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You searched for subject:(Convex Vector Optimization). Showing records 1 – 6 of 6 total matches.

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1. Collonge, Julien. Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms.

Degree: Docteur es, Mathématiques appliquées, 2014, Nouvelle Calédonie

Cette thèse relate certains aspects liés à l'analyse post-Pareto issue de Problèmes d'Optimisation Vectorielle Stochastique. Un problème d'optimisation Vectorielle Stochastique consiste à optimiser l'espérance d'une… (more)

Subjects/Keywords: Optimisation vectorielle; Optimisation stochastique; Analyse post Pareto; Optimization over a Pareto set; Deterministic Vector Optimization; Convex Vector Optimization; 519

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APA (6th Edition):

Collonge, J. (2014). Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms. (Doctoral Dissertation). Nouvelle Calédonie. Retrieved from http://www.theses.fr/2014NCAL0061

Chicago Manual of Style (16th Edition):

Collonge, Julien. “Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms.” 2014. Doctoral Dissertation, Nouvelle Calédonie. Accessed November 17, 2017. http://www.theses.fr/2014NCAL0061.

MLA Handbook (7th Edition):

Collonge, Julien. “Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms.” 2014. Web. 17 Nov 2017.

Vancouver:

Collonge J. Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms. [Internet] [Doctoral dissertation]. Nouvelle Calédonie; 2014. [cited 2017 Nov 17]. Available from: http://www.theses.fr/2014NCAL0061.

Council of Science Editors:

Collonge J. Analyse post-Pareto en optimisation vectorielle stochastique et déterministe : étude théorique et algorithmes. : Post-Pareto Analysis in Stochastic Multi-Objective Optimization : Theoretical Results and Algorithms. [Doctoral Dissertation]. Nouvelle Calédonie; 2014. Available from: http://www.theses.fr/2014NCAL0061

2. Ulus, Firdevs. Algorithms for Vector Optimization Problems .

Degree: PhD, 2015, Princeton University

 This dissertation studies algorithms to solve linear and convex vector optimization problems. A parametric simplex algorithm for solving linear vector optimization problems (LVOPs) and two… (more)

Subjects/Keywords: Algorithms; Convex Programming; Duality; Linear Programming; Multiobjective optimization; Vector optimization

…1 1.2 Convex Vector Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 5… …53 4 Convex Vector Optimization Problems 58 4.1 Problem Setting and Solution Concepts… …vector optimization problems (LVOPs) and convex vector optimization problems (… …solving an LP or performing a Phase 1 algorithm. 1.2 Convex Vector Optimization Convex vector… …convex analysis are presented, the linear vector optimization problem and the solution concepts… 

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APA (6th Edition):

Ulus, F. (2015). Algorithms for Vector Optimization Problems . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01n009w461x

Chicago Manual of Style (16th Edition):

Ulus, Firdevs. “Algorithms for Vector Optimization Problems .” 2015. Doctoral Dissertation, Princeton University. Accessed November 17, 2017. http://arks.princeton.edu/ark:/88435/dsp01n009w461x.

MLA Handbook (7th Edition):

Ulus, Firdevs. “Algorithms for Vector Optimization Problems .” 2015. Web. 17 Nov 2017.

Vancouver:

Ulus F. Algorithms for Vector Optimization Problems . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2017 Nov 17]. Available from: http://arks.princeton.edu/ark:/88435/dsp01n009w461x.

Council of Science Editors:

Ulus F. Algorithms for Vector Optimization Problems . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp01n009w461x

3. Cox, Bruce. Applications of accuracy certificates for problems with convex structure.

Degree: PhD, Industrial and Systems Engineering, 2011, Georgia Tech

 Applications of accuracy certificates for problems with convex structure   This dissertation addresses the efficient generation and potential applications of accuracy certificates in the framework… (more)

Subjects/Keywords: Accuracy certificates; Convex optimization; Vector algebra; Linear programming; Convex functions; Convex domains

…of accuracy certificates in the framework of “black-box-represented” convex optimization… …certificates for black-box-represented convex optimization. In the second part, we extend the toolbox… …of black-box-oriented convex optimization algorithms with accuracy certificates by… …convex-concave saddle point problem gives rise to a primal-dual pair of convex optimization… …identifications; e.g., the vector field and the accuracy measure associated with a convex-concave saddle… 

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APA (6th Edition):

Cox, B. (2011). Applications of accuracy certificates for problems with convex structure. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39489

Chicago Manual of Style (16th Edition):

Cox, Bruce. “Applications of accuracy certificates for problems with convex structure.” 2011. Doctoral Dissertation, Georgia Tech. Accessed November 17, 2017. http://hdl.handle.net/1853/39489.

MLA Handbook (7th Edition):

Cox, Bruce. “Applications of accuracy certificates for problems with convex structure.” 2011. Web. 17 Nov 2017.

Vancouver:

Cox B. Applications of accuracy certificates for problems with convex structure. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2017 Nov 17]. Available from: http://hdl.handle.net/1853/39489.

Council of Science Editors:

Cox B. Applications of accuracy certificates for problems with convex structure. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/39489

4. Kim, Gitae. Solving support vector machine classification problems and their applications to supplier selection.

Degree: PhD, Department of Industrial & Manufacturing Systems Engineering, 2011, Kansas State University

 Recently, interdisciplinary (management, engineering, science, and economics) collaboration research has been growing to achieve the synergy and to reinforce the weakness of each discipline. Along… (more)

Subjects/Keywords: Support Vector Machine; Nonlinear Knapsack Problem; Supplier Selection; Convex Optimization; Supply Chain Management; Classification; Engineering (0537)

…38 3.2 Support Vector Optimization… …SOLVING SUPPORT VECTOR MACHINE CLASSIFICATION PROBLEMS AND THEIR APPLICATIONS TO SUPPLIER… …knapsack problem. An efficient solving approach is proposed for solving the -support vector… …used to solve the subproblem of the support vector machine problem. For the supply chain… …selection problem. The support vector machine is applied to solve the problem of selecting… 

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

APA (6th Edition):

Kim, G. (2011). Solving support vector machine classification problems and their applications to supplier selection. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/8719

Chicago Manual of Style (16th Edition):

Kim, Gitae. “Solving support vector machine classification problems and their applications to supplier selection.” 2011. Doctoral Dissertation, Kansas State University. Accessed November 17, 2017. http://hdl.handle.net/2097/8719.

MLA Handbook (7th Edition):

Kim, Gitae. “Solving support vector machine classification problems and their applications to supplier selection.” 2011. Web. 17 Nov 2017.

Vancouver:

Kim G. Solving support vector machine classification problems and their applications to supplier selection. [Internet] [Doctoral dissertation]. Kansas State University; 2011. [cited 2017 Nov 17]. Available from: http://hdl.handle.net/2097/8719.

Council of Science Editors:

Kim G. Solving support vector machine classification problems and their applications to supplier selection. [Doctoral Dissertation]. Kansas State University; 2011. Available from: http://hdl.handle.net/2097/8719


Colorado State University

5. Rohrbacker, Nicholas. Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques.

Degree: PhD, Mathematics, 2007, Colorado State University

 In this dissertation we propose Split Bregman algorithms for several multivariate analytic techniques for dimensionality reduction and feature selection including Sparse Principal Components Analysis, Bisparse… (more)

Subjects/Keywords: convex optimization; modularity; sparse PCA; Bisparse Singular Value Decomposition; Split Bregman; support vector machine

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

APA (6th Edition):

Rohrbacker, N. (2007). Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/71581

Chicago Manual of Style (16th Edition):

Rohrbacker, Nicholas. “Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques.” 2007. Doctoral Dissertation, Colorado State University. Accessed November 17, 2017. http://hdl.handle.net/10217/71581.

MLA Handbook (7th Edition):

Rohrbacker, Nicholas. “Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques.” 2007. Web. 17 Nov 2017.

Vancouver:

Rohrbacker N. Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques. [Internet] [Doctoral dissertation]. Colorado State University; 2007. [cited 2017 Nov 17]. Available from: http://hdl.handle.net/10217/71581.

Council of Science Editors:

Rohrbacker N. Sparse multivariate analyses via ℓ1-regularized optimization problems solved with Bregman Iterative Techniques. [Doctoral Dissertation]. Colorado State University; 2007. Available from: http://hdl.handle.net/10217/71581

6. Sapankevych, Nicholas. Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction.

Degree: 2015, University of South Florida

 Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting, weather and environmental state prediction,… (more)

Subjects/Keywords: Competition for Artificial Time Series; Convex Optimization; EUNITE; Mackey-Glass; Support Vector Regression; Electrical and Computer Engineering

…1]. 2.1.2 Support Vector Machines and Particle Swarm Optimization As mentioned in the… …optimization problem that ensures the flatness criterion is met. A convex optimization problem is… …SVR, we formulate a convex optimization problem with two linear constraints as shown in… …Illustrating Support Vectors and Outliers 17 Figure 3.3 Support Vector Regression Linear… …Approximation Example 20 Figure 3.4 Support Vector Regression Non Linear Approximation Example 21… 

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

APA (6th Edition):

Sapankevych, N. (2015). Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction. (Thesis). University of South Florida. Retrieved from http://scholarcommons.usf.edu/etd/5569

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):

Sapankevych, Nicholas. “Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction.” 2015. Thesis, University of South Florida. Accessed November 17, 2017. http://scholarcommons.usf.edu/etd/5569.

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

MLA Handbook (7th Edition):

Sapankevych, Nicholas. “Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction.” 2015. Web. 17 Nov 2017.

Vancouver:

Sapankevych N. Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction. [Internet] [Thesis]. University of South Florida; 2015. [cited 2017 Nov 17]. Available from: http://scholarcommons.usf.edu/etd/5569.

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

Council of Science Editors:

Sapankevych N. Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction. [Thesis]. University of South Florida; 2015. Available from: http://scholarcommons.usf.edu/etd/5569

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

.