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You searched for subject:(submodular maximization). Showing records 1 – 9 of 9 total matches.

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Georgia Tech

1. Torrico Palacios, Alfredo Ignacio. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.

Degree: PhD, Industrial and Systems Engineering, 2019, Georgia Tech

 Resource allocation and subset selection are two relevant classes of problems in the core of combinatorial optimization. Over the past decade, there has been an… (more)

Subjects/Keywords: Combinatorial optimization; Online optimization; Online bipartite matching; Constrained submodular maximization

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APA (6th Edition):

Torrico Palacios, A. I. (2019). Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61720

Chicago Manual of Style (16th Edition):

Torrico Palacios, Alfredo Ignacio. “Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 17, 2021. http://hdl.handle.net/1853/61720.

MLA Handbook (7th Edition):

Torrico Palacios, Alfredo Ignacio. “Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.” 2019. Web. 17 Apr 2021.

Vancouver:

Torrico Palacios AI. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1853/61720.

Council of Science Editors:

Torrico Palacios AI. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61720


Iowa State University

2. Rajagopal Padmanabhan, Madhavan. Spreading information in social networks containing adversarial users.

Degree: 2020, Iowa State University

 In the modern day, social networks have become an integral part of how people communicate information and ideas. Consequently, leveraging the network to maximize information… (more)

Subjects/Keywords: Influence Maximization; Information Diffusion; Social Network Analysis; Submodular Optimization

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APA (6th Edition):

Rajagopal Padmanabhan, M. (2020). Spreading information in social networks containing adversarial users. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18384

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rajagopal Padmanabhan, Madhavan. “Spreading information in social networks containing adversarial users.” 2020. Thesis, Iowa State University. Accessed April 17, 2021. https://lib.dr.iastate.edu/etd/18384.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rajagopal Padmanabhan, Madhavan. “Spreading information in social networks containing adversarial users.” 2020. Web. 17 Apr 2021.

Vancouver:

Rajagopal Padmanabhan M. Spreading information in social networks containing adversarial users. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Apr 17]. Available from: https://lib.dr.iastate.edu/etd/18384.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rajagopal Padmanabhan M. Spreading information in social networks containing adversarial users. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18384

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Pennsylvania

3. Gillenwater, Jennifer Ann. Approximate Inference for Determinantal Point Processes.

Degree: 2014, University of Pennsylvania

 In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were… (more)

Subjects/Keywords: determinantal point processes; expectation-maximization; learning; MAP inference; random projections; submodular maximization; Computer Sciences; Engineering; Statistics and Probability

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APA (6th Edition):

Gillenwater, J. A. (2014). Approximate Inference for Determinantal Point Processes. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/1285

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gillenwater, Jennifer Ann. “Approximate Inference for Determinantal Point Processes.” 2014. Thesis, University of Pennsylvania. Accessed April 17, 2021. https://repository.upenn.edu/edissertations/1285.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gillenwater, Jennifer Ann. “Approximate Inference for Determinantal Point Processes.” 2014. Web. 17 Apr 2021.

Vancouver:

Gillenwater JA. Approximate Inference for Determinantal Point Processes. [Internet] [Thesis]. University of Pennsylvania; 2014. [cited 2021 Apr 17]. Available from: https://repository.upenn.edu/edissertations/1285.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gillenwater JA. Approximate Inference for Determinantal Point Processes. [Thesis]. University of Pennsylvania; 2014. Available from: https://repository.upenn.edu/edissertations/1285

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

4. Zhou, Lifeng. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.

Degree: PhD, Electrical Engineering, 2020, Virginia Tech

 Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of… (more)

Subjects/Keywords: Multi-Robot Systems; Resilient and Risk-aware Submodular Maximization; Planning and Assignment Algorithms; Event-Triggered Control

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APA (6th Edition):

Zhou, L. (2020). Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98596

Chicago Manual of Style (16th Edition):

Zhou, Lifeng. “Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 17, 2021. http://hdl.handle.net/10919/98596.

MLA Handbook (7th Edition):

Zhou, Lifeng. “Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.” 2020. Web. 17 Apr 2021.

Vancouver:

Zhou L. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/10919/98596.

Council of Science Editors:

Zhou L. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98596


University of Washington

5. Raut, Prasanna Sanjay. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.

Degree: 2021, University of Washington

 In this thesis, we consider online continuous DR-submodular maximization with linear stochastic long-term constraints. Compared to the prior work on online submodular maximization , our… (more)

Subjects/Keywords: regret analysis; non-convex optimization; online optimization; submodular maximization; Applied mathematics; Computer science; Operations research; Mechanical engineering

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APA (6th Edition):

Raut, P. S. (2021). Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/46846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Raut, Prasanna Sanjay. “Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.” 2021. Thesis, University of Washington. Accessed April 17, 2021. http://hdl.handle.net/1773/46846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Raut, Prasanna Sanjay. “Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.” 2021. Web. 17 Apr 2021.

Vancouver:

Raut PS. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. [Internet] [Thesis]. University of Washington; 2021. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1773/46846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raut PS. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. [Thesis]. University of Washington; 2021. Available from: http://hdl.handle.net/1773/46846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Ward, Justin. Oblivious and Non-oblivious Local Search for Combinatorial Optimization.

Degree: 2012, University of Toronto

Standard local search algorithms for combinatorial optimization problems repeatedly apply small changes to a current solution to improve the problem's given objective function. In contrast,… (more)

Subjects/Keywords: Approximation Algorithms; Local Search; Submodular Maximization; Independence Systems; 0984

…new areas, including submodular maximization, and obtain improved approximation for a… …for monotone submodular maximization subject to a matroid constraint. Moreover, if the total… …problem of linear and monotone submodular maximization in larger classes of set systems. There… …submodular maximization subject to a uniform matroid constraint. They show that a greedy algorithm… …Preliminaries for submodular maximization are summarized in a survey of Goundan and Schulz [46… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ward, J. (2012). Oblivious and Non-oblivious Local Search for Combinatorial Optimization. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/34957

Chicago Manual of Style (16th Edition):

Ward, Justin. “Oblivious and Non-oblivious Local Search for Combinatorial Optimization.” 2012. Doctoral Dissertation, University of Toronto. Accessed April 17, 2021. http://hdl.handle.net/1807/34957.

MLA Handbook (7th Edition):

Ward, Justin. “Oblivious and Non-oblivious Local Search for Combinatorial Optimization.” 2012. Web. 17 Apr 2021.

Vancouver:

Ward J. Oblivious and Non-oblivious Local Search for Combinatorial Optimization. [Internet] [Doctoral dissertation]. University of Toronto; 2012. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1807/34957.

Council of Science Editors:

Ward J. Oblivious and Non-oblivious Local Search for Combinatorial Optimization. [Doctoral Dissertation]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/34957


Georgia Tech

7. Du, Nan. Modeling, learning, and inference of high-dimensional asynchronous event data.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

 The increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently… (more)

Subjects/Keywords: Multivariate point process; Hawkes process; Survival analysis; Poisson process; Dirichlet process; Low-rank models; Social network analysis; Network structure inference; Information diffusion; Influence estimation; Influence maximization; Submodular maximization; Document clustering; Recommender systems

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APA (6th Edition):

Du, N. (2016). Modeling, learning, and inference of high-dimensional asynchronous event data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55568

Chicago Manual of Style (16th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 17, 2021. http://hdl.handle.net/1853/55568.

MLA Handbook (7th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Web. 17 Apr 2021.

Vancouver:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1853/55568.

Council of Science Editors:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55568


Kyoto University

8. Sakaue, Shinsaku. Online, Submodular, and Polynomial Optimization with Discrete Structures .

Degree: 2020, Kyoto University

Subjects/Keywords: Discrete structures; Zero-suppressed binary decision diagrams; Online combinatorial optimization; Submodular function maximization; Binary polynomial optimization

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APA (6th Edition):

Sakaue, S. (2020). Online, Submodular, and Polynomial Optimization with Discrete Structures . (Thesis). Kyoto University. Retrieved from http://hdl.handle.net/2433/253424

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sakaue, Shinsaku. “Online, Submodular, and Polynomial Optimization with Discrete Structures .” 2020. Thesis, Kyoto University. Accessed April 17, 2021. http://hdl.handle.net/2433/253424.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sakaue, Shinsaku. “Online, Submodular, and Polynomial Optimization with Discrete Structures .” 2020. Web. 17 Apr 2021.

Vancouver:

Sakaue S. Online, Submodular, and Polynomial Optimization with Discrete Structures . [Internet] [Thesis]. Kyoto University; 2020. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/2433/253424.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sakaue S. Online, Submodular, and Polynomial Optimization with Discrete Structures . [Thesis]. Kyoto University; 2020. Available from: http://hdl.handle.net/2433/253424

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Aslay, Çiğdem. From viral marketing to social advertising: ad allocation under social influence.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2016, Universitat Pompeu Fabra

 Esta tesis constituye una de las primeras investigaciones en la intersección entre propagación de influencia social, marketing viral y publicidad social. El objetivo es sacar… (more)

Subjects/Keywords: Influence maximization; Viral marketing; Social advertising; Submodular functions; Social influence; Similarity search; Reverse reachability sampling; Approximation algorithms; Social networks; Influència de maximització; Màrqueting viral; Publicitat social; Funcions submodulars; Influència social; Recerca de similitud; Presa de mostres d'accessibilitat inversa; Algoritmes d'aproximació; Xarxes socials; 62

…is NP-hard and it corresponds to the problem of monotone submodular function maximization… …109] for the maximization of a monotone submodular subject to a cardinality constraint… …Influence Maximization in Social Networks . 2.2 Modeling and Learning Influence Propagation 2.3… …7 7 9 11 12 Online Topic-aware Influence Maximization Queries 3.1 Introduction… …3.2 Related Work . . . . . . . . . . . . . . . . . . . 3.2.1 Influence Maximization… 

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APA (6th Edition):

Aslay, . (2016). From viral marketing to social advertising: ad allocation under social influence. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/401679

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Aslay, Çiğdem. “From viral marketing to social advertising: ad allocation under social influence.” 2016. Thesis, Universitat Pompeu Fabra. Accessed April 17, 2021. http://hdl.handle.net/10803/401679.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Aslay, Çiğdem. “From viral marketing to social advertising: ad allocation under social influence.” 2016. Web. 17 Apr 2021.

Vancouver:

Aslay . From viral marketing to social advertising: ad allocation under social influence. [Internet] [Thesis]. Universitat Pompeu Fabra; 2016. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/10803/401679.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aslay . From viral marketing to social advertising: ad allocation under social influence. [Thesis]. Universitat Pompeu Fabra; 2016. Available from: http://hdl.handle.net/10803/401679

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.