexkombinationen von Routenplanungskriterien wie z.B. Reisezeit, Distanz oder Straßentyp und berechnet unterschiedliche Routen basierend auf den Präferenzen des Nutzers. Wir nennen die optimalen Pfade dieses Modells personalisierte Pfade. Das Angeben solcher Präferenzen ist keine einfache Aufgabe für einen Nutzer. Daher haben wir uns im praktischen Teil unserer Forschung auf Anwendungen konzentriert, die die Präferenzen im Backend verarbeiten ohne den Nutzer mit ihnen zu konfrontieren. Wir stellen drei praxisorientierte Anwendung und eine theoretische Analyse des Models vor. Für jede der Anwendungen entwickelten wir effiziente Algorithmen und verifizierten die Qualität und Laufzeit experimentell. Die erste Anwendung findet große Mengen von Alterantivrouten, die nicht zu sehr überlappen. Dazu entwickeln wir einen Algorithmus, der alle personalisierten Pfade aufzählen kann. Dieser wird dann erweitert um optional das Berechnen von Pfaden zu vermeiden, die zusätzliche Optimalitätskriterien nicht erfüllen. Darüber hinaus zeigen wir 'erfundene' Routenplanungskriterien, die die Ergebnisse noch weiter diversifizieren. In unseren Experimenten beobachteten wir große Mengen an sinnvollen Alternativrouten. Im Gegensatz zur ersten Anwendung sind die nächsten beiden Anwendung nicht an Endnutzer gerichtet, sondern dienen zum Lernen aus bestehenden Trajektorien. Die zweite Anwendung identifiziert Zwischenziele in Trajektorien. Wir nehmen an, dass Fahrer auf längeren Fahrten mit mehreren Zielen personalisierte Pfade zwischen den einzelnen Zielen wählen. Daher verwenden wir einen Trajektoriensegmentierungsansatz, der die Trajektorien in personalisierte Pfade aufteilt. Unser Ansatz konnte ca. 60% aller bekannten Zwischen [...]Florian Benjamin Ihle, Universität Stuttgartwork_xi3okj2q5vhzrl64ewxairuhjmFri, 04 Nov 2022 00:00:00 GMTSmallest covering regions and highest density regions for discrete distributions
https://scholar.archive.org/work/75nvprgz4nc6hg64cnpbk55kbi
This paper examines the problem of computing a canonical smallest covering region for an arbitrary discrete probability distribution. This optimisation problem is similar to the classical 0-1 knapsack problem, but it involves optimisation over a set that may be countably infinite, raising a computational challenge that makes the problem non-trivial. To solve the problem we present theorems giving useful conditions for an optimising region and we develop an iterative one-at-a-time computational method to compute a canonical smallest covering region. We show how this can be programmed in pseudo-code and we examine the performance of our method. We compare this algorithm with other algorithms available in statistical computation packages to compute HDRs. We find that our method is the only one that accurately computes HDRs for arbitrary discrete distributions.Ben O'Neillwork_75nvprgz4nc6hg64cnpbk55kbiFri, 04 Nov 2022 00:00:00 GMT