Dissertationen zum Thema „Online algorithms with recourse“
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Lowe, Wing Wah. „An exploration of stochastic decomposition algorithms for stochastic linear programs with recourse“. Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186667.
Der volle Inhalt der QuelleLi, Le. „Online stochastic algorithms“. Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.
Der volle Inhalt der QuelleThis thesis works mainly on three subjects. The first one is online clustering in which we introduce a new and adaptive stochastic algorithm to cluster online dataset. It relies on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that this algorithm has a regret bound of the order of and is asymptotically minimax under the constraint on the number of clusters. A RJMCMC-flavored implementation is also proposed. The second subject is related to the sequential learning of principal curves which seeks to represent a sequence of data by a continuous polygonal curve. To this aim, we introduce a procedure based on the MAP of Gibbs-posterior that can give polygonal lines whose number of segments can be chosen automatically. We also show that our procedure is supported by regret bounds with sublinear remainder terms. In addition, a greedy local search implementation that incorporates both sleeping experts and multi-armed bandit ingredients is presented. The third one concerns about the work which aims to fulfilling practical tasks within iAdvize, the company which supports this thesis. It includes sentiment analysis for textual messages by using methods in both text mining and statistics, and implementation of chatbot based on nature language processing and neural networks
Shi, Tian. „Novel Algorithms for Understanding Online Reviews“. Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104998.
Der volle Inhalt der QuelleDoctor of Philosophy
Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.
Trippen, Gerhard Wolfgang. „Online exploration and search in graphs /“. View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20TRIPPE.
Der volle Inhalt der QuelleLi, Rongbin, und 李榕滨. „New competitive algorithms for online job scheduling“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197555.
Der volle Inhalt der Quellepublished_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
ALBUQUERQUE, LUIZ FERNANDO FERNANDES DE. „ONLINE ALGORITHMS ANALYSIS FOR SPONSORED LINKS SELECTION“. PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16088@1.
Der volle Inhalt der QuelleLinks patrocinados são aqueles que aparecem em destaque nos resultados de pesquisas em máquinas de busca na Internet e são grande fonte de receita para seus provedores. Para os anunciantes, que fazem ofertas por palavras-chave para aparecerem em destaque nas consultas dos usuários, são uma oportunidade de divulgação da marca, conquista e manutenção de clientes. Um dos desafios das máquinas de busca neste modelo de negócio é selecionar os anunciantes que serão exibidos a cada consulta de modo a maximizar sua receita em determinado período. Este é um problema tipicamente online, onde a cada consulta é tomada uma decisão sem o conhecimento prévio das próximas consultas. Após uma decisão ser tomada, esta não pode mais ser alterada. Nesta dissertação avaliamos experimentalmente algoritmos propostos na literatura para solução deste problema, comparando-os à solução ótima offline, em simulações com dados sintéticos. Supondo que o conjunto das consultas diárias obedeça a uma determinada distribuição, propomos dois algoritmos baseados em informações estocásticas que são avaliados nos mesmos cenários que os outros algoritmos.
Sponsored links are those that appear highlighted at Internet search engine results. They are responsible for a large amount of their providers’ revenue. To advertisers, that place bids for keywords in large auctions at Internet, these links are the opportunity of brand exposing and achieving more clients. To search engine companies, one of the main challenges in this business model is selecting which advertisers should be allocated to each new query to maximize their total revenue in the end of the day. This is a typical online problem, where for each query is taken a decision without previous knowledge of future queries. Once the decision is taken, it can not be modified anymore. In this work, using synthetically generated data, we do experimental evaluation of three algorithms proposed in the literature for this problem and compare their results with the optimal offline solution. Considering that daily query set obeys some well known distribution, we propose two algorithms based on stochastic information, those are evaluated in the same scenarios of the others.
Pasteris, S. U. „Efficient algorithms for online learning over graphs“. Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
Der volle Inhalt der QuelleBonifaci, Vincenzo. „Models and algorithms for online server routing“. Doctoral thesis, La Sapienza, 2007. http://hdl.handle.net/11573/917056.
Der volle Inhalt der QuelleHarrington, Edward Francis. „Aspects of online learning /“. View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.
Der volle Inhalt der QuelleKamphans, Thomas. „Models and algorithms for online exploration and search“. [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980408121.
Der volle Inhalt der QuelleBirks, Martin David. „Online algorithms for temperature aware job scheduling problems“. Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/27686.
Der volle Inhalt der QuelleZadimoghaddam, Morteza. „Online allocation algorithms with applications in computational advertising“. Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87940.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references (pages 99-107).
Over the last few decades, a wide variety of allocation markets emerged from the Internet and introduced interesting algorithmic challenges, e.g., ad auctions, online dating markets, matching skilled workers to jobs, etc. I focus on the use of allocation algorithms in computational advertising as it is the quintessential application of my research. I will also touch on the classic secretary problem with submodular utility functions, and show that how it is related to advertiser's optimization problem in computational advertising applications. In all these practical situations, we should focus on solving the allocation problems in an online setting since the input is being revealed during the course of the algorithm, and at the same time we should make irrevocable decisions. We can formalize these types of computational advertising problems as follows. We are given a set of online items, arriving one by one, and a set of advertisers where each advertiser specifies how much she wants to pay for each of the online items. The goal is to allocate online items to advertisers to maximize some objective function like the total revenue, or the total quality of the allocation. There are two main classes of extensively studied problems in this context: budgeted allocation (a.k.a. the adwords problem) and display ad problems. Each advertiser is constrained by an overall budget limit, the maximum total amount she can pay in the first class, and by some positive integer capacity, the maximum number of online items we can assign to her in the second class.
by Morteza Zadimoghaddam.
Ph. D.
Packer, Heather S. „Evolving ontologies with online learning and forgetting algorithms“. Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/194923/.
Der volle Inhalt der QuelleMoon, Kyung Seob. „Consistency Maintenance Algorithms for Multiplayer Online Digital Games“. Thesis, Griffith University, 2007. http://hdl.handle.net/10072/367081.
Der volle Inhalt der QuelleThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Faculty of Engineering and Information Technology
Full Text
Chowuraya, Tawanda. „Online content clustering using variant K-Means Algorithms“. Thesis, Cape Peninsula University of Technology, 2019. http://hdl.handle.net/20.500.11838/3089.
Der volle Inhalt der QuelleWe live at a time when so much information is created. Unfortunately, much of the information is redundant. There is a huge amount of online information in the form of news articles that discuss similar stories. The number of articles is projected to grow. The growth makes it difficult for a person to process all that information in order to update themselves on a subject matter. There is an overwhelming amount of similar information on the internet. There is need for a solution that can organize this similar information into specific themes. The solution is a branch of Artificial intelligence (AI) called machine learning (ML) using clustering algorithms. This refers to clustering groups of information that is similar into containers. When the information is clustered people can be presented with information on their subject of interest, grouped together. The information in a group can be further processed into a summary. This research focuses on unsupervised learning. Literature has it that K-Means is one of the most widely used unsupervised clustering algorithm. K-Means is easy to learn, easy to implement and is also efficient. However, there is a horde of variations of K-Means. The research seeks to find a variant of K-Means that can be used with an acceptable performance, to cluster duplicate or similar news articles into correct semantic groups. The research is an experiment. News articles were collected from the internet using gocrawler. gocrawler is a program that takes Universal Resource Locators (URLs) as an argument and collects a story from a website pointed to by the URL. The URLs are read from a repository. The stories come riddled with adverts and images from the web page. This is referred to as a dirty text. The dirty text is sanitized. Sanitization is basically cleaning the collected news articles. This includes removing adverts and images from the web page. The clean text is stored in a repository, it is the input for the algorithm. The other input is the K value. All K-Means based variants take K value that defines the number of clusters to be produced. The stories are manually classified and labelled. The labelling is done to check the accuracy of machine clustering. Each story is labelled with a class to which it belongs. The data collection process itself was not unsupervised but the algorithms used to cluster are totally unsupervised. A total of 45 stories were collected and 9 manual clusters were identified. Under each manual cluster there are sub clusters of stories talking about one specific event. The performance of all the variants is compared to see the one with the best clustering results. Performance was checked by comparing the manual classification and the clustering results from the algorithm. Each K-Means variant is run on the same set of settings and same data set, that is 45 stories. The settings used are, • Dimensionality of the feature vectors, • Window size, • Maximum distance between the current and predicted word in a sentence, • Minimum word frequency, • Specified range of words to ignore, • Number of threads to train the model. • The training algorithm either distributed memory (PV-DM) or distributed bag of words (PV-DBOW), • The initial learning rate. The learning rate decreases to minimum alpha as training progresses, • Number of iterations per cycle, • Final learning rate, • Number of clusters to form, • The number of times the algorithm will be run, • The method used for initialization. The results obtained show that K-Means can perform better than K-Modes. The results are tabulated and presented in graphs in chapter six. Clustering can be improved by incorporating Named Entity (NER) recognition into the K-Means algorithms. Results can also be improved by implementing multi-stage clustering technique. Where initial clustering is done then you take the cluster group and further cluster it to achieve finer clustering results.
Hung, Yee-shing Regant. „Scheduling online batching systems“. Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34624016.
Der volle Inhalt der QuelleDrapkin, Dimitri [Verfasser], Rüdiger [Akademischer Betreuer] Schultz und Maarten H. van der [Akademischer Betreuer] Vlerk. „Models and algorithms for dominance-constrained stochastic programs with recourse / Dimitri Drapkin. Gutachter: Maarten H. van der Vlerk. Betreuer: Rüdiger Schultz“. Duisburg, 2014. http://d-nb.info/105157966X/34.
Der volle Inhalt der QuelleMak, Kin-sum. „Energy efficient online deadline scheduling“. Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558277.
Der volle Inhalt der Quelle麥健心 und Kin-sum Mak. „Energy efficient online deadline scheduling“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558277.
Der volle Inhalt der QuelleCESARI, TOMMASO RENATO. „ALGORITHMS, LEARNING, AND OPTIMIZATION“. Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699354.
Der volle Inhalt der QuelleZhu, Jianqiao, und 朱剑桥. „New results on online job scheduling“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50662351.
Der volle Inhalt der Quellepublished_or_final_version
Computer Science
Master
Master of Philosophy
Hung, Yee-shing Regant, und 洪宜成. „Scheduling online batching systems“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34624016.
Der volle Inhalt der QuelleOkamoto, Kazuya. „Efficient Algorithms for Stable Matching and Online Scheduling Problems“. 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/123858.
Der volle Inhalt der QuellePietrzyk, Peter [Verfasser]. „Local and online algorithms for facility location / Peter Pietrzyk“. Paderborn : Universitätsbibliothek, 2013. http://d-nb.info/1046073702/34.
Der volle Inhalt der QuelleWong, Chiu Wai M. Eng Massachusetts Institute of Technology. „Competitive algorithms for online matching and vertex cover problems“. Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85521.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references (pages 73-75).
The past decade has witnessed an explosion of research on the online bipartite matching problem. Surprisingly, its dual problem, online bipartite vertex cover, has never been explicitly studied before. One of the motivation for studying this problem is that it significantly generalizes the classical ski rental problem. An instance of such problems specifies a bipartite graph G = (L, R, E) whose left vertices L are offline and right vertices arrive online one at a time. An algorithm must maintain a valid vertex cover from which no vertex can ever be removed. The objective is to minimize the size of the cover. In this thesis, we introduce a charging-based algorithmic framework for this problem as well as its generalizations. One immediate outcome is a simple analysis of an optimal 1/1-1/e- competitive algorithm for online bipartite vertex cover. By extending the charging-based analysis in various nontrivial ways, we also obtain optimal l_1 e-competitive algorithms for the edge-weighted and submodular versions of online bipartite vertex cover, which all match the best performance of ski rental. As an application, we show that by analyzing our algorithm in the primal-dual framework, our result on submodular vertex cover implies an optimal (1/1-1/e)-competitive algorithm for its dual, online bipartite submodular matching. This problem is a generalization of online bipartite matching and may have applications in display ad allocation. We consider also the more general scenario where all the vertices are online and the graph is not necessarily bipartite, which is known as the online fractional vertex cover and matching problems. Our contribution in this direction is a primal-dual 1.901-competitive (or 1/1.901 ~~ 0.526) algorithm for these problems. Previously, it was only known that they admit a simple well-known 2-competitive (or 1/2) greedy algorithm. Our result is the first successful attempt to beat the greedy algorithm for these two problems. Moreover, our algorithm for the online matching problem significantly generalizes the traditional online bipartite graph matching problem, where vertices from only one side of the bipartite graph arrive online. In particular, our algorithm improves upon the result of the fractional version of the online edge-selection problem in Blum et. al. (JACM '06). Finally, on the hardness side, we show that no randomized online algorithm can achieve a competitive ratio better than 1.753 and 0.625 for the online fractional vertex cover problem and the online fractional matching problem respectively, even for bipartite graphs.
by Chiu Wai Wong.
M. Eng.
Renault, Marc Paul. „Lower and upper bounds for online algorithms with advice“. Paris 7, 2014. http://www.theses.fr/2014PA077196.
Der volle Inhalt der QuelleOnline algorithms operate in a setting where the input is revealed piece by piece; the pieces are called requests. After receiving each request, online algorithms must take an action before the next request is revealed, i. E. Online algorithms must make irrevocable decisions based on the input revealed so far without any knowledge of the future input. The goal is to optimize some cost function over the input. Competitive analysis is the standard method used to analyse the quality of online algorithms. The competitive ratio is the worst case ratio, over all valid finite request sequences, of the online algorithm's performance against an optimal offline algorithm for the same request sequence. The competitive ratio compares the performance of an algorithm with no knowledge about the future against an algorithm with full knowledge about the future. Since the complete absence of future knowledge is often not a reasonable assumption, models, termed online algorithms with advice, which give the online algorithms access to a quantified amount of future knowledge, have been proposed. The interest in this model is in examining how the competitive ratio changes as a function of the amount of advice. In this thesis, we present upper and lower bounds in the advice model for classical online problems such as the k-server problem, the bin packing problem, the dual bin packing (multiple knapsack) problem, scheduling problem on m identical machines, the reordering buffer management problem and the list update problem
Saint-Guillain, Michael. „Models and algorithms for online stochastic vehicle routing problems“. Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI068.
Der volle Inhalt der QuelleWhat will be tomorrow's big cities objectives and challenges? Most of the operational problems from the real world are inherently subject to uncertainty, requiring the decision system to compute new decisions dynamically, as random events occur. In this thesis, we aim at tackling an important growing problem in urban context: online dynamic vehicle routing. Applications of online vehicle routing in the society are manyfold, from intelligent on demand public transportation to sameday delivery services and responsive home healthcare. Given a fleet of vehicles and a set of customers, each being potentially able to request a service at any moment, the current thesis aims at answering the following question. Provided the current state at some moment of the day, which are the best vehicle actions such that the expected number of satisfied requests is maximized by the end of the operational day? How can we minimize the expected average intervention delays of our mobile units? Naturally, most of the requests remain unknown until they appear, hence being revealed online. We assume a stochastic knowledge on each operational problem we tackle, such as the probability that customer request arise at a given location and a given time of the day. By using techniques from operations research and stochastic programming, we are able to build and solve mathematical models that compute near-optimal anticipative actions, such as preventive vehicle relocations, in order to either minimize the overall expected costs or maximize the quality of service. Optimization under uncertainty is definitely not a recent issue. Thanks to evolution of both theoretical and technological tools, our ability to face the unknown constantly grows. However, most of the interesting problems remain extremely hard, if not impossible, to solve. There is still a lot of work. Generally speaking, this thesis explores some fundamentals of optimization under uncertainty. By integrating a stochastic component into the models to be optimized, we will see how it is in fact possible to create anticipation
Verschae, Tannenbaum Jose Claudio Verfasser], und Martin [Akademischer Betreuer] [Skutella. „The Power of Recourse in Online Optimization: Robust Solutions for Scheduling, Matroid and MST Problems / Jose Claudio Verschae Tannenbaum. Betreuer: Martin Skutella“. Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2012. http://d-nb.info/1020057424/34.
Der volle Inhalt der QuelleCunningham, James. „Efficient, Parameter-Free Online Clustering“. The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1606762403895603.
Der volle Inhalt der QuelleSan, Felice Mário César 1985. „Online facility location and Steiner problems = Problemas online de localização de instalações e de Steiner“. [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275552.
Der volle Inhalt der QuelleTese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-27T12:18:11Z (GMT). No. of bitstreams: 1 SanFelice_MarioCesar_D.pdf: 1457706 bytes, checksum: 4813f4ed44c52462656d56537d73d5dc (MD5) Previous issue date: 2015
Resumo: Nesta tese estudamos problemas online das famílias de localização de instalações e de Steiner, através da abordagem de análise competitiva. O objetivo nestes problemas é construir uma rede de custo mínimo para atender a uma determinada demanda. Nós apresentamos resultados conhecidos para o problema Online da Localização de Instalações (OFL), o problema Online da Árvore de Steiner (OST) e o problema Online Single-Source Rent-or-Buy (OSRoB). O OFL consiste em atender a um conjunto de clientes, através da abertura de algumas instalações e da conexão de cada cliente com uma instalação aberta. O OST tem por objetivo conectar um conjunto de terminais utilizando uma árvore, que pode conter vértices não terminais, chamados vértices de Steiner. O OSRoB é uma versão rent-or-buy do OST, onde todos os terminais devem ser conectados a um nó especial chamado raíz. Os algoritmos e técnicas que apresentamos para estes problemas são importantes no desenvolvimento dos nossos algoritmos para os problemas que consideramos. Apresentamos novos resultados para o problema Online da Localização de Instalações com Coleta de Prêmios (OPFL), o problema Online da Árvore Estrela de Steiner (OSTS), e o problema Online da Localização de Instalações Conectadas (OCFL). O OPFL é uma generalização do OFL, em que alguns clientes podem ficar desconectados mediante o pagamento de penalidades. O OSTS é uma variante do OST, em que os vértices possuem custos não negativos. O OCFL é uma combinação do OFL e do OST, em que um conjunto de clientes precisa ser atendido através da abertura de algumas instalações, da conexão de cada cliente com uma instalação aberta, e da construção de uma árvore, mais custosa, que conecta as instalações abertas
Abstract: In this thesis we study online problems from the facility location and Steiner families, through the point of view of competitive analysis. The goal in these problems is to build a minimum cost network to attend a certain demand. We present known results for the Online Facility Location problem (OFL), the Online Steiner Tree problem (OST) and the Online Single-Source Rent-or-Buy problem (OSRoB). The OFL consists of serving a set of clients by opening some facilities and by connecting each client to a facility. The OST aims to connect a set of terminals in order to create a tree network, that may contain nonterminals, called Steiner nodes. The OSRoB is a rent-or-buy version of the OST, in which all terminals must be connected to a special node called root. The algorithms and techniques that we present for these problems play an important role in the design of our algorithms for the problems we consider. We present new results for the Online Prize-Collecting Facility Location problem (OPFL), the Online Steiner Tree Star problem (OSTS), and the Online Connected Facility Location problem (OCFL). The OPFL is a generalization of the OFL, in which some clients may be left unconnected by paying a penalty. The OSTS is a variant of the OST, in which the nodes have non-negative costs. The OCFL is a combination of the OFL and the OST, in which a set of clients needs to be served by opening some facilities, by connecting each client to a facility, and by creating a more expensive tree network that connects the open facilities
Doutorado
Ciência da Computação
Doutor em Ciência da Computação
Kamphans, Tom [Verfasser]. „Models and Algorithms for Online Exploration and Search / Tom Kamphans“. Aachen : Shaker, 2011. http://d-nb.info/1098040260/34.
Der volle Inhalt der QuelleHan, Xin. „Online and approximation algorithms for bin-packing and knapsack problems“. 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/135979.
Der volle Inhalt der QuelleSaintillan, Yves. „Performance evaluation of online call routing and admission control algorithms“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ43558.pdf.
Der volle Inhalt der QuelleAngelopoulos, Spyros. „Efficient online algorithms for multicasting with bandwidth and delay guarantees“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0004/MQ45942.pdf.
Der volle Inhalt der QuelleLiu, Ming. „Design and Evaluation of Algorithms for Online Machine Scheduling Problems“. Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00453316.
Der volle Inhalt der QuelleBender, Marco [Verfasser]. „Randomized Approximation and Online Algorithms for Assignment Problems / Marco Bender“. München : Verlag Dr. Hut, 2015. http://d-nb.info/1074063333/34.
Der volle Inhalt der QuelleLee, Lap-kei, und 李立基. „New results on online job scheduling and data stream algorithms“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42182451.
Der volle Inhalt der QuelleChan, Sze-hang, und 陳思行. „Competitive online job scheduling algorithms under different energy management models“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/206690.
Der volle Inhalt der Quellepublished_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
Lee, Lap-kei. „New results on online job scheduling and data stream algorithms“. Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42182451.
Der volle Inhalt der QuelleLiu, Ming Chu Chengbin. „Design and Evaluation of Algorithms for Online Machine Scheduling Problems“. S. l. : S. n, 2009. http://theses.abes.fr/2009ECAP0028.
Der volle Inhalt der QuelleHavill, Jessen Tait. „Analysis of algorithms for online routing and scheduling in networks“. W&M ScholarWorks, 1998. https://scholarworks.wm.edu/etd/1539623929.
Der volle Inhalt der QuelleZhang, Lele. „On-line scheduling with constraints /“. Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/3538.
Der volle Inhalt der QuelleFung, Ping-yuen. „Online algorithms for the provision of quality of service in networks“. Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3158052X.
Der volle Inhalt der QuelleMinerva, Michela. „Automated Configuration of Offline/Online Algorithms: an Empirical Model Learning Approach“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22649/.
Der volle Inhalt der QuelleOchel, Marcel [Verfasser]. „Approximation and online algorithms for selected network optimization problems / Marcel Ochel“. Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2013. http://d-nb.info/1035688484/34.
Der volle Inhalt der QuelleFung, Ping-yuen, und 馮秉遠. „Online algorithms for the provision of quality of service in networks“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3158052X.
Der volle Inhalt der QuelleHANDA, MANISH. „ONLINE PLACEMENT AND SCHEDULING ALGORITHMS AND METHODOLOGIES FOR RECONFIGURABLE COMPUTING SYSTEMS“. University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100030953.
Der volle Inhalt der QuelleHarrington, Edward, und edwardharrington@homemail com au. „Aspects of Online Learning“. The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.
Der volle Inhalt der QuelleTripathi, Pushkar. „Allocation problems with partial information“. Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44789.
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Der volle Inhalt der Quelle