Generating test data is a critical component of software testing, essential for ensuring the proper functioning of software systems. Automated test data generation, in particular, can greatly improve the efficiency an...
详细信息
Generating test data is a critical component of software testing, essential for ensuring the proper functioning of software systems. Automated test data generation, in particular, can greatly improve the efficiency and accuracy of the testing process. In 1990, the paper "Automated Software Test Data Generation," published in IEEE Transactions on Software Engineering (Korel, 1990), introduced a novel approach to automated test data generation, focusing on actual program execution, fitness function minimization methods, and dynamic data flow analysis. This paper discusses the impact of the 1990 IEEE TSE publication (Korel, 1990) on automated test data generation and software engineering.
The Genvar criterion, proposed by Steel, is one of the important generalizations of canonical correlation analysis. This paper deals with iterative methods for the Genvar criterion. An alternating variable method is a...
详细信息
The Genvar criterion, proposed by Steel, is one of the important generalizations of canonical correlation analysis. This paper deals with iterative methods for the Genvar criterion. An alternating variable method is analysed and an inexact version of it is proposed. Two starting point strategies are suggested to enhance these iterative algorithms. Numerical results show that, these starting point strategies not only can improve the rate of convergence, but also boost up the probability of finding a global solution.
Several iterative methods for maximal correlation problems (MCPs) have been proposed in the literature. This paper deals with the convergence of these iterations and contains three contributions. Firstly, a unified an...
详细信息
Several iterative methods for maximal correlation problems (MCPs) have been proposed in the literature. This paper deals with the convergence of these iterations and contains three contributions. Firstly, a unified and concise proof of the monotone convergence of these iterative methods is presented. Secondly, a starting point strategy is analysed. Thirdly, some error estimates are presented to test the quality of a computed solution. Both theoretical results and numerical tests suggest that combining with this starting point strategy these methods converge rapidly and are more likely converging to a global maximizer of MCP. Copyright (C) 2016 John Wiley & Sons, Ltd.
Dr. Hayashi developed one of methods for multidimensional scaling problem, named MDA. This method is a very useful one, but its computation is very difficult. In the present paper, the author shows a modification of i...
详细信息
Dr. Hayashi developed one of methods for multidimensional scaling problem, named MDA. This method is a very useful one, but its computation is very difficult. In the present paper, the author shows a modification of its computation with some numerical examples in one dimensional case.
This paper deals with numerical methods for the Maxnear criterion of multiple-sets canonical analysis. Optimality conditions are derived. Upper and lower bounds of the optimal objective function value are presented. T...
详细信息
This paper deals with numerical methods for the Maxnear criterion of multiple-sets canonical analysis. Optimality conditions are derived. Upper and lower bounds of the optimal objective function value are presented. Two iterative methods are proposed. One is an alternating variable method, and the other called Gauss-Seidel method is an inexact version of the alternating variable method. Convergence of these methods are analyzed. A starting point strategy is suggested for both methods. Numerical results are presented to demonstrate the efficiency of these methods and the starting point strategy.
暂无评论