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You searched for subject:(Submodular optimization). Showing records 1 – 19 of 19 total matches.

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University of Sydney

1. Yu, Baosheng. Robust Diversity-Driven Subset Selection in Combinatorial Optimization .

Degree: 2019, University of Sydney

 Subset selection is fundamental in combinatorial optimization with applications in biology, operations research, and computer science, especially machine learning and computer vision. However, subset selection… (more)

Subjects/Keywords: subset selection; bandits; submodular optimization

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APA (6th Edition):

Yu, B. (2019). Robust Diversity-Driven Subset Selection in Combinatorial Optimization . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/19834

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):

Yu, Baosheng. “Robust Diversity-Driven Subset Selection in Combinatorial Optimization .” 2019. Thesis, University of Sydney. Accessed June 26, 2019. http://hdl.handle.net/2123/19834.

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

MLA Handbook (7th Edition):

Yu, Baosheng. “Robust Diversity-Driven Subset Selection in Combinatorial Optimization .” 2019. Web. 26 Jun 2019.

Vancouver:

Yu B. Robust Diversity-Driven Subset Selection in Combinatorial Optimization . [Internet] [Thesis]. University of Sydney; 2019. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/2123/19834.

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

Council of Science Editors:

Yu B. Robust Diversity-Driven Subset Selection in Combinatorial Optimization . [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/19834

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

2. Yu, Jiajin. Optimization and separation for structured submodular functions with constraints.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Various kinds of optimization problems involve nonlinear functions of binary variables that exhibit a property of diminishing marginal returns. Such a property is known as… (more)

Subjects/Keywords: Submodular optimization; Mixed-integer optimization

…submodularity. Vast amount of work has been devoted to the problem of submodular optimization. On the… …information for several classes of submodular optimization problems. We strive for polynomial time… …aversion. Vast amount of work has been devoted to the problem of submodular optimization. On the… …classes of submodular optimization problems. We strive for polynomial time algorithms with… …problems in submodular optimization of general functions are NP-hard. For specific submodular… 

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APA (6th Edition):

Yu, J. (2015). Optimization and separation for structured submodular functions with constraints. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53517

Chicago Manual of Style (16th Edition):

Yu, Jiajin. “Optimization and separation for structured submodular functions with constraints.” 2015. Doctoral Dissertation, Georgia Tech. Accessed June 26, 2019. http://hdl.handle.net/1853/53517.

MLA Handbook (7th Edition):

Yu, Jiajin. “Optimization and separation for structured submodular functions with constraints.” 2015. Web. 26 Jun 2019.

Vancouver:

Yu J. Optimization and separation for structured submodular functions with constraints. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1853/53517.

Council of Science Editors:

Yu J. Optimization and separation for structured submodular functions with constraints. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53517


University of California – Berkeley

3. Wong, Chiu Wai. Optimization Everywhere: Convex, Combinatorial, and Economic.

Degree: Computer Science, 2018, University of California – Berkeley

 In this thesis we study fundamental problems that arise in optimization and its applications. We present provably efficient algorithms that achieve better running times or… (more)

Subjects/Keywords: Computer science; Mathematics; Operations research; Approximation Algorithms; Combinatorial Optimization; Convex Optimization; Market Equilibrium; Submodular Functions

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APA (6th Edition):

Wong, C. W. (2018). Optimization Everywhere: Convex, Combinatorial, and Economic. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/1f977832

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):

Wong, Chiu Wai. “Optimization Everywhere: Convex, Combinatorial, and Economic.” 2018. Thesis, University of California – Berkeley. Accessed June 26, 2019. http://www.escholarship.org/uc/item/1f977832.

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

MLA Handbook (7th Edition):

Wong, Chiu Wai. “Optimization Everywhere: Convex, Combinatorial, and Economic.” 2018. Web. 26 Jun 2019.

Vancouver:

Wong CW. Optimization Everywhere: Convex, Combinatorial, and Economic. [Internet] [Thesis]. University of California – Berkeley; 2018. [cited 2019 Jun 26]. Available from: http://www.escholarship.org/uc/item/1f977832.

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

Council of Science Editors:

Wong CW. Optimization Everywhere: Convex, Combinatorial, and Economic. [Thesis]. University of California – Berkeley; 2018. Available from: http://www.escholarship.org/uc/item/1f977832

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

4. Simão, Juliana Barby. Minimização de funções submodulares.

Degree: Mestrado, Ciência da Computação, 2009, University of São Paulo

Funções submodulares aparecem naturalmente em diversas áreas, tais como probabilidade, geometria e otimização combinatória. Pode-se dizer que o papel desempenhado por essas funções em otimização… (more)

Subjects/Keywords: algoritmos combinatórios; combinatorial algorithms; combinatorial optimization; funções submodulares; otimização combinatória; submodular functions

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APA (6th Edition):

Simão, J. B. (2009). Minimização de funções submodulares. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03112010-231536/ ;

Chicago Manual of Style (16th Edition):

Simão, Juliana Barby. “Minimização de funções submodulares.” 2009. Masters Thesis, University of São Paulo. Accessed June 26, 2019. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03112010-231536/ ;.

MLA Handbook (7th Edition):

Simão, Juliana Barby. “Minimização de funções submodulares.” 2009. Web. 26 Jun 2019.

Vancouver:

Simão JB. Minimização de funções submodulares. [Internet] [Masters thesis]. University of São Paulo; 2009. [cited 2019 Jun 26]. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03112010-231536/ ;.

Council of Science Editors:

Simão JB. Minimização de funções submodulares. [Masters Thesis]. University of São Paulo; 2009. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03112010-231536/ ;


University of Louisville

5. Emara, Wael. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.

Degree: PhD, 2012, University of Louisville

 The revolution in information technology and the explosion in the use of computing devices in people's everyday activities has forever changed the perspective of the… (more)

Subjects/Keywords: Machine learning; online learning; semi-supervised learning; Never-Ending Learning; submodular optimization; Support Vector Machines

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APA (6th Edition):

Emara, W. (2012). A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404

Chicago Manual of Style (16th Edition):

Emara, Wael. “A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.” 2012. Doctoral Dissertation, University of Louisville. Accessed June 26, 2019. 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404.

MLA Handbook (7th Edition):

Emara, Wael. “A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.” 2012. Web. 26 Jun 2019.

Vancouver:

Emara W. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. [Internet] [Doctoral dissertation]. University of Louisville; 2012. [cited 2019 Jun 26]. Available from: 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404.

Council of Science Editors:

Emara W. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. [Doctoral Dissertation]. University of Louisville; 2012. Available from: 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404


University of Washington

6. Wei, Kai. Submodular Optimization and Data Processing.

Degree: PhD, 2016, University of Washington

 Data sets are large and and are getting larger. Two common paradigms – data summarization and data partitioning, are often used to handle the big… (more)

Subjects/Keywords: big data; machine learning; speech recognition; submodular optimization; Electrical engineering; Computer science; Bioinformatics; electrical engineering

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APA (6th Edition):

Wei, K. (2016). Submodular Optimization and Data Processing. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/36601

Chicago Manual of Style (16th Edition):

Wei, Kai. “Submodular Optimization and Data Processing.” 2016. Doctoral Dissertation, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/36601.

MLA Handbook (7th Edition):

Wei, Kai. “Submodular Optimization and Data Processing.” 2016. Web. 26 Jun 2019.

Vancouver:

Wei K. Submodular Optimization and Data Processing. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/36601.

Council of Science Editors:

Wei K. Submodular Optimization and Data Processing. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/36601


Montana Tech

7. Zhong, Ninghui. Submodularity min-max results and total dual integrality of combinatorial optimization problems.

Degree: PhD, 1994, Montana Tech

Subjects/Keywords: Combinatorial optimization Mathematical models.; Submodular functions.

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APA (6th Edition):

Zhong, N. (1994). Submodularity min-max results and total dual integrality of combinatorial optimization problems. (Doctoral Dissertation). Montana Tech. Retrieved from https://scholarworks.umt.edu/etd/10427

Chicago Manual of Style (16th Edition):

Zhong, Ninghui. “Submodularity min-max results and total dual integrality of combinatorial optimization problems.” 1994. Doctoral Dissertation, Montana Tech. Accessed June 26, 2019. https://scholarworks.umt.edu/etd/10427.

MLA Handbook (7th Edition):

Zhong, Ninghui. “Submodularity min-max results and total dual integrality of combinatorial optimization problems.” 1994. Web. 26 Jun 2019.

Vancouver:

Zhong N. Submodularity min-max results and total dual integrality of combinatorial optimization problems. [Internet] [Doctoral dissertation]. Montana Tech; 1994. [cited 2019 Jun 26]. Available from: https://scholarworks.umt.edu/etd/10427.

Council of Science Editors:

Zhong N. Submodularity min-max results and total dual integrality of combinatorial optimization problems. [Doctoral Dissertation]. Montana Tech; 1994. Available from: https://scholarworks.umt.edu/etd/10427


University of Washington

8. Iyer, Rishabh Krishnan. Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications.

Degree: PhD, 2015, University of Washington

 In this dissertation, we explore a class of unifying and scalable algorithms for a number of submodular optimization problems, and connect them to several machine… (more)

Subjects/Keywords: Data Summarization; Discrete Optimization; Machine Learning; Submodular Optimization; Artificial intelligence; Computer science; Electrical engineering; electrical engineering

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APA (6th Edition):

Iyer, R. K. (2015). Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/33810

Chicago Manual of Style (16th Edition):

Iyer, Rishabh Krishnan. “Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications.” 2015. Doctoral Dissertation, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/33810.

MLA Handbook (7th Edition):

Iyer, Rishabh Krishnan. “Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications.” 2015. Web. 26 Jun 2019.

Vancouver:

Iyer RK. Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications. [Internet] [Doctoral dissertation]. University of Washington; 2015. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/33810.

Council of Science Editors:

Iyer RK. Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications. [Doctoral Dissertation]. University of Washington; 2015. Available from: http://hdl.handle.net/1773/33810


University of Texas – Austin

9. -3269-6167. Graph analytics and subset selection problems in machine learning.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 In this dissertation we examine two topics relevant to modern machine learning research: 1) Subgraph counting and 2) High-dimensional subset selection. The former can be… (more)

Subjects/Keywords: Machine learning; Approximation algorithms; Graph analytics; Graph algorithms; Subset selection; Submodular optimization; Weak submodularity; Restricted strong convexity; Streaming algorithms; Interpretability

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APA (6th Edition):

-3269-6167. (2018). Graph analytics and subset selection problems in machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68499

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

Chicago Manual of Style (16th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Thesis, University of Texas – Austin. Accessed June 26, 2019. http://hdl.handle.net/2152/68499.

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

MLA Handbook (7th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Web. 26 Jun 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/2152/68499.

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

Council of Science Editors:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68499

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

10. Ene, Alina. Approximation algorithms for submodular optimization and graph problems.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 In this thesis, we consider combinatorial optimization problems involving submodular functions and graphs. The problems we study are NP-hard and therefore, assuming that P =/=… (more)

Subjects/Keywords: Approximation algorithms; Submodular optimization; Routing; Network design

…organization . . . . . . . . . . . . . . . 1.2.1 Submodular optimization… …submodular. Submodularity is a central concept in combinatorial optimization. Starting with the… …functions has been shown to underpin the tractability1 of many optimization problems. Submodular… …submodular optimization problems that can be viewed as allocation or labeling problems. We… …approximation algorithms for several NP-hard optimization problems involving submodular functions and… 

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APA (6th Edition):

Ene, A. (2014). Approximation algorithms for submodular optimization and graph problems. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46738

Chicago Manual of Style (16th Edition):

Ene, Alina. “Approximation algorithms for submodular optimization and graph problems.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed June 26, 2019. http://hdl.handle.net/2142/46738.

MLA Handbook (7th Edition):

Ene, Alina. “Approximation algorithms for submodular optimization and graph problems.” 2014. Web. 26 Jun 2019.

Vancouver:

Ene A. Approximation algorithms for submodular optimization and graph problems. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/2142/46738.

Council of Science Editors:

Ene A. Approximation algorithms for submodular optimization and graph problems. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46738


University of Washington

11. Liu, Zhipeng. Submodular Optimization for Power System Control and Stability.

Degree: PhD, 2019, University of Washington

 Due to the increasing demand for electricity and unpredictable supplies from renewable energy, power systems are being operated close to their stability limits. Maintaining power… (more)

Subjects/Keywords: Cascading Failure; Power Systems; Small Signal Stability; Submodular Optimization; Voltage Control; Weak Submodularity; Electrical engineering; Applied mathematics; Engineering; Electrical engineering

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APA (6th Edition):

Liu, Z. (2019). Submodular Optimization for Power System Control and Stability. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43264

Chicago Manual of Style (16th Edition):

Liu, Zhipeng. “Submodular Optimization for Power System Control and Stability.” 2019. Doctoral Dissertation, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/43264.

MLA Handbook (7th Edition):

Liu, Zhipeng. “Submodular Optimization for Power System Control and Stability.” 2019. Web. 26 Jun 2019.

Vancouver:

Liu Z. Submodular Optimization for Power System Control and Stability. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/43264.

Council of Science Editors:

Liu Z. Submodular Optimization for Power System Control and Stability. [Doctoral Dissertation]. University of Washington; 2019. Available from: http://hdl.handle.net/1773/43264

12. Price, Christopher. Combinatorial Algorithms for Submodular Function Minimization and Related Problems.

Degree: 2015, University of Waterloo

Submodular functions are common in combinatorics; examples include the cut capacity function of a graph and the rank function of a matroid. The submodular function… (more)

Subjects/Keywords: algorithms; combinatorial optimization; submodular function minimization

…Schrijver’s Submodular Function Minimization Algorithm . . . . Local Augmentation of λ using ef… …Relabel Submodular Function Minimization Algorithm . . viii… …combinatorial algorithms for submodular function minimization, solving a problem that remained open… …the problem of submodular function minimization, with an emphasis on its relationship to the… …originally claimed. Chapter 4 studies submodular function minimization, which contains both matroid… 

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APA (6th Edition):

Price, C. (2015). Combinatorial Algorithms for Submodular Function Minimization and Related Problems. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9356

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):

Price, Christopher. “Combinatorial Algorithms for Submodular Function Minimization and Related Problems.” 2015. Thesis, University of Waterloo. Accessed June 26, 2019. http://hdl.handle.net/10012/9356.

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

MLA Handbook (7th Edition):

Price, Christopher. “Combinatorial Algorithms for Submodular Function Minimization and Related Problems.” 2015. Web. 26 Jun 2019.

Vancouver:

Price C. Combinatorial Algorithms for Submodular Function Minimization and Related Problems. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/10012/9356.

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

Council of Science Editors:

Price C. Combinatorial Algorithms for Submodular Function Minimization and Related Problems. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9356

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

13. Ng, Hong Wei. Machine learning for selecting parallel I/O benchmark applications.

Degree: MS, Computer Science, 2018, University of Illinois – Urbana-Champaign

 I/O is one of the main performance bottlenecks for many data-intensive scientific applications. Accurate I/O performance benchmarking, which can help us better understand the causes… (more)

Subjects/Keywords: Machine Learning; Submodular Function Optimization; Parallel I/O

…APPLICATIONS Submodular functions optimization have been used successfully to achieve state-of-the… …submodular function optimization. We therefore begin by giving a short review on submodular… …modeling power of the submodular function optimization framework. We will use them to construct… …these methods in that we formulate the selection of applications in a data-driven optimization… …submodular function maximization have been successfully applied to the problem of document… 

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APA (6th Edition):

Ng, H. W. (2018). Machine learning for selecting parallel I/O benchmark applications. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101588

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):

Ng, Hong Wei. “Machine learning for selecting parallel I/O benchmark applications.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed June 26, 2019. http://hdl.handle.net/2142/101588.

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

MLA Handbook (7th Edition):

Ng, Hong Wei. “Machine learning for selecting parallel I/O benchmark applications.” 2018. Web. 26 Jun 2019.

Vancouver:

Ng HW. Machine learning for selecting parallel I/O benchmark applications. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/2142/101588.

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

Council of Science Editors:

Ng HW. Machine learning for selecting parallel I/O benchmark applications. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101588

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

14. Powers, Thomas. Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays.

Degree: 2017, University of Washington

 We develop a framework to select a subset of sensors from a field in which the sensors have an ingrained independence structure. Given an arbitrary… (more)

Subjects/Keywords: optimization; robust; sensor; submodular; Electrical engineering; electrical engineering

…be present in the solution together. Submodular function optimization (SFO)… …new strategy for robust submodular optimization over constraint sets (such as matroids… …constraints in submodular function optimization. SFO can handle constraints that make problems… …space. For ping sequence optimization, submodular functions can be used to find optimal… …from the SFO toolbox to solve the above optimization problem [12]. This submodular… 

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APA (6th Edition):

Powers, T. (2017). Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/38119

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):

Powers, Thomas. “Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays.” 2017. Thesis, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/38119.

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

MLA Handbook (7th Edition):

Powers, Thomas. “Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays.” 2017. Web. 26 Jun 2019.

Vancouver:

Powers T. Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays. [Internet] [Thesis]. University of Washington; 2017. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/38119.

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

Council of Science Editors:

Powers T. Constrained Robust Submodular Sensor Selection with Application to Multistatic Sonar Arrays. [Thesis]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/38119

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


University of California – Berkeley

15. Tezel, Birce. Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems.

Degree: Industrial Engineering & Operations Research, 2017, University of California – Berkeley

 Capacitated fixed-charge network flow problems (CFCNF) are used to model a variety of problems in telecommunication, facility location, production planning and supply chain management. We… (more)

Subjects/Keywords: Operations research; Applied mathematics; Industrial engineering; fixed-charge network flow; lot-sizing problem; mixed-integer optimization; polyhedral analysis; submodular functions; valid inequalities

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

APA (6th Edition):

Tezel, B. (2017). Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/5t17357f

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):

Tezel, Birce. “Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems.” 2017. Thesis, University of California – Berkeley. Accessed June 26, 2019. http://www.escholarship.org/uc/item/5t17357f.

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

MLA Handbook (7th Edition):

Tezel, Birce. “Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems.” 2017. Web. 26 Jun 2019.

Vancouver:

Tezel B. Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems. [Internet] [Thesis]. University of California – Berkeley; 2017. [cited 2019 Jun 26]. Available from: http://www.escholarship.org/uc/item/5t17357f.

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

Council of Science Editors:

Tezel B. Submodular Inequalities for the Path Structures of the Capacitated Fixed-Charge Network Flow Problems. [Thesis]. University of California – Berkeley; 2017. Available from: http://www.escholarship.org/uc/item/5t17357f

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

16. Koepke, Hoyt. An Algorithmic Framework for High Dimensional Regression with Dependent Variables.

Degree: PhD, 2014, University of Washington

 We present an exploration of the rich theoretical connections between several classes of regularized models, network flows, and recent results in submodular function theory. This… (more)

Subjects/Keywords: Algorithms; Optimization; Regression; Regularization; Submodular Optimization; Statistics; Mathematics; Computer science; statistics

…dependent variables and recent results in combinatorial optimization, particularly submodular… …connecting network flows, submodular function minimization, and the optimization of ( B )… …polytope, one of the fundamental structures in submodular function optimization. 1.5.3 Network… …combinatorial optimization problems called submodular function minimization. Like the problem of… …4.2 Structure of the Solution Path . . . . . 4.3 General Optimization… 

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

APA (6th Edition):

Koepke, H. (2014). An Algorithmic Framework for High Dimensional Regression with Dependent Variables. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/24982

Chicago Manual of Style (16th Edition):

Koepke, Hoyt. “An Algorithmic Framework for High Dimensional Regression with Dependent Variables.” 2014. Doctoral Dissertation, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/24982.

MLA Handbook (7th Edition):

Koepke, Hoyt. “An Algorithmic Framework for High Dimensional Regression with Dependent Variables.” 2014. Web. 26 Jun 2019.

Vancouver:

Koepke H. An Algorithmic Framework for High Dimensional Regression with Dependent Variables. [Internet] [Doctoral dissertation]. University of Washington; 2014. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/24982.

Council of Science Editors:

Koepke H. An Algorithmic Framework for High Dimensional Regression with Dependent Variables. [Doctoral Dissertation]. University of Washington; 2014. Available from: http://hdl.handle.net/1773/24982

17. Lin, Hui. Submodularity in Natural Language Processing: Algorithms and Applications.

Degree: PhD, 2012, University of Washington

 Most natural language processing tasks can be seen as finding an optimal object from a finite set of objects. Often, the object of interest is… (more)

Subjects/Keywords: Document summarization; Natural language processing; Submodular function optimization; Computer science; Electrical engineering; Electrical engineering

…2.3 Continuous Extensions of Submodular Functions 2.4 Submodular Function Optimization… …interest is then a combinatorial optimization problem involving submodular functions. If the… …corresponding submodular optimization problem has not been studied before, we then analysis its… …hardness and design algorithms for it (e.g., the problem of optimization over the submodular… …prior theoretical research on submodular optimization, they cannot be applied to the NLP… 

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

APA (6th Edition):

Lin, H. (2012). Submodularity in Natural Language Processing: Algorithms and Applications. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/20591

Chicago Manual of Style (16th Edition):

Lin, Hui. “Submodularity in Natural Language Processing: Algorithms and Applications.” 2012. Doctoral Dissertation, University of Washington. Accessed June 26, 2019. http://hdl.handle.net/1773/20591.

MLA Handbook (7th Edition):

Lin, Hui. “Submodularity in Natural Language Processing: Algorithms and Applications.” 2012. Web. 26 Jun 2019.

Vancouver:

Lin H. Submodularity in Natural Language Processing: Algorithms and Applications. [Internet] [Doctoral dissertation]. University of Washington; 2012. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1773/20591.

Council of Science Editors:

Lin H. Submodularity in Natural Language Processing: Algorithms and Applications. [Doctoral Dissertation]. University of Washington; 2012. Available from: http://hdl.handle.net/1773/20591

18. Tabatabaei-Yazdi, Seyed. Design and Analysis of Low Complexity Network Coding Schemes.

Degree: 2012, Texas A&M University

 In classical network information theory, information packets are treated as commodities, and the nodes of the network are only allowed to duplicate and forward the… (more)

Subjects/Keywords: Information theory; network coding; undirected ring networks; line networks; star networks; node-constrained networks; index coding; relay networks; deterministic networks; submodular optimization

submodular optimization to analyze our 11 scheme and to obtain polynomial-time algorithms for… …1. Submodular flow . . . . . . . . . . . . . . . . 2. Proof of theorem 31… 

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

APA (6th Edition):

Tabatabaei-Yazdi, S. (2012). Design and Analysis of Low Complexity Network Coding Schemes. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/128797

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):

Tabatabaei-Yazdi, Seyed. “Design and Analysis of Low Complexity Network Coding Schemes.” 2012. Thesis, Texas A&M University. Accessed June 26, 2019. http://hdl.handle.net/1969.1/128797.

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

MLA Handbook (7th Edition):

Tabatabaei-Yazdi, Seyed. “Design and Analysis of Low Complexity Network Coding Schemes.” 2012. Web. 26 Jun 2019.

Vancouver:

Tabatabaei-Yazdi S. Design and Analysis of Low Complexity Network Coding Schemes. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1969.1/128797.

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

Council of Science Editors:

Tabatabaei-Yazdi S. Design and Analysis of Low Complexity Network Coding Schemes. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/128797

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

19. Chen, Cheng. Trustworthiness, diversity and inference in recommendation systems.

Degree: Department of Computer Science, 2016, University of Victoria

 Recommendation systems are information filtering systems that help users effectively and efficiently explore large amount of information and identify items of interest. Accurate predictions of… (more)

Subjects/Keywords: Bipartite Graphs; Matchings; NP-hardness; Linear Programming; Submodular Systems; Recommendation Systems; Anomaly Detection; Community Question and Answer Websites; Paid Posters; Adaptive Detection Systems; Weighted Bipartite b-Matching; Conflict Constraints; Optimization; Approximation; Reverse Engineering of Recommendations; Wi-Fi Data Mining; Profile Inference; Copula Modelling

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

APA (6th Edition):

Chen, C. (2016). Trustworthiness, diversity and inference in recommendation systems. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/7576

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):

Chen, Cheng. “Trustworthiness, diversity and inference in recommendation systems.” 2016. Thesis, University of Victoria. Accessed June 26, 2019. http://hdl.handle.net/1828/7576.

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

MLA Handbook (7th Edition):

Chen, Cheng. “Trustworthiness, diversity and inference in recommendation systems.” 2016. Web. 26 Jun 2019.

Vancouver:

Chen C. Trustworthiness, diversity and inference in recommendation systems. [Internet] [Thesis]. University of Victoria; 2016. [cited 2019 Jun 26]. Available from: http://hdl.handle.net/1828/7576.

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

Council of Science Editors:

Chen C. Trustworthiness, diversity and inference in recommendation systems. [Thesis]. University of Victoria; 2016. Available from: http://hdl.handle.net/1828/7576

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

.