ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Shen, Wei
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Recognizing that CEOs are less capable of diversifying their employment risks than shareholders who could diversify their investment risks through portfolio investments, agency theory assumes that CEOs tend to be risk averse compared with shareholders. Based on this assumption, agency theory scholars suggest that to align the risk preference of CEOs with that of shareholders, CEOs need to be closely monitored and have less power. SEC regulators have been adopting the suggestion and accordingly CEO power has been reduced in the past decades. However, the empirical results are mixed and cannot provide solid support for the suggestion that reducing CEO power could lead the CEO to take more risks.
Considering that managerial risk taking is an important issue in strategic management research and agency theory has been widely adopted in academia and business worlds, it is imperative to clarify the mechanism behind the relationship between CEO power and risk taking. My study aims to fill this research gap. In this study I follow agency theory to take an employment security perspective and fully consider how CEOs’ concern about employment security is affected by their power and ownership structure to enrich the understanding of the effects of CEO power and ownership structure on risk taking. I fine-tune the key concept CEO power into the CEO power over board and introduce a key aspect of ownership structure - nontransient investor ownership. I further suggest that CEO power over board and nontransient investor ownership affect CEOs’ employment security and the resulting CEO risk taking. In addition, I consider a set of industry and firm characteristics as the boundary conditions for the effects of CEO power and nontransient investor ownership on CEO risk-taking. This set of industry and firm characteristics include industry complexity, industry dynamism, industry munificence and firm slack.
I test my theory using a large-scale, multi-year sample of U.S. publicly listed S&P 1500 firms between 2001 and 2017. My main hypotheses about the effects of CEO power over board and nontransient investor ownership on CEO risk taking receive strong support.
Considering that managerial risk taking is an important issue in strategic management research and agency theory has been widely adopted in academia and business worlds, it is imperative to clarify the mechanism behind the relationship between CEO power and risk taking. My study aims to fill this research gap. In this study I follow agency theory to take an employment security perspective and fully consider how CEOs’ concern about employment security is affected by their power and ownership structure to enrich the understanding of the effects of CEO power and ownership structure on risk taking. I fine-tune the key concept CEO power into the CEO power over board and introduce a key aspect of ownership structure - nontransient investor ownership. I further suggest that CEO power over board and nontransient investor ownership affect CEOs’ employment security and the resulting CEO risk taking. In addition, I consider a set of industry and firm characteristics as the boundary conditions for the effects of CEO power and nontransient investor ownership on CEO risk-taking. This set of industry and firm characteristics include industry complexity, industry dynamism, industry munificence and firm slack.
I test my theory using a large-scale, multi-year sample of U.S. publicly listed S&P 1500 firms between 2001 and 2017. My main hypotheses about the effects of CEO power over board and nontransient investor ownership on CEO risk taking receive strong support.
ContributorsZhu, Qi (Author) / Shen, Wei (Thesis advisor) / Zhu, David (Thesis advisor) / Certo, Trevis (Committee member) / Arizona State University (Publisher)
Created2019