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You searched for subject:(vector knapsack). Showing records 1 – 2 of 2 total matches.

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Wayne State University

1. Rampersaud, Safraz. Sharing-Aware Resource Management Algorithms For Virtual Computing Environments.

Degree: PhD, Computer Science, 2016, Wayne State University

Virtualization technologies in cloud computing are ubiquitous throughout data centers around the world where providers consider operational costs and fast delivery guarantees for a variety of profitable services. These providers should consistently invoke measures for increasing the efficiencies of their virtualized services in a competitive environment where fast entry to market, technology advancement, and service price differentials separate sustaining providers from antiquated ones. Therefore, providers seeking further efficiencies and profit opportunities should consider how their resources are managed in virtual computing environments which leverage memory reclamation techniques, specifically page-sharing; motivating the design of new memory sharing-aware resource management algorithms. In this dissertation, we design families of offline and online sharing-aware algorithms for resource management in virtual computing environments and investigate their properties and relationships to various sharing models. Our contribution consists of the design of new online and approximation algorithms offering relevant performance guarantees and their applications to next-generation virtualization technologies. Advisors/Committee Members: Daniel Grosu.

Subjects/Keywords: approximation algorithms; multilinear programming; sharing-aware; vector bin-packing; vector knapsack; virtualization; Computer Sciences

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

APA (6th Edition):

Rampersaud, S. (2016). Sharing-Aware Resource Management Algorithms For Virtual Computing Environments. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1477

Chicago Manual of Style (16th Edition):

Rampersaud, Safraz. “Sharing-Aware Resource Management Algorithms For Virtual Computing Environments.” 2016. Doctoral Dissertation, Wayne State University. Accessed December 13, 2019. https://digitalcommons.wayne.edu/oa_dissertations/1477.

MLA Handbook (7th Edition):

Rampersaud, Safraz. “Sharing-Aware Resource Management Algorithms For Virtual Computing Environments.” 2016. Web. 13 Dec 2019.

Vancouver:

Rampersaud S. Sharing-Aware Resource Management Algorithms For Virtual Computing Environments. [Internet] [Doctoral dissertation]. Wayne State University; 2016. [cited 2019 Dec 13]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1477.

Council of Science Editors:

Rampersaud S. Sharing-Aware Resource Management Algorithms For Virtual Computing Environments. [Doctoral Dissertation]. Wayne State University; 2016. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1477

2. 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 this trend, this research combines three topics: mathematical programming, data mining, and supply chain management. A new pegging algorithm is developed for solving the continuous nonlinear knapsack problem. An efficient solving approach is proposed for solving the ν-support vector machine for classification problem in the field of data mining. The new pegging algorithm is used to solve the subproblem of the support vector machine problem. For the supply chain management, this research proposes an efficient integrated solving approach for the supplier selection problem. The support vector machine is applied to solve the problem of selecting potential supplies in the procedure of the integrated solving approach. In the first part of this research, a new pegging algorithm solves the continuous nonlinear knapsack problem with box constraints. The problem is to minimize a convex and differentiable nonlinear function with one equality constraint and box constraints. Pegging algorithm needs to calculate primal variables to check bounds on variables at each iteration, which frequently is a time-consuming task. The newly proposed dual bound algorithm checks the bounds of Lagrange multipliers without calculating primal variables explicitly at each iteration. In addition, the calculation of the dual solution at each iteration can be reduced by a proposed new method for updating the solution. In the second part, this research proposes several streamlined solution procedures of ν-support vector machine for the classification. The main solving procedure is the matrix splitting method. The proposed method in this research is a specified matrix splitting method combined with the gradient projection method, line search technique, and the incomplete Cholesky decomposition method. The method proposed can use a variety of methods for line search and parameter updating. Moreover, large scale problems are solved with the incomplete Cholesky decomposition and some efficient implementation techniques. To apply the research findings in real-world problems, this research developed an efficient integrated approach for supplier selection problems using the support vector machine and the mixed integer programming. Supplier selection is an essential step in the procurement processes. For companies considering maximizing their profits and reducing costs, supplier selection requires seeking satisfactory suppliers and allocating proper orders to the selected suppliers. In the early stage of supplier selection, a company can use the support vector machine classification to choose potential qualified suppliers using specific criteria. However, the company may not need to purchase from all qualified suppliers. Once the company determines the amount of raw materials and components to purchase, the company then selects final suppliers from which to order… Advisors/Committee Members: Chih-Hang Wu.

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

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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 December 13, 2019. 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. 13 Dec 2019.

Vancouver:

Kim G. Solving support vector machine classification problems and their applications to supplier selection. [Internet] [Doctoral dissertation]. Kansas State University; 2011. [cited 2019 Dec 13]. 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

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