Cisco’s embedded logic analyzer module (ELAM) is a debugging device used for many of Cisco’s application specific integrated chips (ASICs). The ELAM is used to capture data of interest to the user and stored for analysis purposes. The user enters a trigger expression containing data fields of interest in the form of a logical equation. The data fields associated with the trigger expression are stored in a set of Match and Mask (MM) registers. Incoming data packets are matched against these registers, and if the user-specified data pattern is detected, the ELAM triggers and begins a countdown sequence to stop data capture. The current ELAM implementation is restricted in the form of trigger expressions that are allowed and in the allocation of resources. Currently, data fields in the trigger expression can only be logically ANDed together, Match and Mask registers are inefficiently utilized, and a static state machine exists in the ELAM trigger logic. To optimize the usage of the ELAM, a trigger expression is first treated as a Boolean expression so that minimization algorithms can be run. Next, the data stored in the Match and Mask registers is analyzed for redundancies. Finally, a dynamic state machine is programmed with a distinct set of states generated from the trigger expression. This set of states is further minimized. A feasibility study is done to analyze the validity of the results.

This thesis investigates the portfolio optimization problem using Value-at-Risk (VaR) as a risk measure, when m sample scenarios are given. Minimizing VaR of a portfolio is computationally difficult: it is non-convex, non-smooth, and has many local minima. Recently Gaivoronski and Pflug define a quantile-based smoothed VaR function to approximate the original VaR; this smoothed VaR function is then minimized to obtain the minimal VaR portfolio. Unfortunately this method suffers two problems. Firstly, computational cost of minimization is high since each function evaluation requires O(m^3) work, where m is the number of scenarios. Secondly, it is difficult to determine the smooth parameter. We propose a new gradual non-convexation penalty method which can efficiently solve a VaR minimization problem. We first introduce an auxiliary variable and formulate the VaR minimization problem as an optimization problem with a probabilistic constraint, which involves a sum of step functions. A continuously differentiable piecewise quadratic function is used to approximate the step function. An exact penalty method is used to solve the constrained optimization problem. In an attempt to reach the global minimize, we also use a gradual non-convexation process with the initial problem close to a convex problem. The solution of the kth optimization problem is used as the starting point of the k+1th problem. As the indexing parameter increases, the problem becomes non-convex. Our new method has three advantages. Firstly, our formulation is structurally simpler. Secondly, our method has three computationally more efficient since each function evaluation requires O(m) work. Thirdly, we use a gradual non-convexation process in an attempt to track the global minimum; this also avoids the difficulty in choosing the smooth parameter. Both historical and synthetic data are used to test our VaR minimization method. We compare our method with both Uryasev and Rockafellar’s CVaR minimization method and Gaivoronski and Pflug’s quantile-based smoothed VaR method in terms of VaR, CPU time, and efficient frontiers. We show that our gradual non-convexation penalty method yields better minimal VaR portfolio than the other two methods. In addition, we show that the proposed gradual non-convexation penalty method is computationally much more efficient than Gaivoronski and Pflug’s quantile-based smoothed VaR method, especially when the number of scenarios is large.

No description available.

No description available.

Program of Analysis of Machine Testing is the process of: • Creating additional test cases to increase coverage, and • Determining a quantitative measure of code coverage, which is an indirect measure of quality. • Identifying redundant test cases that do not increase coverage. The program automates this process. You use coverage analysis to assure quality of your set of tests, not the quality of the actual product. You do not generally use a coverage analyzer when running your set of tests through your release candidate. Coverage analysis requires access to test program source code and often requires recompiling it.

This thesis investigates the portfolio optimization problem using Value-at-Risk (VaR) as a risk measure, when m sample scenarios are given. Minimizing VaR of a portfolio is computationally difficult: it is non-convex, non-smooth, and has many local minima. Recently Gaivoronski and Pflug define a quantile-based smoothed VaR function to approximate the original VaR; this smoothed VaR function is then minimized to obtain the minimal VaR portfolio. Unfortunately this method suffers two problems. Firstly, computational cost of minimization is high since each function evaluation requires O(m^3) work, where m is the number of scenarios. Secondly, it is difficult to determine the smooth parameter. We propose a new gradual non-convexation penalty method which can efficiently solve a VaR minimization problem. We first introduce an auxiliary variable and formulate the VaR minimization problem as an optimization problem with a probabilistic constraint, which involves a sum of step functions. A continuously differentiable piecewise quadratic function is used to approximate the step function. An exact penalty method is used to solve the constrained optimization problem. In an attempt to reach the global minimize, we also use a gradual non-convexation process with the initial problem close to a convex problem. The solution of the kth optimization problem is used as the starting point of the k+1th problem. As the indexing parameter increases, the problem becomes non-convex. Our new method has three advantages. Firstly, our formulation is structurally simpler. Secondly, our method has three computationally more efficient since each function evaluation requires O(m) work. Thirdly, we use a gradual non-convexation process in an attempt to track the global minimum; this also avoids the difficulty in choosing the smooth parameter. Both historical and synthetic data are used to test our VaR minimization method. We compare our method with both Uryasev and Rockafellar’s CVaR minimization method and Gaivoronski and Pflug’s quantile-based smoothed VaR method in terms of VaR, CPU time, and efficient frontiers. We show that our gradual non-convexation penalty method yields better minimal VaR portfolio than the other two methods. In addition, we show that the proposed gradual non-convexation penalty method is computationally much more efficient than Gaivoronski and Pflug’s quantile-based smoothed VaR method, especially when the number of scenarios is large.

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Seitzman, Jerry; Committee Member: Gaeta, Richard; Committee Member: Jagoda, Jeff; Committee Member: Neumeier, Yedidia; Committee Member: Yoda, Minami; Committee Member: Zinn, Ben.

Dissertation (Ph.D.)--Auburn University, / Abstract. Vita. Includes bibliographic references.

Thesis (M.P.A.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 103-104).

Thesis (Ph.D.(Chemical Engineering))--University of Pretoria, 2008. / Summary in English. Includes bibliographical references.